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|
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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "7e2c4bbb-5e8b-4d84-9997-ecb2c349cf54", |
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"metadata": {}, |
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"source": [ |
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"## First step - generate training data from examples" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 105, |
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"id": "16cf3aa2-f407-4b95-8b9e-c3c586f67835", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import requests\n", |
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"import pandas as pd\n", |
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"from datetime import datetime, timedelta,timezone\n", |
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"from datasets import load_dataset, Dataset\n", |
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"from dotenv import load_dotenv\n", |
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"import os\n", |
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"from openai import OpenAI\n", |
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"import json\n", |
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"import tiktoken\n", |
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"from IPython.display import display, Markdown\n", |
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"\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 96, |
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"id": "375302b6-b6a7-46ea-a74c-c2400dbd8bbe", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Load environment variables in a file called .env\n", |
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"load_dotenv()\n", |
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"\n", |
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"# Replace with your CoinAPI key\n", |
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"API_KEY = os.getenv('YOUR_COINAPI_KEY')\n", |
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"\n", |
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"# Define the base URL for CoinAPI\n", |
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"BASE_URL = 'https://rest.coinapi.io/v1/ohlcv/'\n", |
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"OLLAMA_URL = \"http://localhost:11434/v1\"\n", |
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"\n", |
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", |
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"# URL to fetch the OHLCV data\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 98, |
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"id": "d0cc964d", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"openai = OpenAI()\n", |
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"\n", |
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"ollama = OpenAI(\n", |
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" base_url=OLLAMA_URL,\n", |
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" api_key='OLAMMA'\n", |
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")\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 104, |
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"id": "8a0c9fff-9eff-42fd-971b-403c99d9b726", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Define the symbol and timeframe\n", |
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"base_data = {\n", |
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" 'name': 'Cardano',\n", |
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" 'symbol': f'BINANCE_SPOT_ADA_USDT',\n", |
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" 'timeframe': '1DAY',\n", |
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" 'time_range': 365 * 2\n", |
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"}\n", |
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"\n", |
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"\n", |
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"# Calculate the start date for one year ago\n", |
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"end_date = datetime.now(tz=timezone.utc)\n", |
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"\n", |
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"start_date = end_date - timedelta(days=base_data['time_range'])\n", |
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"\n", |
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"# Format the dates in the required format (ISO 8601)\n", |
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"start_date_str = start_date.strftime('%Y-%m-%dT%H:%M:%S')\n", |
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"end_date_str = end_date.strftime('%Y-%m-%dT%H:%M:%S')\n", |
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"\n", |
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"# Headers for authentication\n", |
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"headers = {\n", |
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" 'X-CoinAPI-Key': API_KEY\n", |
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"}\n", |
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"\n", |
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"# URL to fetch the OHLCV base_data\n", |
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"url = f'{BASE_URL}{base_data['symbol']}/history'\n", |
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"\n", |
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"# Request parameters\n", |
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"params = {\n", |
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" 'period_id': base_data['timeframe'],\n", |
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" 'time_start': start_date_str,\n", |
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" 'time_end': end_date_str,\n", |
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" 'limit': 1000 # Maximum number of records per request\n", |
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"}" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 91, |
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"id": "586b07ba-5396-4c34-a696-01c8bc3597a0", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"729" |
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] |
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}, |
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"execution_count": 91, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"# Fetch the data\n", |
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"response = requests.get(url, headers=headers, params=params) \n", |
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"len(response.json())" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 76, |
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"id": "953422d0-2e75-4d01-862e-6383df54d9e5", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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" Timestamp Open High Low Close\n", |
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"724 2025-02-06 0.7325 0.7660 0.6978 0.7052\n", |
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"725 2025-02-07 0.7052 0.7532 0.6902 0.7072\n", |
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"726 2025-02-08 0.7072 0.7180 0.6815 0.7005\n", |
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"727 2025-02-09 0.7006 0.7160 0.6503 0.6814\n", |
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"728 2025-02-10 0.6815 0.7177 0.6632 0.7037\n" |
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] |
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} |
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], |
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"source": [ |
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"# Check for successful response\n", |
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"if response.status_code == 200:\n", |
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" data = response.json()\n", |
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"\n", |
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" if data:\n", |
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" # Convert to DataFrame for better readability\n", |
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" df = pd.DataFrame(data)\n", |
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"\n", |
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" df = df[[\"time_period_start\", \"price_open\", \"price_high\", \"price_low\", \"price_close\"]]\n", |
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" df.columns = [\"Timestamp\", \"Open\", \"High\", \"Low\", \"Close\"]\n", |
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"\n", |
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" # Convert timestamp to readable format\n", |
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" df[\"Timestamp\"] = pd.to_datetime(df[\"Timestamp\"]).dt.strftime(\"%Y-%m-%d\")\n", |
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"\n", |
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" # Display the first few rows of the data\n", |
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" print(df.tail())\n", |
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" \n", |
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" # Convert last 365 days of data into JSON format\n", |
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" price_history = df.to_dict(orient=\"records\")\n", |
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" \n", |
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" else:\n", |
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" print('No data found for the given period.')\n", |
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"else:\n", |
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" print(f'Error fetching data: {response.status_code}, {response.text}')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 47, |
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"id": "ada5ed4f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def count_tokens(text, model=\"gpt-4o\"):\n", |
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" encoding = tiktoken.encoding_for_model(model)\n", |
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" return len(encoding.encode(text))\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ab47d974", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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" # Construct prompt \n", |
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"\n", |
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"prompt = f\"\"\"\n", |
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" Given the last 365 days of ${base_data['name']} OHLC data:\n", |
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"\n", |
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" {json.dumps(price_history, indent=2)}\n", |
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"\n", |
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" Analyze this data and provide a trading signal (Buy, Sell, or Hold) for today based on the trend and the price action.\n", |
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" Note that today is {end_date.strftime('%Y-%m-%d')}\n", |
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" Also, provide short term ,mid term and long term signals.\n", |
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" \"\"\"\n", |
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"num_tokens = count_tokens(prompt)\n", |
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"print(f\"Estimated Tokens: {num_tokens}\")\n", |
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"\n", |
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"print(prompt)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "b40fec12", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/markdown": [ |
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"To analyze this data, I'll use a combination of moving averages, relative strength index (RSI), and other technical indicators. Please note that this is a simplified analysis and should not be considered as professional trading advice.\n", |
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"\n", |
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"**Current Data**\n", |
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"\n", |
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"For 2025-02-10, the opening price is not available. However, we can calculate the current prices based on the historical data provided.\n", |
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"\n", |
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"Let's assume the last known close price for 2025-02-09 was $0.6815. For simplicity, let's use this as the opening price for today (2025-02-10).\n", |
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"\n", |
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"**Short-Term Signal**\n", |
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"\n", |
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"For a short-term signal, I'll use a simple moving average crossover system.\n", |
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"\n", |
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"* Short-Term Moving Average (20 days): $0.6922\n", |
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"* Short-Term Moving Average (10 days): $0.6747\n", |
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"\n", |
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"Since the 20-day MA ($0.6922) is above the 10-day MA ($0.6747), we can conclude that **Buy** in this timeframe.\n", |
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"\n", |
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"**Mid-Term Signal**\n", |
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"\n", |
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"For a mid-term signal, I'll use RSI.\n", |
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"\n", |
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"* Current Price: $0.6815\n", |
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"* Overbought Region: 70-80\n", |
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"* Oversold Region: 20-50\n", |
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"\n", |
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"The current price ($0.6815) is at the lower end of the oversold region (20-50), indicating a potential buying opportunity.\n", |
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"\n", |
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"Since RSI values are not provided for the entire dataset, we'll use an RSI value of 30 (midpoint of the low and high values). At $0.6815, RSI is approximately 34.\n", |
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"\n", |
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"* Mid-Term Moving Average: Not available\n", |
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"* Mid-Term Momentum: Rising\n", |
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"\n", |
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"Considering the oversold region and rising momentum, **Hold** is a reasonable mid-term strategy for today.\n", |
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"\n", |
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"**Long-Term Signal**\n", |
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"\n", |
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"For a long-term signal, I'll use the overall trend direction based on historical data.\n", |
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"\n", |
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"The dataset shows an upward trend (average True Range, AtR, value has been increasing). From 2025-02-03 to 2025-02-09, there were 6 consecutive increases in this dataset. That's a strong positive trend.\n", |
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"\n", |
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"Since there are no obvious signs of weakness in the long-term data or divergence with other trends (like 50-day MA), I recommend **Hold** for an extended holding period, keeping an eye on RSI values and adjusting positions as needed to stay ahead of potential price drops.\n", |
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"\n", |
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"**Summary**\n", |
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"\n", |
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"* Short-Term: **Buy**\n", |
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"* Mid-Term: **Hold**\n", |
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"* Long-Term: **Hold**" |
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], |
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"text/plain": [ |
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"<IPython.core.display.Markdown object>" |
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] |
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}, |
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"metadata": {}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
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"def get_response(prompt):\n", |
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" new_response = ollama.chat.completions.create(model=\"llama3.2\",\n", |
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" messages=[\n", |
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" {\"role\": \"system\", \"content\": f\"You are a trading analyst providing Buy/Sell/Hold signals based on ${base_data['name']} price history.Note that today is {end_date.strftime('%Y-%m-%d')}\"},\n", |
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" {\"role\": \"user\", \"content\": prompt}\n", |
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" ],\n", |
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" stream=True,\n", |
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" max_tokens=5500\n", |
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" )\n", |
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" markdown_content = \"\"\n", |
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" \n", |
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" # Stream response and accumulate markdown content\n", |
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" for chunk in new_response:\n", |
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" content = chunk.choices[0].delta.content or ''\n", |
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" markdown_content += content\n", |
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" \n", |
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" # Clear output and display updated markdown\n", |
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" display(Markdown(markdown_content), clear=True)\n", |
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" \n", |
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" yield content\n", |
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"\n", |
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"# Call the function and consume the generator to start streaming\n", |
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"for _ in get_response(prompt):\n", |
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" pass" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 88, |
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"id": "ba09436c", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/markdown": [ |
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"# $Cardano Trading Analysis for 2025-02-10\n", |
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"\n", |
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"### **Current Price Analysis**\n", |
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"- **Open:** 0.6815\n", |
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"- **High:** 0.7177\n", |
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"- **Low:** 0.6632\n", |
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"- **Close:** 0.7037\n", |
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"\n", |
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"The price of $Cardano closed 3.59% higher than the previous day's close. This suggests a potential bullish reversal following a downward trend observed over the last few days. However, the volatility in the high-low range reflects uncertainty in the market.\n", |
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"\n", |
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"### **Trend Overview**\n", |
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"- **Short-term:** \n", |
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" - The recent price action indicates a possible recovery as we see an upward close. The price is currently attempting to break resistance, but the last few days exhibited mixed movements (e.g., a decrease before the recent increase). \n", |
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"- **Mid-term:**\n", |
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" - Over the past month, $Cardano has experienced significant volatility. While it reached its peak at around 1.079 earlier in January, the subsequent decline indicates selling pressure in the mid-term. A consolidation phase appears as buyers are trying to push the price back up.\n", |
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"- **Long-term:**\n", |
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" - Over the past year, $Cardano has shown high volatility and a fluctuating price range, but it has generally been trending downwards since its recent highs. \n", |
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"\n", |
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"### **Trading Signals**\n", |
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"- **Short-term Signal:** **Buy**\n", |
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" - The recent upward price movement along with a closing above 0.7000 indicates potential upward momentum. Short-term traders may consider buying into this recovery signal.\n", |
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"\n", |
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"- **Mid-term Signal:** **Hold**\n", |
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" - Within the last month, while recovery is in place, it is prudent to wait for confirmation of sustained upward movement before committing larger positions. A hold is advised to monitor the situation.\n", |
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"\n", |
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"- **Long-term Signal:** **Sell**\n", |
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" - Given that the longer-term trends show a downward trajectory since peaking at higher prices, long-term holders might consider selling or reducing positions, especially if the price fails to stay above recent resistance levels.\n", |
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"\n", |
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"### **Conclusion**\n", |
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"Today’s price action indicates a bullish sentiment in the short term but still reflects uncertainty in the mid and long-term periods. It would be wise for traders to remain cautious and adjust positions as the market dynamics evolve further. Always consider your risk management strategies when deciding to enter or exit positions." |
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], |
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"text/plain": [ |
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"<IPython.core.display.Markdown object>" |
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] |
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}, |
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"metadata": {}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
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"def get_response(prompt):\n", |
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" new_response = openai.chat.completions.create(model=\"gpt-4o-mini\",\n", |
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" messages=[\n", |
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" {\"role\": \"system\", \"content\": f\"You are a trading analyst providing Buy/Sell/Hold signals based on ${base_data['name']} price history. Format your response in markdown.Note that today is {end_date.strftime('%Y-%m-%d')}\"},\n", |
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" {\"role\": \"user\", \"content\": prompt}\n", |
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" ],\n", |
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" stream=True,\n", |
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" max_tokens=5500\n", |
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" )\n", |
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" \n", |
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" # Initialize markdown cell output\n", |
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" markdown_content = \"\"\n", |
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" \n", |
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" # Stream response and accumulate markdown content\n", |
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" for chunk in new_response:\n", |
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" content = chunk.choices[0].delta.content or ''\n", |
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" markdown_content += content\n", |
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" \n", |
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" # Clear output and display updated markdown\n", |
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" display(Markdown(markdown_content), clear=True)\n", |
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" \n", |
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" yield content\n", |
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"\n", |
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"# Call the function and consume the generator to start streaming\n", |
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"for _ in get_response(prompt):\n", |
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" pass" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "f52bcc0a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "venv", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.12.7" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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} |
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|
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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "fef36918-109d-41e3-8603-75ff81b42379", |
||||
"metadata": {}, |
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"source": [ |
||||
"# Solution for exercise day 2 - slight modification: model is a parameter also - display_summary(\"deepseek-r1:1.5b\",\"https://yoururl\")\n", |
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"\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "b50349ac-93ea-496b-ae20-bd72a93bb138", |
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"metadata": {}, |
||||
"outputs": [], |
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"source": [ |
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"# imports\n", |
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"\n", |
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"import requests\n", |
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"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "edd073c7-8444-4a0d-b84e-4b2ed0ee7f35", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Constants\n", |
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"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
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"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
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"#MODEL = \"llama3.2\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "2e3a6e1a-e4c7-4448-9852-1b6ba2bd8d66", |
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"metadata": {}, |
||||
"outputs": [], |
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"source": [ |
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"# A class to represent a Webpage\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
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"headers = {\n", |
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" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
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"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
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" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
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" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ae3752ca-3a97-4d6a-ac84-5b75ebfb50ed", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Define the system prompt \n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "48b5240f-7617-4e51-a320-cba9650bec84", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||
"\n", |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||
"please provide a short summary of this website in markdown. \\\n", |
||||
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6f7d84f0-60f2-4cbf-b4d1-173a79fe3380", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "25520a31-c857-4ed5-86da-50dfe5fab7bb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def summarize(model,url):\n", |
||||
" website = Website(url)\n", |
||||
" payload = {\n", |
||||
" \"model\": model,\n", |
||||
" \"messages\": messages_for(website),\n", |
||||
" \"stream\": False\n", |
||||
" }\n", |
||||
" response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
||||
" return response.json()['message']['content']" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "430776ed-8516-43a9-8a22-618d9080f2e1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||
"def display_summary(model,url):\n", |
||||
" summary = summarize(model,url)\n", |
||||
" display(Markdown(summary))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b2b05c1f-e4a2-4f65-bd6d-634d72e38b6e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#!ollama pull deepseek-r1:1.5b" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "01513f8a-15b7-4053-bfe4-44b36e5494d1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display_summary(\"deepseek-r1:1.5b\",\"https://www.ipma.pt\")" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.12.9" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,123 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Day 2 EXERCISE Solution:\n", |
||||
"\n", |
||||
"Upgraded day 1 project that scrapes and summarizes any webpage using an Open Source model running locally via Ollama instead of OpenAI\n", |
||||
"\n", |
||||
"## Note:-\n", |
||||
"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative: \n", |
||||
"1. Run `ollama pull llama3.2:1b` from a Terminal or Powershell\n", |
||||
"2. **Ctrl + /** to comment this code line below: `MODEL = \"llama3.2\"`\n", |
||||
"3. same **Ctrl + /** to uncomment: `MODEL = \"llama3.2:1b\"`" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports:-\n", |
||||
"\n", |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"import ollama" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "29ddd15d-a3c5-4f4e-a678-873f56162724", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Constants:-\n", |
||||
"\n", |
||||
"# MODEL = \"llama3.2\"\n", |
||||
"MODEL = \"llama3.2:1b\"\n", |
||||
"# MODEL = \"deepseek-r1:1.5b\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6de38216-6d1c-48c4-877b-86d403f4e0f8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"class Website:\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
"\n", |
||||
"\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
" and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
" Respond in markdown.\"\n", |
||||
"\n", |
||||
"\n", |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||
" please provide a short summary of this website in markdown. \\\n", |
||||
" If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt\n", |
||||
"\n", |
||||
"\n", |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ]\n", |
||||
"\n", |
||||
"\n", |
||||
"def summary(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = ollama.chat(\n", |
||||
" model = MODEL,\n", |
||||
" messages = messages_for(website)\n", |
||||
" )\n", |
||||
" return display(Markdown(response['message']['content']))\n", |
||||
"\n", |
||||
"\n", |
||||
"summary(\"https://edwarddonner.com\")\n", |
||||
"# summary(\"https://cnn.com\")\n", |
||||
"# summary(\"https://anthropic.com\")" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.10.7" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,432 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 52, |
||||
"id": "b56a950c-db41-4575-bef9-0fa651dea363", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import requests\n", |
||||
"import json\n", |
||||
"import ollama\n", |
||||
"from typing import List\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display, update_display,clear_output\n", |
||||
"\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0ec875db-0f6a-4eec-a3b6-eae4b71a4b89", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Constants\n", |
||||
"\n", |
||||
"MODEL = \"llama3.2\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "227cd07c-98a4-463b-94ad-94e33d04944b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
" \"\"\"\n", |
||||
" A utility class to represent a Website that we have scraped, now with links\n", |
||||
" \"\"\"\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" self.body = response.content\n", |
||||
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" if soup.body:\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
" else:\n", |
||||
" self.text = \"\"\n", |
||||
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||
" self.links = [link for link in links if link]\n", |
||||
"\n", |
||||
" def get_contents(self):\n", |
||||
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4d5c5e40-c010-4102-8359-899f988185fb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"ed = Website(\"https://edwarddonner.com\")\n", |
||||
"ed.links" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "5f0b5d71-487c-47a5-ace6-8e02465ed452", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", |
||||
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||
"link_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||
"link_system_prompt += \"\"\"\n", |
||||
"{\n", |
||||
" \"links\": [\n", |
||||
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", |
||||
" ]\n", |
||||
"}\n", |
||||
"\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c6550325-5160-42c9-b7e7-980b504cd096", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(link_system_prompt)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2db4ccc6-5c35-4775-a5b2-4b86e4c73808", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_links_user_prompt(website):\n", |
||||
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n", |
||||
"Do not include Terms of Service, Privacy, email links.\\n\"\n", |
||||
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||
" user_prompt += \"\\n\".join(website.links)\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8af511c7-5a74-4d1a-b763-b31370e70cff", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(get_links_user_prompt(ed))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a3b7fb61-ca15-4eab-b017-b0fe5cce46fd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_links(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = ollama.chat(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||
" ], format = \"json\" #Define format as json!\n", |
||||
" )\n", |
||||
" result = response['message']['content']\n", |
||||
"\n", |
||||
" return json.loads(result)\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7816d393-620d-4c53-913e-4ec130b2baba", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n", |
||||
"\n", |
||||
"anthropic = Website(\"https://anthropic.com\")\n", |
||||
"anthropic.links" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f32ceccb-1d45-41a3-a5c1-fb2e6cd76afe", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"get_links(\"https://anthropic.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a7ec4727-e897-473c-a657-e74f6999c974", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_all_details(url):\n", |
||||
" result = \"Landing page:\\n\"\n", |
||||
" result += Website(url).get_contents()\n", |
||||
" links = get_links(url)\n", |
||||
" print(\"Found links:\", links)\n", |
||||
" for link in links[\"links\"]:\n", |
||||
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||
" result += Website(link[\"url\"]).get_contents()\n", |
||||
" return result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7acde0c5-1af2-4e8e-9303-e2a98ec9cdbb", |
||||
"metadata": { |
||||
"scrolled": true |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(get_all_details(\"https://anthropic.com\"))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "5a2e2b1d-eb55-4bfb-bf55-5e8c87db0d96", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||
"Include details of company culture, customers and careers/jobs if you have the information.\"\n", |
||||
"\n", |
||||
"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n", |
||||
"\n", |
||||
"# system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||
"# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||
"# Include details of company culture, customers and careers/jobs if you have the information.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8eac1719-7f94-4460-bc4a-0c9c93bb17a5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_brochure_user_prompt(company_name, url):\n", |
||||
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||
" user_prompt += get_all_details(url)\n", |
||||
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e2e312f6-01c5-4e57-9134-fb4aa447d155", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"get_brochure_user_prompt(\"Anthropic\", \"https://anthropic.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8b05cbab-f0d2-4a9e-8b8c-c868a036e9cd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def create_brochure(company_name, url):\n", |
||||
" response = ollama.chat(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||
" ]\n", |
||||
" )\n", |
||||
" result = response[\"message\"][\"content\"]\n", |
||||
" display(Markdown(result))\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "91ede0c0-daf2-42ef-9d31-749afb9d5352", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"create_brochure(\"Anthropic\", \"https://anthropic.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "afb4aeee-5108-42a7-a1c1-5bad254b7e8b", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Final omprovement\n", |
||||
"\n", |
||||
"getting a typewriter animation" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 50, |
||||
"id": "177de611-1cb1-49e2-b7ea-8d01191af3ee", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def create_brochure(company_name, url):\n", |
||||
" messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||
" ]\n", |
||||
"\n", |
||||
" display_markdown = display(Markdown(\"\"), display_id=True) # Initialize Markdown display\n", |
||||
" response_text = \"\"\n", |
||||
"\n", |
||||
" for chunk in ollama.chat(model=MODEL, messages=messages, stream=True): # Ensure stream=True (not a string)\n", |
||||
" response_text += chunk['message']['content']\n", |
||||
" clear_output(wait=True) # Clear previous output to create a streaming effect\n", |
||||
" display_markdown.update(Markdown(response_text)) # Update Markdown dynamically\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 53, |
||||
"id": "a1971d81-fc7f-4ed1-97a0-7ef5e8ed332a", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"Found links: {'links': [{'type': 'About page', 'url': 'https://www.anthropic.com/company'}, {'type': 'Careers page', 'url': 'https://www.anthropic.com/careers'}, {'type': 'Company page', 'url': 'https://www.anthropic.com/'}, {'type': 'Research page', 'url': 'https://www.anthropic.com/research'}, {'type': 'Twitter profile', 'url': 'https://twitter.com/AnthropicAI'}, {'type': 'LinkedIn company page', 'url': 'https://www.linkedin.com/company/anthropicresearch'}, {'type': 'YouTube channel', 'url': 'https://www.youtube.com/@anthropic-ai'}]}\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/markdown": [ |
||||
"**Anthropic Brochure**\n", |
||||
"======================\n", |
||||
"\n", |
||||
"**Mission Statement**\n", |
||||
"-------------------\n", |
||||
"\n", |
||||
"Anthropic is an AI safety and research company dedicated to building reliable, interpretable, and steerable AI systems that benefit humanity in the long run.\n", |
||||
"\n", |
||||
"**Company Overview**\n", |
||||
"--------------------\n", |
||||
"\n", |
||||
"Anthropic is headquartered in San Francisco and brings together a diverse team of researchers, engineers, policy experts, and business leaders with experience spanning various disciplines. Our mission is to conduct frontier AI research, develop and apply safety techniques, and deploy the resulting systems via partnerships and products.\n", |
||||
"\n", |
||||
"**Research Focus**\n", |
||||
"-----------------\n", |
||||
"\n", |
||||
"Anthropic conducts cutting-edge AI research across various modalities, exploring novel and emerging safety research areas such as interpretability, RL from human feedback, policy, and societal impacts analysis. Our research aims to advance the field of AI safety and inform our product development.\n", |
||||
"\n", |
||||
"**Product Portfolio**\n", |
||||
"---------------------\n", |
||||
"\n", |
||||
"Our flagship product is Claude, a highly intelligent AI model that enables customers to build custom applications and experiences using our API. We also offer various enterprise solutions, including Claude for Enterprise, designed to meet the needs of large organizations.\n", |
||||
"\n", |
||||
"**Customer Base**\n", |
||||
"-----------------\n", |
||||
"\n", |
||||
"Anthropic serves a diverse range of customers, including businesses, nonprofits, civil society groups, and their clients around the globe. Our commitment to safety and reliability has earned us a reputation as a trusted partner in the AI industry.\n", |
||||
"\n", |
||||
"**Values and Culture**\n", |
||||
"----------------------\n", |
||||
"\n", |
||||
"At Anthropic, we value:\n", |
||||
"\n", |
||||
"* **Acting for the global good**: We strive to make decisions that maximize positive outcomes for humanity in the long run.\n", |
||||
"* **Holding light and shade**: We acknowledge the potential risks of AI and approach our work with caution and transparency.\n", |
||||
"\n", |
||||
"**Join Our Team**\n", |
||||
"-----------------\n", |
||||
"\n", |
||||
"We're a collaborative team of researchers, engineers, policy experts, and business leaders passionate about building safer AI systems. Join us to be part of this exciting journey and contribute your skills and expertise to shaping the future of AI.\n", |
||||
"\n", |
||||
"**Careers**\n", |
||||
"------------\n", |
||||
"\n", |
||||
"Check our website for open roles and learn more about our company culture, benefits, and career opportunities.\n", |
||||
"\n", |
||||
"[Learn More](link)\n", |
||||
"\n", |
||||
"**Get in Touch**\n", |
||||
"-----------------\n", |
||||
"\n", |
||||
"Stay up-to-date with the latest news and announcements from Anthropic. Follow us on Twitter, LinkedIn, or YouTube to join the conversation and stay informed.\n", |
||||
"\n", |
||||
"[Twitter](link)\n", |
||||
"[LinkedIn](link)\n", |
||||
"[YouTube](link)" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.Markdown object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
} |
||||
], |
||||
"source": [ |
||||
"create_brochure(\"Anthropic\", \"https://anthropic.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c33277a4-84f1-447c-a66e-eb7e2af42d2a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.13.2" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,76 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# 1) Import Required Libraries \n", |
||||
"\n", |
||||
"import requests\n", |
||||
"import gradio as gr\n", |
||||
"\n", |
||||
"# Deepseek only uses abstract summarization\n", |
||||
"# This tool use DeepSeek API Endpoint\n", |
||||
"\n", |
||||
"# 2) Define the DeepSeek API Endpoint\n", |
||||
"\n", |
||||
"OLLAMA_URL = \"http://localhost:11434/api/generate\"\n", |
||||
"\n", |
||||
"# 3) Define the Summarization Function which can retrieve Information\n", |
||||
"\n", |
||||
"def summarize_text(text):\n", |
||||
" payload = {\n", |
||||
" \"model\": \"deepseek-r1\", #Here you can load whatever the model you have in your ollama(ex:deepseek-r1:1.5b,7b,8b,14b) I used 7b model here \n", |
||||
" \"prompt\": f\"Summarize the following text in **5 bullet points**:\\n\\n{text}\", #The prompt is here for tell commands for the llm to act \n", |
||||
" \"stream\": False # Ensures the response is returned as a whole, not streamed\n", |
||||
" }\n", |
||||
"\n", |
||||
" response = requests.post(OLLAMA_URL, json=payload) #Send Requests to deepseekAPI\n", |
||||
"\n", |
||||
" if response.status_code == 200: #if server run correctly it return the result or it will give error\n", |
||||
" return response.json().get(\"response\", \"No summary generated.\")\n", |
||||
" else:\n", |
||||
" return f\"Error: {response.text}\"\n", |
||||
"\n", |
||||
"# 4) Create Gradio interface to design \n", |
||||
"interface = gr.Interface(\n", |
||||
" fn=summarize_text,\n", |
||||
" inputs=gr.Textbox(lines=10, placeholder=\"Enter text to summarize\"),\n", |
||||
" outputs=gr.Textbox(label=\"Summarized Text\"),\n", |
||||
" #theme='NoCrypt/miku', #Theme for the Interface I used Hatsune Miku from HF \n", |
||||
" title=\"AI-Powered Text Summarizer\",\n", |
||||
" description=\"Enter a long text and DeepSeek AI will generate a concise summary.\"\n", |
||||
")\n", |
||||
"\n", |
||||
"# Launch the web app\n", |
||||
"if __name__ == \"__main__\":\n", |
||||
" interface.launch()\n", |
||||
"\n", |
||||
"\n" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "base", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.12.4" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 2 |
||||
} |
@ -0,0 +1,138 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# End of week 1 exercise Solution Ollama with streaming\n", |
||||
"\n", |
||||
"A tool that takes a technical question, and responds with an explanation." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c1070317-3ed9-4659-abe3-828943230e03", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Imports\n", |
||||
"\n", |
||||
"import ollama\n", |
||||
"import requests\n", |
||||
"from IPython.display import Markdown, display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Constants\n", |
||||
"\n", |
||||
"MODEL_LLAMA = 'llama3.2'\n", |
||||
"MODEL_LLAMA1b = \"llama3.2:1b\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Environment\n", |
||||
"\n", |
||||
"system_prompt = \"\"\"\n", |
||||
"You are an assistant that takes a technical question and respond with an explanation.\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"question = \"\"\"\n", |
||||
"Please explain what this code does and why:\n", |
||||
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"question2 = \"\"\"\n", |
||||
"What is the purpose of using yield from in the following code, and how does it differ from a standard for loop with yield?\n", |
||||
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"user_prompt = \"Answer these two questions in detail please, Question1:\" + question + \"Question2:\" + question2\n", |
||||
"\n", |
||||
"def message():\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Llama 3.2 answer, with streaming\n", |
||||
"\n", |
||||
"def llama():\n", |
||||
" response = ollama.chat(\n", |
||||
" model = MODEL_LLAMA,\n", |
||||
" messages = message(),\n", |
||||
" stream =True\n", |
||||
" )\n", |
||||
" full_response = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" for chunk in response:\n", |
||||
" content = chunk.get(\"message\", {}).get(\"content\", \"\")\n", |
||||
" if content:\n", |
||||
" full_response += content\n", |
||||
" display_handle.update(Markdown(full_response))\n", |
||||
"llama()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "342a470c-9aab-4051-ad21-514dceec76eb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Llama 3.2:1b answer\n", |
||||
"\n", |
||||
"def llama():\n", |
||||
" response = ollama.chat(\n", |
||||
" model = MODEL_LLAMA1b,\n", |
||||
" messages = message()\n", |
||||
" )\n", |
||||
" return display(Markdown(response['message']['content']))\n", |
||||
"\n", |
||||
"llama()" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.10.7" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,408 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "raw", |
||||
"id": "f64407a0-fda5-48f3-a2d3-82e80d320931", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"### \"Career Well-Being Companion\" ###\n", |
||||
"This project will gather feelings at the end of day from employee.\n", |
||||
"Based on employee feelings provided as input, model will analyze feelings and provide suggestions and acknowledge with feelings employtee is going thru.\n", |
||||
"Model even will ask employee \"Do you want more detailed resposne to cope up with your feelings?\".\n", |
||||
"If employee agrees, model even replies with online courses, tools, meetups and other ideas for the well being of the employee.\n", |
||||
"\n", |
||||
"Immediate Impact: Professionals can quickly see value through insights or actionable suggestions.\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2b30a8fa-1067-4369-82fc-edb197551e43", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"### Step 1: Emotional Check-in:\n", |
||||
"\n", |
||||
"# Input: User describes their feelings or workday.\n", |
||||
"# LLM Task: Analyze the input for emotional tone and identify keywords (e.g., \"stress,\" \"boredom\").\n", |
||||
"# Output: A summary of emotional trends.\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2b52469e-da81-42ec-9e6c-0c121ad349a7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(\"I am your well being companion and end goal is to help you in your career.\\nI want to start by asking about your feelings, how was your day today.\\n\")\n", |
||||
"print(\"I will do my best as well being companion to analyze your day and come up with the suggestions that might help you in your career and life. \\n\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a6df2e2c-785d-4323-90f4-b49592ab33fc", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"how_was_day = \"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "247e4a80-f634-4a7a-9f40-315f042be59c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"how_was_day = input(\"How was your day today,can you describe about your day, what went well, what did not go well, what you did not like :\\n\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0faac2dd-0d53-431a-87a7-d57a6881e043", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"what_went_well = input(\"What went well for you , today?\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2c11628b-d14b-47eb-a97e-70d08ddf3364", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"what_went_bad = input(\"What did not go well, today?\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f64e34b4-f83a-4ae4-86bb-5bd164121412", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"how_was_day = how_was_day + what_went_well + what_went_bad\n", |
||||
"print(how_was_day)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c5fe08c4-4d21-4917-a556-89648eb543c7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"from openai import OpenAI\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"import json\n", |
||||
"from IPython.display import Markdown, display, update_display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d6875d51-f33b-462e-85cb-a5d6a7cfb86e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#Initialize environment and constants:\n", |
||||
"load_dotenv(override=True)\n", |
||||
"\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", |
||||
" print(\"API key looks good so far\")\n", |
||||
"else:\n", |
||||
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||
" \n", |
||||
"MODEL = 'gpt-4o-mini'\n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 1, |
||||
"id": "c12cf934-4bd4-4849-9e8f-5bb89eece996", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"### Step 2: From day spent and what went good, what went bad => LLM will extract feelings, emotions from those unspoken words :)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "237d14b3-571e-4598-a57b-d3ebeaf81afc", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt_for_emotion_check_in = \"You are a career well-being assistant. Your task is to analyze the user's emotional state based on their text input.\"\\\n", |
||||
"\"Look for signs of stress, burnout, dissatisfaction, boredom, motivation, or any other emotional indicators related to work.\"\\\n", |
||||
"\"Based on the input, provide a summary of the user's feelings and categorize them under relevant emotional states (e.g., ‘Burnout,’ ‘Boredom,’ ‘Stress,’ ‘Satisfaction,’ etc.).\"\\\n", |
||||
"\"Your response should be empathetic and non-judgmental. Please summarize the list of feelings, emotions , those unspoken but unheard feelings you get it.\\n\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a205a6d3-b0d7-4fcb-9eed-f3a86576cd9f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_feelings(how_was_day):\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages = [\n", |
||||
" {'role':'system','content': system_prompt_for_emotion_check_in},\n", |
||||
" {'role':'user', 'content': how_was_day}\n", |
||||
" ]\n", |
||||
" )\n", |
||||
" result = response.choices[0].message.content\n", |
||||
" return result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "45e152c8-37c4-4818-a8a0-49f1ea3c1b65", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"## LLM will give the feelings you have based on \"the day you had today\".\n", |
||||
"print(get_feelings(how_was_day))\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4a62a385-4c51-42b1-ad73-73949e740e66", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"### Step 3: From those feelings, emotions ==> Get suggestions from LLM." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d856ca4f-ade9-4e6f-b540-2d07a70867c7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"## Lets construct system prompt for LLM to get suggestions (from these feelings above).\n", |
||||
"\n", |
||||
"system_prompt_for_suggestion =\"You are a career well-being assistant.Provide a list of practical,actionable suggestions to help them improve their emotional state.\"\n", |
||||
"\n", |
||||
"system_prompt_for_suggestion+=\"The suggestions should be personalized based on their current feelings, and they should be simple, effective actions the user can take immediately.\"\\\n", |
||||
"\"Include activities, tasks, habits, or approaches that will either alleviate stress, boost motivation, or help them reconnect with their work in a positive way.\"\\\n", |
||||
"\"Be empathetic, non-judgmental, and encouraging in your tone.\\n\"\n", |
||||
"system_prompt_for_suggestion += \"Request you to respond in JSON format. Below is example:\\n\"\n", |
||||
"system_prompt_for_suggestion += '''\n", |
||||
"{\n", |
||||
" \"suggestions\": [\n", |
||||
" {\n", |
||||
" \"action\": \"Take a short break\",\n", |
||||
" \"description\": \"Step away from your workspace for 5-10 minutes. Use this time to take deep breaths, stretch, or grab a drink. This mini-break can help clear your mind and reduce feelings of overwhelm.\"\n", |
||||
" },\n", |
||||
" {\n", |
||||
" \"action\": \"Write a quick journal entry\",\n", |
||||
" \"description\": \"Spend 5-10 minutes writing down your thoughts and feelings. Specify what's distracting you and what you appreciate about your personal life. This can help you process emotions and refocus on tasks.\"\n", |
||||
" },\n", |
||||
" {\n", |
||||
" \"action\": \"Set a small task goal\",\n", |
||||
" \"description\": \"Choose one manageable task to complete today. Break it down into smaller steps to make it less daunting. Completing even a small task can give you a sense of achievement and boost motivation.\"\n", |
||||
" }\n", |
||||
" ]\n", |
||||
"}\n", |
||||
"'''\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e9eee380-7fa5-4d21-9357-f4fc34d3368d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"## Lets build user prompt to ask LLM for the suggestions based on the feelings above.\n", |
||||
"## Note: Here while building user_prompt, we are making another LLM call (via function get_feelings() to get feelings analyzed from \"day spent\".\n", |
||||
"## Because first step is to get feelings from day spent then we move to offer suggestions to ease discomfort feelings.\n", |
||||
"\n", |
||||
"def get_user_prompt_for_suggestion(how_was_day):\n", |
||||
" user_prompt_for_suggestion = \"You are a career well-being assistant.Please see below user’s emotional input on 'day user had spent' and this user input might have feeling burnt out, bored, uninspired, or stressed or sometime opposite \"\\\n", |
||||
" \"of these feelings.\"\n", |
||||
" user_prompt_for_suggestion += f\"{get_feelings(how_was_day)}\"\n", |
||||
" return user_prompt_for_suggestion\n", |
||||
" " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3576e451-b29c-44e1-bcdb-addc8d61afa7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(get_user_prompt_for_suggestion(how_was_day))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4a41ee40-1f49-4474-809f-a0d5e44e4aa4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_suggestions(how_was_day):\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages = [\n", |
||||
" {'role': 'system', 'content':system_prompt_for_suggestion},\n", |
||||
" {'role': 'user', 'content': get_user_prompt_for_suggestion(how_was_day)}\n", |
||||
" ],\n", |
||||
" response_format={\"type\": \"json_object\"}\n", |
||||
" )\n", |
||||
" result = response.choices[0].message.content\n", |
||||
" return json.loads(result)\n", |
||||
" #display(Markdown(result))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "33e3a14e-0e2c-43cb-b50b-d6df52b4d300", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"suggestions = get_suggestions(how_was_day)\n", |
||||
"print(suggestions)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "31c75e04-2800-4ba2-845b-bc38f8965622", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"### Step 4: From those suggestions from companion ==> Enhance with support you need to follow sugestions like action plan for your self." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d07f9d3f-5acf-4a86-9160-4c6de8df4eb0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt_for_enhanced_suggestions = \"You are a helpful assistant that enhances actionable suggestions for users. For each suggestion provided, enhance it by adding:\\n\"\\\n", |
||||
"\"1. A step-by-step guide for implementation.\"\\\n", |
||||
"\"2. Tools, resources, or apps that can help.\"\\\n", |
||||
"\"3. Examples or additional context to make the suggestion practical.\"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6ab449f1-7a6c-4982-99e0-83d99c45ad2d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_user_prompt_for_enhanced_suggestions(suggestions):\n", |
||||
" prompt = \"You are able to check below suggestions and can enhance to help end user. Below is the list of suggestions.\\n\"\n", |
||||
" prompt += f\"{suggestions}\"\n", |
||||
" return prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d5187b7a-d8cd-4377-b011-7805bd50443d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def enhance_suggestions(suggestions):\n", |
||||
" stream = openai.chat.completions.create(\n", |
||||
" model = MODEL,\n", |
||||
" messages=[\n", |
||||
" {'role':'system', 'content':system_prompt_for_enhanced_suggestions},\n", |
||||
" {'role':'user', 'content':get_user_prompt_for_enhanced_suggestions(suggestions)}\n", |
||||
" ],\n", |
||||
" stream = True\n", |
||||
" )\n", |
||||
" \n", |
||||
" #result = response.choices[0].message.content\n", |
||||
" #for chunk in stream:\n", |
||||
" # print(chunk.choices[0].delta.content or '', end='')\n", |
||||
"\n", |
||||
" response = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" for chunk in stream:\n", |
||||
" response += chunk.choices[0].delta.content or ''\n", |
||||
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||
" update_display(Markdown(response), display_id=display_handle.display_id)\n", |
||||
" \n", |
||||
" #display(Markdown(result))\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "429cd6f8-3215-4140-9a6d-82d14a9b9798", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"detailed = input(\"\\nWould you like a DETAILED PLAN for implementing this suggestion?(Yes/ No)\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "5efda045-5bde-4c51-bec6-95b5914102dd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"if detailed.lower() == 'yes':\n", |
||||
" enhance_suggestions(suggestions)\n", |
||||
"else:\n", |
||||
" print(suggestions)\n", |
||||
" " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1969b2ec-c850-4dfc-b790-8ae8e3fa36e9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,256 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Import tkinter and ollama to create the app" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 20, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import ollama\n", |
||||
"import tkinter as tk\n", |
||||
"from tkinter import ttk" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Basic configuration parameters for the Ollama API:" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 21, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||
"HEADERS = {\"Content-Type\":\"application/json\"}\n", |
||||
"MODEL = \"llama3.2\"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Initialize conversation history." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 22, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"conversation_history = []" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Defining the key presses. If user presses shit + enter then simply go to the next line. \n", |
||||
"\n", |
||||
"If user presses only enter then submit the question." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 23, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def handle_keypress(event):\n", |
||||
" if event.state & 0x1: # Check if Shift is pressed\n", |
||||
" return\n", |
||||
" else:\n", |
||||
" display_answer()\n", |
||||
" return 'break'" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Defining the function that will display answers using Ollama.\n", |
||||
"\n", |
||||
"\n", |
||||
"To turn it into a chatbot we simply append user's question and Ollama's response to our conversation history and pass that into Ollama as our next question." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 24, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def display_answer(event=None):\n", |
||||
" question_text['state'] = 'disabled'\n", |
||||
" question_text['bg'] = '#F0F0F0'\n", |
||||
" status_label.config(text=\"Looking for an answer...\")\n", |
||||
" root.update()\n", |
||||
"\n", |
||||
" # Get question text and prepare message\n", |
||||
" question = question_text.get(\"1.0\", tk.END).strip()\n", |
||||
" if question:\n", |
||||
" # Append the user's question to the conversation history\n", |
||||
" conversation_history.append({\"role\": \"user\", \"content\": question})\n", |
||||
"\n", |
||||
" # Pass the entire conversation history to Ollama\n", |
||||
" try:\n", |
||||
" # Get the answer\n", |
||||
" response = ollama.chat(model=MODEL, messages=conversation_history)\n", |
||||
" answer = response[\"message\"][\"content\"]\n", |
||||
"\n", |
||||
" # Append the assistant's answer to the conversation history\n", |
||||
" conversation_history.append({\"role\": \"assistant\", \"content\": answer})\n", |
||||
"\n", |
||||
" # Update the text widget with the answer\n", |
||||
" answer_text.configure(state='normal')\n", |
||||
" answer_text.delete(1.0, tk.END)\n", |
||||
" answer_text.insert(tk.END, answer)\n", |
||||
" answer_text.configure(state='disabled')\n", |
||||
"\n", |
||||
" status_label.config(text=\"Answered\")\n", |
||||
" except Exception as e:\n", |
||||
" answer_text.configure(state='normal')\n", |
||||
" answer_text.delete(1.0, tk.END)\n", |
||||
" answer_text.insert(tk.END, f\"Error: {str(e)}\")\n", |
||||
" answer_text.configure(state='disabled')\n", |
||||
" status_label.config(text=\"Error\")\n", |
||||
" else:\n", |
||||
" # If empty question string was received\n", |
||||
" answer_text.configure(state='normal')\n", |
||||
" answer_text.delete(1.0, tk.END)\n", |
||||
" answer_text.insert(tk.END, \"Please enter a question.\")\n", |
||||
" answer_text.configure(state='disabled')\n", |
||||
" status_label.config(text=\"\")\n", |
||||
"\n", |
||||
" # Re-enable question input and restore normal background\n", |
||||
" question_text['state'] = 'normal'\n", |
||||
" question_text['bg'] = 'white'\n", |
||||
" root.update()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"A button to remove the conversation history and start all over again." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def remove_all():\n", |
||||
" \"\"\"Clears the conversation history and resets the interface.\"\"\"\n", |
||||
" global conversation_history\n", |
||||
" conversation_history = [] # Clear conversation history\n", |
||||
"\n", |
||||
" # Reset text widgets\n", |
||||
" question_text.delete(1.0, tk.END)\n", |
||||
" answer_text.configure(state='normal')\n", |
||||
" answer_text.delete(1.0, tk.END)\n", |
||||
" answer_text.insert(tk.END, \"Your answer will appear here.\")\n", |
||||
" answer_text.configure(state='disabled')\n", |
||||
"\n", |
||||
" # Reset status label\n", |
||||
" status_label.config(text=\"\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Creating the app window using tkinter." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 18, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Create the main window\n", |
||||
"root = tk.Tk()\n", |
||||
"root.title(\"Ollama with GUI\")\n", |
||||
"root.geometry(\"500x800\")\n", |
||||
"\n", |
||||
"# Create and configure the Questions window\n", |
||||
"question_frame = ttk.LabelFrame(root, text=\"Questions\", padding=(10, 10))\n", |
||||
"question_frame.pack(fill=\"both\", expand=True, padx=10, pady=10)\n", |
||||
"\n", |
||||
"question_label = ttk.Label(question_frame, text=\"Enter your question:\")\n", |
||||
"question_label.pack(anchor=\"w\", pady=5)\n", |
||||
"\n", |
||||
"# Replace Entry with Text widget for questions\n", |
||||
"question_text = tk.Text(question_frame, wrap=tk.WORD, width=50, height=4)\n", |
||||
"question_text.pack(anchor=\"w\", pady=5)\n", |
||||
"question_text.bind(\"<Return>\", handle_keypress)\n", |
||||
"\n", |
||||
"# Add status label\n", |
||||
"status_label = ttk.Label(question_frame, text=\"\")\n", |
||||
"status_label.pack(anchor=\"w\", pady=5)\n", |
||||
"\n", |
||||
"# Add Remove All button\n", |
||||
"remove_all_button = ttk.Button(question_frame, text=\"Remove All\", command=remove_all)\n", |
||||
"remove_all_button.pack(anchor=\"e\", pady=5)\n", |
||||
"\n", |
||||
"# Create and configure the Answers window\n", |
||||
"answer_frame = ttk.LabelFrame(root, text=\"Answer\", padding=(10, 10))\n", |
||||
"answer_frame.pack(fill=\"both\", expand=True, padx=10, pady=10)\n", |
||||
"\n", |
||||
"# Create a frame to hold the text widget and scrollbar\n", |
||||
"text_frame = ttk.Frame(answer_frame)\n", |
||||
"text_frame.pack(fill=\"both\", expand=True)\n", |
||||
"\n", |
||||
"# Create the text widget and scrollbar\n", |
||||
"answer_text = tk.Text(text_frame, wrap=tk.WORD, width=70, height=100)\n", |
||||
"scrollbar = ttk.Scrollbar(text_frame, orient=\"vertical\", command=answer_text.yview)\n", |
||||
"answer_text.configure(yscrollcommand=scrollbar.set)\n", |
||||
"\n", |
||||
"# Pack the text widget and scrollbar\n", |
||||
"answer_text.pack(side=\"left\", fill=\"both\", expand=True)\n", |
||||
"scrollbar.pack(side=\"right\", fill=\"y\")\n", |
||||
"\n", |
||||
"# Set initial text and disable editing\n", |
||||
"answer_text.insert(tk.END, \"Your answer will appear here.\")\n", |
||||
"answer_text.configure(state='disabled')\n", |
||||
"\n", |
||||
"# Run the main event loop\n", |
||||
"root.mainloop()\n" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "llms", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 2 |
||||
} |
@ -0,0 +1,126 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d25b0aef-3e5e-4026-90ee-2b373bf262b7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Step 0: Import libraries and load environment variables\n", |
||||
"import os\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from openai import OpenAI\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv(\"OPENAI_API_KEY\")\n", |
||||
"\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found!\")\n", |
||||
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||
" print(\"An API key was found, but it does not start with 'sk-proj-'! Please ensure you are using the right key.\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end! Please remove them.\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")\n", |
||||
"\n", |
||||
"# Step 1: Create prompts\n", |
||||
"print(\"[INFO] Creating system prompt ...\")\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of \\\n", |
||||
" email texts and suggests short subject lines for the email based \\\n", |
||||
" on the requested tone and language. Respond in markdown.\"\n", |
||||
"\n", |
||||
"print(\"[INFO] Creating user prompt ...\")\n", |
||||
"user_prompt = \"\"\"\n", |
||||
" The text below is an e-mail text for which you are required to \\\n", |
||||
" provide subject lines. Please provide two snarky, two funny, and \\\n", |
||||
" two formal short subject lines for the email text. Each of the six \\\n", |
||||
" subject lines should be presented in both English and French \\\n", |
||||
" languages, making a total of 12 subject lines. Please provide your \\\n", |
||||
" answer in markdown.\\\n", |
||||
" \n", |
||||
" \\n\\n\n", |
||||
" \n", |
||||
" Welcome to arXiv!\n", |
||||
"\n", |
||||
" Thank you for creating an account and joining the arXiv community. We look\n", |
||||
" forward to receiving your contribution.\n", |
||||
"\n", |
||||
" Help Pages\n", |
||||
" An overview on how to navigate and use arXiv can be found here:\n", |
||||
" https://arxiv.org/help\n", |
||||
" https://arxiv.org/about\n", |
||||
"\n", |
||||
" If you would like to know more about the submission process, please go here:\n", |
||||
" https://arxiv.org/help/submit\n", |
||||
"\n", |
||||
" Before Submitting to arXiv\n", |
||||
" The arXiv.org e-print archive is fully automated and processes nearly\n", |
||||
" 1,000 new submissions per day. To help us keep the process running smoothly\n", |
||||
" and efficiently please check your submission carefully for mistakes, typos\n", |
||||
" and layout issues. Once you have submitted your work please check your account\n", |
||||
" frequently for verification messages and other communication from arXiv.\n", |
||||
"\n", |
||||
" Contacting arXiv\n", |
||||
" We have provided extensive help pages to guide you through the process and\n", |
||||
" to answer the most common questions. If you have problems with the submission\n", |
||||
" process please contact us here:\n", |
||||
" https://arxiv.org/help/contact\n", |
||||
" We aim to assist submitters within one business day, but during times of high\n", |
||||
" volume or maintenance work we may be slightly delayed in our response.\n", |
||||
"\n", |
||||
" Thank you for your cooperation.\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"# Step 2: Make messages list\n", |
||||
"print(\"[INFO] Making messages list ...\")\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
"]\n", |
||||
"\n", |
||||
"# Step 3: Call OpenAI\n", |
||||
"print(\"[INFO] Calling OpenAI ...\")\n", |
||||
"openai = OpenAI()\n", |
||||
"response = openai.chat.completions.create(\n", |
||||
" model=\"gpt-4o-mini\",\n", |
||||
" messages=messages\n", |
||||
" )\n", |
||||
"\n", |
||||
"# Step 4: Print result\n", |
||||
"print(\"[INFO] Print result ...\")\n", |
||||
"display(Markdown(response.choices[0].message.content))\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b0a6676e-fb43-4725-9389-2acd74c13c4e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.12.8" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,129 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d25b0aef-3e5e-4026-90ee-2b373bf262b7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Step 0: Import Libraries\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"import ollama\n", |
||||
"from openai import OpenAI\n", |
||||
"import requests\n", |
||||
"\n", |
||||
"# Step 1: Set Constants and Variables\n", |
||||
"print(\"[INFO] Setting constants and variable ...\")\n", |
||||
"WEBSITE_URL = \"https://arxiv.org/\"\n", |
||||
"MODEL = \"llama3.2\"\n", |
||||
"approaches = [\"local-call\", \"python-package\", \"openai-python-library\"]\n", |
||||
"approach = approaches[2]\n", |
||||
"\n", |
||||
"# Step 1: Scrape Website\n", |
||||
"print(\"[INFO] Scraping website ...\")\n", |
||||
"url_response = requests.get(\n", |
||||
" url=WEBSITE_URL,\n", |
||||
" headers={\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"}\n", |
||||
" )\n", |
||||
"soup = BeautifulSoup(\n", |
||||
" markup=url_response.content,\n", |
||||
" features=\"html.parser\"\n", |
||||
" )\n", |
||||
"website_title = soup.title.string if soup.title else \"No title found!!!\"\n", |
||||
"for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
"website_text = soup.body.get_text(\n", |
||||
" separator=\"\\n\",\n", |
||||
" strip=True\n", |
||||
" )\n", |
||||
"\n", |
||||
"# Step 2: Create Prompts\n", |
||||
"print(\"[INFO] Creating system prompt ...\")\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a \\\n", |
||||
" website and provides a short summary, ignoring text that might be \\\n", |
||||
" navigation related. Respond in markdown.\"\n", |
||||
"\n", |
||||
"print(\"[INFO] Creating user prompt ...\")\n", |
||||
"user_prompt = f\"You are looking at a website titled {website_title}\"\n", |
||||
"user_prompt += \"\\nBased on the contents of the website, please provide \\\n", |
||||
" a short summary of this website in markdown. If the website \\\n", |
||||
" includes news or announcements, summarize them, too. The contents \\\n", |
||||
" of this website are as follows:\\n\\n\"\n", |
||||
"user_prompt += website_text\n", |
||||
"\n", |
||||
"# Step 3: Make Messages List\n", |
||||
"print(\"[INFO] Making messages list ...\")\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
"]\n", |
||||
"\n", |
||||
"# Step 4: Call Model and Print Results\n", |
||||
"if approach == \"local-call\":\n", |
||||
" response = requests.post(\n", |
||||
" url=\"http://localhost:11434/api/chat\",\n", |
||||
" json={\n", |
||||
" \"model\": MODEL,\n", |
||||
" \"messages\": messages,\n", |
||||
" \"stream\": False\n", |
||||
" },\n", |
||||
" headers={\"Content-Type\": \"application/json\"}\n", |
||||
" )\n", |
||||
" print(\"[INFO] Printing result ...\")\n", |
||||
" display(Markdown(response.json()[\"message\"][\"content\"]))\n", |
||||
"elif approach == \"python-package\":\n", |
||||
" response = ollama.chat(\n", |
||||
" model=MODEL,\n", |
||||
" messages=messages,\n", |
||||
" stream=False\n", |
||||
" )\n", |
||||
" print(\"[INFO] Printing result ...\")\n", |
||||
" display(Markdown(response[\"message\"][\"content\"]))\n", |
||||
"elif approach == \"openai-python-library\":\n", |
||||
" ollama_via_openai = OpenAI(\n", |
||||
" base_url=\"http://localhost:11434/v1\",\n", |
||||
" api_key=\"ollama\"\n", |
||||
" )\n", |
||||
" response = ollama_via_openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=messages\n", |
||||
" )\n", |
||||
" print(\"[INFO] Printing result ...\")\n", |
||||
" display(Markdown(response.choices[0].message.content))\n", |
||||
"else:\n", |
||||
" raise ValueError(f\"[INFO] Invalid approach! Please select an approach from {approaches} and try again.\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b0a6676e-fb43-4725-9389-2acd74c13c4e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.12.8" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,530 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## DAY1 LLM Project with GROQ!\n", |
||||
"\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from groq import Groq\n", |
||||
"\n", |
||||
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "5d899ad6-1428-481b-b308-750308d80442", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"If you are getting error ModuleNotFoundError: No module named 'groq' follow below steps.\n", |
||||
"\n", |
||||
"1. Activate llms enviornment from Anaconda, so that (llms) is showing in your prompt, as this is the environment where the package will get installed.Install pip here. \n", |
||||
"\n", |
||||
"(base) PS C:\\Users\\test\\OneDrive\\Desktop\\AI\\projects\\llm_engineering> conda activate llms\n", |
||||
"(llms) PS C:\\Users\\test\\OneDrive\\Desktop\\AI\\projects\\llm_engineering> pip install groq\n", |
||||
"\n", |
||||
"\n", |
||||
"2. After you install a new package, you'd need to restart the Kernel in jupyter lab for each notebook (Kernel >> Restart Kernel and Clear Values Of All Outputs).\n", |
||||
"\n", |
||||
"You can also run this command in jupyter lab to see whether it's installed:\n", |
||||
"\n", |
||||
"!pip show groq\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "99c0c3c9-fa5e-405e-8453-2a557dc60c09", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!pip show groq" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6900b2a8-6384-4316-8aaa-5e519fca4254", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Connecting to GROQ\n", |
||||
"\n", |
||||
"The next cell is where we load in the environment variables in your `.env` file and connect to GROQ.\n", |
||||
"\n", |
||||
".env file should have below entry\n", |
||||
"\n", |
||||
"GROQ_API_KEY=gsk_xxxxxx\n", |
||||
"\n", |
||||
"GROQ keys can be configired by logging to below link\n", |
||||
"https://console.groq.com/keys\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7b87cadb-d513-4303-baee-a37b6f938e4d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('GROQ_API_KEY')\n", |
||||
"\n", |
||||
"# Check the key\n", |
||||
"\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||
"elif not api_key.startswith(\"gsk_\"):\n", |
||||
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"groq = Groq()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "442fc84b-0815-4f40-99ab-d9a5da6bda91", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Let's make a quick call to a Frontier model to get started, as a preview!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a58394bf-1e45-46af-9bfd-01e24da6f49a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# To give you a preview -- calling Groq with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n", |
||||
"\n", |
||||
"message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n", |
||||
"response = groq.chat.completions.create(model=\"llama-3.3-70b-versatile\", messages=[{\"role\":\"user\", \"content\":message}])\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "2aa190e5-cb31-456a-96cc-db109919cd78", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## OK onwards with our first project" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c5e793b2-6775-426a-a139-4848291d0463", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||
"\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's try one out. Change the website and add print statements to follow along.\n", |
||||
"\n", |
||||
"ed = Website(\"https://edwarddonner.com\")\n", |
||||
"print(ed.title)\n", |
||||
"print(ed.text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6a478a0c-2c53-48ff-869c-4d08199931e1", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Types of prompts\n", |
||||
"\n", |
||||
"You may know this already - but if not, you will get very familiar with it!\n", |
||||
"\n", |
||||
"Models like GPT4o have been trained to receive instructions in a particular way.\n", |
||||
"\n", |
||||
"They expect to receive:\n", |
||||
"\n", |
||||
"**A system prompt** that tells them what task they are performing and what tone they should use\n", |
||||
"\n", |
||||
"**A user prompt** -- the conversation starter that they should reply to" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "abdb8417-c5dc-44bc-9bee-2e059d162699", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||
"\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||
"\n", |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||
"please provide a short summary of this website in markdown. \\\n", |
||||
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "26448ec4-5c00-4204-baec-7df91d11ff2e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(user_prompt_for(ed))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Messages\n", |
||||
"\n", |
||||
"Similar to OPENAI GROQ APIs share this structure:\n", |
||||
"\n", |
||||
"```\n", |
||||
"[\n", |
||||
" {\"role\": \"system\", \"content\": \"system message goes here\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"user message goes here\"}\n", |
||||
"]\n", |
||||
"\n", |
||||
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the might GPT (yet!)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n", |
||||
"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "21ed95c5-7001-47de-a36d-1d6673b403ce", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# To give you a preview -- calling Groq with system and user messages:\n", |
||||
"\n", |
||||
"response = groq.chat.completions.create(model=\"llama-3.3-70b-versatile\", messages=messages)\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## And now let's build useful messages for LLAMA3.3, using a function" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0134dfa4-8299-48b5-b444-f2a8c3403c88", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# See how this function creates exactly the format above\n", |
||||
"\n", |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "36478464-39ee-485c-9f3f-6a4e458dbc9c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Try this out, and then try for a few more websites\n", |
||||
"\n", |
||||
"messages_for(ed)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Time to bring it together - the API for GROQ is very simple!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "905b9919-aba7-45b5-ae65-81b3d1d78e34", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# And now: call the GROQ API\n", |
||||
"\n", |
||||
"def summarize(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = groq.chat.completions.create(\n", |
||||
" model = \"llama-3.3-70b-versatile\",\n", |
||||
" messages = messages_for(website)\n", |
||||
" )\n", |
||||
" return response.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"summarize(\"https://edwarddonner.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3d926d59-450e-4609-92ba-2d6f244f1342", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||
"\n", |
||||
"def display_summary(url):\n", |
||||
" summary = summarize(url)\n", |
||||
" display(Markdown(summary))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3018853a-445f-41ff-9560-d925d1774b2f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display_summary(\"https://edwarddonner.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Let's try more websites\n", |
||||
"\n", |
||||
"Note that this will only work on websites that can be scraped using this simplistic approach.\n", |
||||
"\n", |
||||
"Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n", |
||||
"\n", |
||||
"Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n", |
||||
"\n", |
||||
"But many websites will work just fine!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "45d83403-a24c-44b5-84ac-961449b4008f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display_summary(\"https://cnn.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "75e9fd40-b354-4341-991e-863ef2e59db7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display_summary(\"https://anthropic.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "c951be1a-7f1b-448f-af1f-845978e47e2c", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#181;\">Business applications</h2>\n", |
||||
" <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n", |
||||
"\n", |
||||
"More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>\n", |
||||
"\n", |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n", |
||||
" <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "00743dac-0e70-45b7-879a-d7293a6f68a6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Step 1: Create your prompts\n", |
||||
"\n", |
||||
"system_prompt = \"something here\"\n", |
||||
"user_prompt = \"\"\"\n", |
||||
" Lots of text\n", |
||||
" Can be pasted here\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"# Step 2: Make the messages list\n", |
||||
"\n", |
||||
"messages = [] # fill this in\n", |
||||
"\n", |
||||
"# Step 3: Call OpenAI\n", |
||||
"\n", |
||||
"response =\n", |
||||
"\n", |
||||
"# Step 4: print the result\n", |
||||
"\n", |
||||
"print(" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "36ed9f14-b349-40e9-a42c-b367e77f8bda", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## An extra exercise for those who enjoy web scraping\n", |
||||
"\n", |
||||
"You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "eeab24dc-5f90-4570-b542-b0585aca3eb6", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Sharing your code\n", |
||||
"\n", |
||||
"I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n", |
||||
"\n", |
||||
"If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n", |
||||
"\n", |
||||
"Here are good instructions courtesy of an AI friend: \n", |
||||
"https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,530 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## DAY1 LLM Project with GROQ!\n", |
||||
"\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from groq import Groq\n", |
||||
"\n", |
||||
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "5d899ad6-1428-481b-b308-750308d80442", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"If you are getting error ModuleNotFoundError: No module named 'groq' follow below steps.\n", |
||||
"\n", |
||||
"1. Activate llms enviornment from Anaconda, so that (llms) is showing in your prompt, as this is the environment where the package will get installed.Install pip here. \n", |
||||
"\n", |
||||
"(base) PS C:\\Users\\test\\OneDrive\\Desktop\\AI\\projects\\llm_engineering> conda activate llms\n", |
||||
"(llms) PS C:\\Users\\test\\OneDrive\\Desktop\\AI\\projects\\llm_engineering> pip install groq\n", |
||||
"\n", |
||||
"\n", |
||||
"2. After you install a new package, you'd need to restart the Kernel in jupyter lab for each notebook (Kernel >> Restart Kernel and Clear Values Of All Outputs).\n", |
||||
"\n", |
||||
"You can also run this command in jupyter lab to see whether it's installed:\n", |
||||
"\n", |
||||
"!pip show groq\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "99c0c3c9-fa5e-405e-8453-2a557dc60c09", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!pip show groq" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6900b2a8-6384-4316-8aaa-5e519fca4254", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Connecting to GROQ\n", |
||||
"\n", |
||||
"The next cell is where we load in the environment variables in your `.env` file and connect to GROQ.\n", |
||||
"\n", |
||||
".env file should have below entry\n", |
||||
"\n", |
||||
"GROQ_API_KEY=gsk_xxxxxx\n", |
||||
"\n", |
||||
"GROQ keys can be configired by logging to below link\n", |
||||
"https://console.groq.com/keys\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7b87cadb-d513-4303-baee-a37b6f938e4d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('GROQ_API_KEY')\n", |
||||
"\n", |
||||
"# Check the key\n", |
||||
"\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||
"elif not api_key.startswith(\"gsk_\"):\n", |
||||
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"groq = Groq()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "442fc84b-0815-4f40-99ab-d9a5da6bda91", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Let's make a quick call to a Frontier model to get started, as a preview!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a58394bf-1e45-46af-9bfd-01e24da6f49a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# To give you a preview -- calling Groq with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n", |
||||
"\n", |
||||
"message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n", |
||||
"response = groq.chat.completions.create(model=\"llama-3.3-70b-versatile\", messages=[{\"role\":\"user\", \"content\":message}])\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "2aa190e5-cb31-456a-96cc-db109919cd78", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## OK onwards with our first project" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c5e793b2-6775-426a-a139-4848291d0463", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||
"\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's try one out. Change the website and add print statements to follow along.\n", |
||||
"\n", |
||||
"ed = Website(\"https://edwarddonner.com\")\n", |
||||
"print(ed.title)\n", |
||||
"print(ed.text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6a478a0c-2c53-48ff-869c-4d08199931e1", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Types of prompts\n", |
||||
"\n", |
||||
"You may know this already - but if not, you will get very familiar with it!\n", |
||||
"\n", |
||||
"Models like GPT4o have been trained to receive instructions in a particular way.\n", |
||||
"\n", |
||||
"They expect to receive:\n", |
||||
"\n", |
||||
"**A system prompt** that tells them what task they are performing and what tone they should use\n", |
||||
"\n", |
||||
"**A user prompt** -- the conversation starter that they should reply to" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "abdb8417-c5dc-44bc-9bee-2e059d162699", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||
"\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||
"\n", |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||
"please provide a short summary of this website in markdown. \\\n", |
||||
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "26448ec4-5c00-4204-baec-7df91d11ff2e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(user_prompt_for(ed))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Messages\n", |
||||
"\n", |
||||
"Similar to OPENAI GROQ APIs share this structure:\n", |
||||
"\n", |
||||
"```\n", |
||||
"[\n", |
||||
" {\"role\": \"system\", \"content\": \"system message goes here\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"user message goes here\"}\n", |
||||
"]\n", |
||||
"\n", |
||||
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the might GPT (yet!)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n", |
||||
"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "21ed95c5-7001-47de-a36d-1d6673b403ce", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# To give you a preview -- calling Groq with system and user messages:\n", |
||||
"\n", |
||||
"response = groq.chat.completions.create(model=\"llama-3.3-70b-versatile\", messages=messages)\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## And now let's build useful messages for LLAMA3.3, using a function" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0134dfa4-8299-48b5-b444-f2a8c3403c88", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# See how this function creates exactly the format above\n", |
||||
"\n", |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "36478464-39ee-485c-9f3f-6a4e458dbc9c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Try this out, and then try for a few more websites\n", |
||||
"\n", |
||||
"messages_for(ed)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Time to bring it together - the API for GROQ is very simple!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "905b9919-aba7-45b5-ae65-81b3d1d78e34", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# And now: call the GROQ API\n", |
||||
"\n", |
||||
"def summarize(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = groq.chat.completions.create(\n", |
||||
" model = \"llama-3.3-70b-versatile\",\n", |
||||
" messages = messages_for(website)\n", |
||||
" )\n", |
||||
" return response.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"summarize(\"https://edwarddonner.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3d926d59-450e-4609-92ba-2d6f244f1342", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||
"\n", |
||||
"def display_summary(url):\n", |
||||
" summary = summarize(url)\n", |
||||
" display(Markdown(summary))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3018853a-445f-41ff-9560-d925d1774b2f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display_summary(\"https://edwarddonner.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Let's try more websites\n", |
||||
"\n", |
||||
"Note that this will only work on websites that can be scraped using this simplistic approach.\n", |
||||
"\n", |
||||
"Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n", |
||||
"\n", |
||||
"Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n", |
||||
"\n", |
||||
"But many websites will work just fine!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "45d83403-a24c-44b5-84ac-961449b4008f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display_summary(\"https://cnn.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "75e9fd40-b354-4341-991e-863ef2e59db7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display_summary(\"https://anthropic.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "c951be1a-7f1b-448f-af1f-845978e47e2c", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#181;\">Business applications</h2>\n", |
||||
" <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n", |
||||
"\n", |
||||
"More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>\n", |
||||
"\n", |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n", |
||||
" <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "00743dac-0e70-45b7-879a-d7293a6f68a6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Step 1: Create your prompts\n", |
||||
"\n", |
||||
"system_prompt = \"something here\"\n", |
||||
"user_prompt = \"\"\"\n", |
||||
" Lots of text\n", |
||||
" Can be pasted here\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"# Step 2: Make the messages list\n", |
||||
"\n", |
||||
"messages = [] # fill this in\n", |
||||
"\n", |
||||
"# Step 3: Call OpenAI\n", |
||||
"\n", |
||||
"response =\n", |
||||
"\n", |
||||
"# Step 4: print the result\n", |
||||
"\n", |
||||
"print(" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "36ed9f14-b349-40e9-a42c-b367e77f8bda", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## An extra exercise for those who enjoy web scraping\n", |
||||
"\n", |
||||
"You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "eeab24dc-5f90-4570-b542-b0585aca3eb6", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Sharing your code\n", |
||||
"\n", |
||||
"I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n", |
||||
"\n", |
||||
"If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n", |
||||
"\n", |
||||
"Here are good instructions courtesy of an AI friend: \n", |
||||
"https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,131 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 2, |
||||
"id": "f3c6d883-58a2-47de-823f-3c7430cffcc9", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"\"Airbrush or Air Bust? Let's Find Out!\"\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from openai import OpenAI\n", |
||||
"\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"\n", |
||||
"openai = OpenAI()\n", |
||||
"\n", |
||||
"# Step 1: Create your prompts\n", |
||||
"\n", |
||||
"system_prompt = \"You will take the body of an email and evaluate it to suggest a brief snarky subject\"\n", |
||||
"user_prompt = \"\"\"\n", |
||||
"Dear Air Brush Customer Service Team,\n", |
||||
"\n", |
||||
"I hope this message finds you well. I am writing to formally lodge a complaint regarding the airbrush product I purchased from your store. Unfortunately, the product I received is defective and does not meet the quality standards as advertised.\n", |
||||
"\n", |
||||
"Below are the details of my issue:\n", |
||||
"\n", |
||||
"Order Number: #12345\n", |
||||
"\n", |
||||
"Product Name: Air Brush model 123\n", |
||||
"\n", |
||||
"Date of Purchase: 18/1/2025\n", |
||||
"\n", |
||||
"Issue Description:\n", |
||||
"Defective Nozzle: The nozzle of the airbrush is clogged and does not allow proper airflow, making it impossible to use.\n", |
||||
"\n", |
||||
"Inconsistent Spray Pattern: Even after multiple attempts to clean and adjust the settings, the spray pattern is uneven and inconsistent.\n", |
||||
"\n", |
||||
"Leakage: The airbrush leaks air and paint from the joints, which is a significant safety hazard.\n", |
||||
"\n", |
||||
"Build Quality: The overall build quality of the product feels subpar, with loose fittings and a flimsy trigger mechanism.\n", |
||||
"\n", |
||||
"Steps Taken:\n", |
||||
"I followed the user manual and cleaning instructions provided, but the issues persist.\n", |
||||
"\n", |
||||
"I also reached out to your technical support team on [Date] but have not received a resolution.\n", |
||||
"\n", |
||||
"Expectation:\n", |
||||
"Given the defective nature of the product, I would like to request a full refund for the item. Alternatively, if a refund is not possible, I would appreciate a replacement with a fully functional unit.\n", |
||||
"\n", |
||||
"Attachments:\n", |
||||
"I have attached photos and a video demonstrating the issues for your reference.\n", |
||||
"\n", |
||||
"Copies of the invoice and order confirmation are also attached for your convenience.\n", |
||||
"\n", |
||||
"Request for Resolution:\n", |
||||
"Kindly let me know the next steps to process the refund or replacement. I would appreciate a prompt response within [X business days, e.g., 3-5 business days] to resolve this matter.\n", |
||||
"\n", |
||||
"Thank you for your attention to this issue. I trust that you will handle this matter professionally and ensure customer satisfaction.\n", |
||||
"\n", |
||||
"Looking forward to your swift response.\n", |
||||
"\n", |
||||
"Best regards,\n", |
||||
"Oya YILDIZ\n", |
||||
"İstanbul\n", |
||||
"Turkey\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"# Step 2: Make the messages list\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
"] # fill this in\n", |
||||
"\n", |
||||
"# Step 3: Call OpenAI\n", |
||||
"\n", |
||||
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n", |
||||
"\n", |
||||
"# Step 4: print the result\n", |
||||
"\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d9b655de-e8c3-4136-b6a6-2fb0ce01c364", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,159 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from openai import OpenAI\n", |
||||
"\n", |
||||
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7b87cadb-d513-4303-baee-a37b6f938e4d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"# Check the key\n", |
||||
"\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0d2d5441-2afe-41b9-8039-c367acd715f9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c5e793b2-6775-426a-a139-4848291d0463", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||
"\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7c7e0988-8f2d-4844-a847-eebec76b114a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"website = \"https://www.screener.in/company/CMSINFO/\"\n", |
||||
"biz = Website(website)\n", |
||||
"user_prompt = \"Give short summary of the business \" + biz.text +\" and recommend pros and cons of the business in bullet points alongwith recommendation to buy or sell\"\n", |
||||
"print(user_prompt)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "00743dac-0e70-45b7-879a-d7293a6f68a6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Step 1: Create your prompts\n", |
||||
"website = \"https://www.screener.in/company/CMSINFO/\"\n", |
||||
"biz = Website(website)\n", |
||||
"\n", |
||||
"system_prompt = \"You are an equity research analyst. Analyze the content of the website and give a summary of the business\"\n", |
||||
"user_prompt = \"Give short summary of the business \" + biz.text +\" and recommend pros and cons of the business in bullet points alongwith recommendation to buy or sell\"\n", |
||||
"\n", |
||||
"# Step 2: Make the messages list\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
"]\n", |
||||
"# Step 3: Call OpenAI\n", |
||||
"\n", |
||||
"# To give you a preview -- calling OpenAI with system and user messages:\n", |
||||
"\n", |
||||
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n", |
||||
"# Step 4: print the result\n", |
||||
"\n", |
||||
"print(response.choices[0].message.content)\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d9edf96e-1190-44fe-9261-405709fb39cd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,651 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Instant Gratification\n", |
||||
"\n", |
||||
"## Your first Frontier LLM Project!\n", |
||||
"\n", |
||||
"Let's build a useful LLM solution - in a matter of minutes.\n", |
||||
"\n", |
||||
"By the end of this course, you will have built an autonomous Agentic AI solution with 7 agents that collaborate to solve a business problem. All in good time! We will start with something smaller...\n", |
||||
"\n", |
||||
"Our goal is to code a new kind of Web Browser. Give it a URL, and it will respond with a summary. The Reader's Digest of the internet!!\n", |
||||
"\n", |
||||
"Before starting, you should have completed the setup for [PC](../SETUP-PC.md) or [Mac](../SETUP-mac.md) and you hopefully launched this jupyter lab from within the project root directory, with your environment activated.\n", |
||||
"\n", |
||||
"## If you're new to Jupyter Lab\n", |
||||
"\n", |
||||
"Welcome to the wonderful world of Data Science experimentation! Once you've used Jupyter Lab, you'll wonder how you ever lived without it. Simply click in each \"cell\" with code in it, such as the cell immediately below this text, and hit Shift+Return to execute that cell. As you wish, you can add a cell with the + button in the toolbar, and print values of variables, or try out variations. \n", |
||||
"\n", |
||||
"I've written a notebook called [Guide to Jupyter](Guide%20to%20Jupyter.ipynb) to help you get more familiar with Jupyter Labs, including adding Markdown comments, using `!` to run shell commands, and `tqdm` to show progress.\n", |
||||
"\n", |
||||
"## If you'd prefer to work in IDEs\n", |
||||
"\n", |
||||
"If you're more comfortable in IDEs like VSCode or Pycharm, they both work great with these lab notebooks too. \n", |
||||
"If you'd prefer to work in VSCode, [here](https://chatgpt.com/share/676f2e19-c228-8012-9911-6ca42f8ed766) are instructions from an AI friend on how to configure it for the course.\n", |
||||
"\n", |
||||
"## If you'd like to brush up your Python\n", |
||||
"\n", |
||||
"I've added a notebook called [Intermediate Python](Intermediate%20Python.ipynb) to get you up to speed. But you should give it a miss if you already have a good idea what this code does: \n", |
||||
"`yield from {book.get(\"author\") for book in books if book.get(\"author\")}`\n", |
||||
"\n", |
||||
"## I am here to help\n", |
||||
"\n", |
||||
"If you have any problems at all, please do reach out. \n", |
||||
"I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!)\n", |
||||
"\n", |
||||
"## More troubleshooting\n", |
||||
"\n", |
||||
"Please see the [troubleshooting](troubleshooting.ipynb) notebook in this folder to diagnose and fix common problems. At the very end of it is a diagnostics script with some useful debug info.\n", |
||||
"\n", |
||||
"## If this is old hat!\n", |
||||
"\n", |
||||
"If you're already comfortable with today's material, please hang in there; you can move swiftly through the first few labs - we will get much more in depth as the weeks progress.\n", |
||||
"\n", |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Please read - important note</h2>\n", |
||||
" <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you do this with me, either at the same time, or (perhaps better) right afterwards. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>\n", |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#181;\">Business value of these exercises</h2>\n", |
||||
" <span style=\"color:#181;\">A final thought. While I've designed these notebooks to be educational, I've also tried to make them enjoyable. We'll do fun things like have LLMs tell jokes and argue with each other. But fundamentally, my goal is to teach skills you can apply in business. I'll explain business implications as we go, and it's worth keeping this in mind: as you build experience with models and techniques, think of ways you could put this into action at work today. Please do contact me if you'd like to discuss more or if you have ideas to bounce off me.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from openai import OpenAI\n", |
||||
"\n", |
||||
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6900b2a8-6384-4316-8aaa-5e519fca4254", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Connecting to OpenAI\n", |
||||
"\n", |
||||
"The next cell is where we load in the environment variables in your `.env` file and connect to OpenAI.\n", |
||||
"\n", |
||||
"## Troubleshooting if you have problems:\n", |
||||
"\n", |
||||
"Head over to the [troubleshooting](troubleshooting.ipynb) notebook in this folder for step by step code to identify the root cause and fix it!\n", |
||||
"\n", |
||||
"If you make a change, try restarting the \"Kernel\" (the python process sitting behind this notebook) by Kernel menu >> Restart Kernel and Clear Outputs of All Cells. Then try this notebook again, starting at the top.\n", |
||||
"\n", |
||||
"Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n", |
||||
"\n", |
||||
"Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7b87cadb-d513-4303-baee-a37b6f938e4d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"# Check the key\n", |
||||
"\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"openai = OpenAI()\n", |
||||
"\n", |
||||
"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n", |
||||
"# If it STILL doesn't work (horrors!) then please see the Troubleshooting notebook in this folder for full instructions" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "442fc84b-0815-4f40-99ab-d9a5da6bda91", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Let's make a quick call to a Frontier model to get started, as a preview!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a58394bf-1e45-46af-9bfd-01e24da6f49a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n", |
||||
"\n", |
||||
"message = \"Tell me about a way to analyse what people do in a video clip.\"\n", |
||||
"#response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=[{\"role\":\"user\", \"content\":message}])\n", |
||||
"#print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "2aa190e5-cb31-456a-96cc-db109919cd78", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## OK onwards with our first project" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c5e793b2-6775-426a-a139-4848291d0463", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||
"\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's try one out. Change the website and add print statements to follow along.\n", |
||||
"\n", |
||||
"ed = Website(\"https://edwarddonner.com\")\n", |
||||
"print(ed.title)\n", |
||||
"print(ed.text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6a478a0c-2c53-48ff-869c-4d08199931e1", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Types of prompts\n", |
||||
"\n", |
||||
"You may know this already - but if not, you will get very familiar with it!\n", |
||||
"\n", |
||||
"Models like GPT4o have been trained to receive instructions in a particular way.\n", |
||||
"\n", |
||||
"They expect to receive:\n", |
||||
"\n", |
||||
"**A system prompt** that tells them what task they are performing and what tone they should use\n", |
||||
"\n", |
||||
"**A user prompt** -- the conversation starter that they should reply to" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "abdb8417-c5dc-44bc-9bee-2e059d162699", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||
"\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||
"\n", |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||
"please provide a short summary of this website in markdown. \\\n", |
||||
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "26448ec4-5c00-4204-baec-7df91d11ff2e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(user_prompt_for(ed))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Messages\n", |
||||
"\n", |
||||
"The API from OpenAI expects to receive messages in a particular structure.\n", |
||||
"Many of the other APIs share this structure:\n", |
||||
"\n", |
||||
"```\n", |
||||
"[\n", |
||||
" {\"role\": \"system\", \"content\": \"system message goes here\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"user message goes here\"}\n", |
||||
"]\n", |
||||
"\n", |
||||
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the might GPT (yet!)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n", |
||||
"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "21ed95c5-7001-47de-a36d-1d6673b403ce", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# To give you a preview -- calling OpenAI with system and user messages:\n", |
||||
"\n", |
||||
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## And now let's build useful messages for GPT-4o-mini, using a function" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0134dfa4-8299-48b5-b444-f2a8c3403c88", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# See how this function creates exactly the format above\n", |
||||
"\n", |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "36478464-39ee-485c-9f3f-6a4e458dbc9c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Try this out, and then try for a few more websites\n", |
||||
"\n", |
||||
"messages_for(ed)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Time to bring it together - the API for OpenAI is very simple!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "905b9919-aba7-45b5-ae65-81b3d1d78e34", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# And now: call the OpenAI API. You will get very familiar with this!\n", |
||||
"\n", |
||||
"def summarize(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model = \"gpt-4o-mini\",\n", |
||||
" messages = messages_for(website)\n", |
||||
" )\n", |
||||
" return response.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"summary = summarize(\"https://edwarddonner.com\")\n", |
||||
"print(summary)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3d926d59-450e-4609-92ba-2d6f244f1342", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||
"\n", |
||||
"def display_summary(url):\n", |
||||
" summary = summarize(url)\n", |
||||
" display(Markdown(summary))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3018853a-445f-41ff-9560-d925d1774b2f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display_summary(\"https://edwarddonner.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Let's try more websites\n", |
||||
"\n", |
||||
"Note that this will only work on websites that can be scraped using this simplistic approach.\n", |
||||
"\n", |
||||
"Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n", |
||||
"\n", |
||||
"Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n", |
||||
"\n", |
||||
"But many websites will work just fine!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "45d83403-a24c-44b5-84ac-961449b4008f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"urls = ['https://be-able.info/de/be-able/', \"https://taz.de/\", \"https://www.bundestagswahl-bw.de/wahlprogramm-gruene\"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "75e9fd40-b354-4341-991e-863ef2e59db7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display_summary(urls[0])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "c951be1a-7f1b-448f-af1f-845978e47e2c", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#181;\">Business applications</h2>\n", |
||||
" <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n", |
||||
"\n", |
||||
"More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>\n", |
||||
"\n", |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n", |
||||
" <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "856ff857-ba5f-4596-90b9-cd6cee4073dc", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Naive extraction of name of the political party from user input\n", |
||||
"\n", |
||||
"party_mapping = {\"grünen\": \"grüne\", \"grüne\": \"grüne\", \"linken\": \"linke\", \"spd\": \"spd\", \"cdu\": \"cdu\", \"cdu/csu\": \"cdu\", \"csu\": \"cdu\", \"fdp\": \"fdp\", \"afd\": \"afd\", \"bsw\": \"bsw\"}\n", |
||||
"\n", |
||||
"def extract_party_from_user_prompt(user_input):\n", |
||||
" toks = user_input.split()\n", |
||||
" for tok in toks:\n", |
||||
" tok = tok.lower()\n", |
||||
" if tok in party_mapping.keys():\n", |
||||
" return party_mapping[tok]\n", |
||||
" return \"I can only answer your question concerning the election program of a certain political party. Mention one of 'FDP', 'BSW', 'Grüne', 'Linke', 'SPD', 'CDU' or 'AFD' in your question and I will try my best.\"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3962d846-ce82-47d2-8c3f-5a6fe296710d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"from selenium import webdriver\n", |
||||
"from selenium.webdriver.common.by import By\n", |
||||
"\n", |
||||
"def get_election_program(partyname):\n", |
||||
" \"\"\"Scrape parties' election programs from the official election website. Naively ignore cookie banner stuff.\"\"\"\n", |
||||
"\n", |
||||
" # Download the browser driver for your OS and add the path here\n", |
||||
" browser_driver_path = r'C:\\Program Files\\BrowserDrivers\\geckodriver.exe'\n", |
||||
" \n", |
||||
" service = webdriver.firefox.service.Service(executable_path=browser_driver_path)\n", |
||||
" \n", |
||||
" parties = {\"grüne\": \"https://www.bundestagswahl-bw.de/wahlprogramm-gruene\",\n", |
||||
" \"spd\": \"https://www.bundestagswahl-bw.de/wahlprogramm-spd\",\n", |
||||
" \"cdu\": \"https://www.bundestagswahl-bw.de/wahlprogramm-cdu\",\n", |
||||
" \"linke\": \"https://www.bundestagswahl-bw.de/wahlprogramm-die-linke\",\n", |
||||
" \"fdp\": \"https://www.bundestagswahl-bw.de/wahlprogramm-fdp\",\n", |
||||
" \"afd\": \"https://www.bundestagswahl-bw.de/wahlprogramm-afd\",\n", |
||||
" \"bsw\": \"https://www.bundestagswahl-bw.de/wahlprogramm-bsw\"}\n", |
||||
" \n", |
||||
" election_prog = \"\"\n", |
||||
" \n", |
||||
" if partyname in parties.keys():\n", |
||||
" site = parties[partyname]\n", |
||||
" driver = webdriver.Firefox(service=service)\n", |
||||
" driver.get(site)\n", |
||||
" elements = driver.find_elements(By.TAG_NAME, 'p')\n", |
||||
" \n", |
||||
" for e in elements:\n", |
||||
" if not any(x in [\"Cookies\", \"Cookie\", \"akzeptiere\", \"Datenschutzerklärung\", \"Impressum\"] for x in e.text.split()) and e.text:\n", |
||||
" election_prog += e.text\n", |
||||
" if len(election_prog.split()) > 100:\n", |
||||
" print(\"Election program extracted.\")\n", |
||||
"\n", |
||||
" else:\n", |
||||
" election_prog = f\"Schade, für die Partei {partyname} konnte ich leider kein Wahlprogramm finden.\"\n", |
||||
" \n", |
||||
" driver.quit()\n", |
||||
" return election_prog" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b3a408d1-d824-4e33-a5f4-c672bc6c6198", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"\n", |
||||
"def answer_my_election_program_question(input_from_user):\n", |
||||
" partyname = extract_party_from_user_prompt(input_from_user)\n", |
||||
" print(f\"This is a question about the political party: {partyname.capitalize()}\")\n", |
||||
" \n", |
||||
" # Step 1: Create your prompts\n", |
||||
" system_prompt = \"Du bist ein neutraler Beobachter, der aufgrund der ihm zur Verfügung gestellten Wahlprogramme Fragen zum Wahlprogramm der verschiedenen Parteien beantwortet. Beantworte Fragen zum Wahlprogramm auf Deutsch. Basiere deine Antwort ausschließlich auf den im Folgenden aufgeführten Informationen.\"\n", |
||||
" election_program = get_election_program(partyname)\n", |
||||
" \n", |
||||
" user_prompt = f\"Beantworte folgende Frage: \\n {input_from_user} \\n Verwende dafür folgende Infos: \\n {election_program}.\\n\\n Gib deine Antwort in Markdown aus.\"\n", |
||||
" \n", |
||||
" # Step 2: Make the messages list\n", |
||||
" \n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": user_prompt}] # fill this in\n", |
||||
" \n", |
||||
" # Step 3: Call OpenAI\n", |
||||
" \n", |
||||
" response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n", |
||||
" formatted_response = f\"\\n\\n{response.choices[0].message.content}\"\n", |
||||
" # Step 4: print the result\n", |
||||
" return formatted_response" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e66a0967-d1e9-4f92-aeb6-95e478465a1f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Ask questions about the election programs of the main political parties for the Bundestagswahl 2025 in Germany\n", |
||||
"\n", |
||||
"question = \"Wie verhält sich die SPD zu Verkehrsfragen und Klimaschutz?\"\n", |
||||
"answer = answer_my_election_program_question(question)\n", |
||||
"display(Markdown(answer))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "36ed9f14-b349-40e9-a42c-b367e77f8bda", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## An extra exercise for those who enjoy web scraping\n", |
||||
"\n", |
||||
"You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "eeab24dc-5f90-4570-b542-b0585aca3eb6", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Sharing your code\n", |
||||
"\n", |
||||
"I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n", |
||||
"\n", |
||||
"If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n", |
||||
"\n", |
||||
"Here are good instructions courtesy of an AI friend: \n", |
||||
"https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,127 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0ee39d65-f27d-416d-8b46-43d15aebe752", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Below is a sample for email reviewer using Bahasa Indonesia. " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f9fd62af-9b14-490b-8d0b-990da96101bf", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Step 1: Create your prompts\n", |
||||
"\n", |
||||
"system_prompt = \"Anda adalah seorang Asisten untuk menganalisa email berdasarkan user prompt yang nanti akan diberikan. Summarize the email and give me a tone about that email\"\n", |
||||
"user_prompt = \"\"\"\n", |
||||
" Subject: Permintaan Pertemuan\n", |
||||
"\n", |
||||
"Yang terhormat Bapak Rijal,\n", |
||||
"\n", |
||||
"Saya ingin meminta waktu Anda untuk membahas Generative AI untuk bisnis. Apakah Anda tersedia pada besok pukul 19:00? \n", |
||||
"Jika tidak, mohon beri tahu waktu yang lebih sesuai bagi Anda.\n", |
||||
"\n", |
||||
"Terima kasih atas perhatian Anda.\n", |
||||
"\n", |
||||
"Salam,\n", |
||||
"\n", |
||||
"Mentari\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"# Step 2: Make the messages list\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
" ] # fill this in\n", |
||||
"\n", |
||||
"# Step 3: Call OpenAI\n", |
||||
"\n", |
||||
"response = openai.chat.completions.create(\n", |
||||
" model = \"gpt-4o-mini\",\n", |
||||
" messages = messages\n", |
||||
" )\n", |
||||
"\n", |
||||
"# Step 4: print the result\n", |
||||
"\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d10208fa-02d8-41a0-b9bb-0bf30f237f25", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Step 1: Create your prompts\n", |
||||
"\n", |
||||
"system_prompt = \"Anda adalah seorang Asisten untuk menganalisa email berdasarkan user prompt yang nanti akan diberikan. Summarize the email and give me a tone about that email\"\n", |
||||
"user_prompt = \"\"\"\n", |
||||
" Subject: Feedback terkait Bapak\n", |
||||
"\n", |
||||
"Yang terhormat Bapak Rijal,\n", |
||||
"\n", |
||||
"Saya ingin memberikan sedikit feedback untuk BBapak.\n", |
||||
"\n", |
||||
"Kemampuan Anda dalam memimpin tim ini mampu membawa saya dan rekan lainnya untuk mengerahkan semua kemampuan saya agar jadi lebih baik.\n", |
||||
"Selama ini saya cukup senang bekerja dengan Anda karena memberikan saya peluang untuk mencoba banyak hal baru. Tapi ada beberapa kekhawatiran yang mau saya sampaikan, terutama terkait target yang perlu dicapai oleh tim. Saya pikir melihat performa ke belakang, target yang ditentukan harus lebih realistis lagi.\n", |
||||
"Saya beruntung bisa berkesempatan bekerja dengan Anda sehingga banyak ilmu yang saya dapat. Kira-kira untuk ke depannya, hal apa lagi yang bisa tim ini tingkatkan agar kita bisa mencapai target yang lebih baik?\n", |
||||
"Selama ini, banyak terjadi miskomunikasi dalam pekerjaan. Dan menurut saya salah satunya karena arahan yang Anda berikan kurang jelas dan kurang ditangkap sepenuhnya oleh anggota yang lain. Saya dan tim berharap ke depan bisa mendapatkan arahan yang lebih jelas dan satu arah.\n", |
||||
"\n", |
||||
"Terima kasih atas perhatian Anda.\n", |
||||
"\n", |
||||
"Salam,\n", |
||||
"\n", |
||||
"Mentari\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"# Step 2: Make the messages list\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
" ] # fill this in\n", |
||||
"\n", |
||||
"# Step 3: Call OpenAI\n", |
||||
"\n", |
||||
"response = openai.chat.completions.create(\n", |
||||
" model = \"gpt-4o-mini\",\n", |
||||
" messages = messages\n", |
||||
" )\n", |
||||
"\n", |
||||
"# Step 4: print the result\n", |
||||
"\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,233 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1b8f7ac7-7089-427a-8f63-57211da7e691", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Summarizing Research Papers" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "641d5c00-ff09-4697-9c87-5de5df1469f8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from openai import OpenAI\n", |
||||
"\n", |
||||
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1a6a2864-fd9d-43e2-b0ca-1476c0153077", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"# Check the key\n", |
||||
"\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "340e3166-5aa7-4bcf-9cf0-e2fc776dc322", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "73198fb7-581f-42ac-99a6-76c56c86248d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||
"\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Paper:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3b39c3ad-d238-418e-9e6a-55a4fd717ebc", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#Insert Paper URL\n", |
||||
"res = Paper(\" \")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "83bc1eec-4187-4c6c-b188-3f72564351f1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt = \"\"\"You are a research paper summarizer. You take the url of the research paper and extract the following:\n", |
||||
"1) Title and Author of the research paper.\n", |
||||
"2) Year it was published it\n", |
||||
"3) Objective or aim of the research to specify why the research was conducted\n", |
||||
"4) Background or Introduction to explain the need to conduct this research or any topics the readers must have knowledge about\n", |
||||
"5) Type of research/study/experiment to explain what kind of research it is.\n", |
||||
"6) Methods or methodology to explain what the researchers did to conduct the research\n", |
||||
"7) Results and key findings to explain what the researchers found\n", |
||||
"8) Conclusion tells about the conclusions that can be drawn from this research including limitations and future direction\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4aba1b51-9a72-4325-8c86-3968b9d3172e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||
"\n", |
||||
"def user_prompt_for(paper):\n", |
||||
" user_prompt = f\"You are looking at a website titled {paper.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this paper is as follows; \\\n", |
||||
"please provide a short summary of this paper in markdown. \\\n", |
||||
"If it includes additional headings, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += paper.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "659cb3c4-8a02-493d-abe7-20da9219e358", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# See how this function creates exactly the format above\n", |
||||
"def messages_for(paper):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(paper)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "08ea1193-1bbb-40de-ba64-d02ffe109372", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"messages_for(res)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e07d00e7-1b87-4ca8-a69d-4a206e34a2b2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# And now: call the OpenAI API. You will get very familiar with this!\n", |
||||
"\n", |
||||
"def summarize(url):\n", |
||||
" paper = Paper(url)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model = \"gpt-4o-mini\",\n", |
||||
" messages = messages_for(paper)\n", |
||||
" )\n", |
||||
" return response.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "5c12df95-1700-47ee-891b-96b0a7227bdd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||
"\n", |
||||
"def display_summary(url):\n", |
||||
" summary = summarize(url)\n", |
||||
" display(Markdown(summary))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "05cff05f-2b74-44a4-9dbd-57c08f8f56cb", |
||||
"metadata": { |
||||
"scrolled": true |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Insert Paper URL in the quotes below\n", |
||||
"display_summary(\" \")" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,224 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from openai import OpenAI\n", |
||||
"\n", |
||||
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6900b2a8-6384-4316-8aaa-5e519fca4254", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Connecting to OpenAI\n", |
||||
"\n", |
||||
"The next cell is where we load in the environment variables in your `.env` file and connect to OpenAI.\n", |
||||
"\n", |
||||
"## Troubleshooting if you have problems:\n", |
||||
"\n", |
||||
"Head over to the [troubleshooting](troubleshooting.ipynb) notebook in this folder for step by step code to identify the root cause and fix it!\n", |
||||
"\n", |
||||
"If you make a change, try restarting the \"Kernel\" (the python process sitting behind this notebook) by Kernel menu >> Restart Kernel and Clear Outputs of All Cells. Then try this notebook again, starting at the top.\n", |
||||
"\n", |
||||
"Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n", |
||||
"\n", |
||||
"Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7b87cadb-d513-4303-baee-a37b6f938e4d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"\n", |
||||
"load_dotenv()\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"# Check the key\n", |
||||
"\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"openai = OpenAI()\n", |
||||
"\n", |
||||
"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n", |
||||
"# If it STILL doesn't work (horrors!) then please see the troubleshooting notebook, or try the below line instead:\n", |
||||
"# openai = OpenAI(api_key=\"your-key-here-starting-sk-proj-\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "abdb8417-c5dc-44bc-9bee-2e059d162699", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||
"\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||
"\n", |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||
"please provide a short summary of this website in markdown. \\\n", |
||||
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "36ed9f14-b349-40e9-a42c-b367e77f8bda", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## An extra exercise for those who enjoy web scraping\n", |
||||
"\n", |
||||
"You may notice that if you try the course example with \"https://openai.com\" - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. Below an example created with Playwright." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "dca2768e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"! pip install playwright\n", |
||||
"! playwright install" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "682eff74-55c4-4d4b-b267-703edbc293c7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import asyncio\n", |
||||
"from playwright.async_api import async_playwright\n", |
||||
"import nest_asyncio\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"import time\n", |
||||
"\n", |
||||
"nest_asyncio.apply()\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
" title: str\n", |
||||
" text: str\n", |
||||
" url: str\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" \n", |
||||
" async def run(self, playwright):\n", |
||||
" browser = await playwright.chromium.launch(headless=False)\n", |
||||
" page = await browser.new_page()\n", |
||||
" await page.goto(self.url)\n", |
||||
" await page.wait_for_load_state('load')\n", |
||||
" \n", |
||||
" # Extract data from the page\n", |
||||
" self.title = await page.title()\n", |
||||
" text = await page.content()\n", |
||||
" await browser.close()\n", |
||||
" \n", |
||||
" soup = BeautifulSoup(text, 'html.parser')\n", |
||||
" for irrelevant in soup([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.get_text(separator=\"\\n\", strip=True)\n", |
||||
" \n", |
||||
" async def main(self):\n", |
||||
" async with async_playwright() as playwright:\n", |
||||
" await self.run(playwright) \n", |
||||
" \n", |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ]\n", |
||||
"\n", |
||||
"if __name__ == \"__main__\":\n", |
||||
" site = Website('https://openai.com')\n", |
||||
" asyncio.run(site.main())\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model = \"gpt-4o-mini\",\n", |
||||
" messages = messages_for(site)\n", |
||||
" )\n", |
||||
"\n", |
||||
" web_summary = response.choices[0].message.content\n", |
||||
" display(Markdown(web_summary))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "69218dec-749c-412d-84a0-40a10fd80c73", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,115 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "44aba2a0-c6eb-4fc1-a5cc-0a8f8679dbb8", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Far Far Away..." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d4d58124-5e9a-4f5a-9e0a-ff74f43896a8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from openai import OpenAI\n", |
||||
"\n", |
||||
"# Load environment variables in a file called .env\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "33179b68-7ed5-46ab-b583-d67ed57cd39d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def add_user_greeting(greeting):\n", |
||||
" user_prompt = \"\"\"\n", |
||||
" The following is the greeting from the user. Please respond in character as a barman in the Mos Eisley Cantina.\\n\\n\n", |
||||
" \"\"\"\n", |
||||
" user_prompt += greeting\n", |
||||
"\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "67dc3099-2ccc-4ee8-8ff2-0dbbe4ae2fcb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def approach_the_bar(greeting):\n", |
||||
"\n", |
||||
" system_prompt = \"You are a barman in the Mos Eisley Cantina from the Star Wars universe.\\\n", |
||||
"It is a Tuesday evening, the year is 3BBY, and the Cantina is quiet except for a few lonely regulars.\\\n", |
||||
"The barman (you) is slightly skeptical but eager to share some interesting news regarding some nearby imperial activity.\\\n", |
||||
"You will recieve a greeting from the user, you must respond and provide them with some gossip detailing \\\n", |
||||
"some local shady dealings occuring in Mos Eisley. Please format your response using markdown to provide a sense of the conversation.\"\n", |
||||
"\n", |
||||
" user_prompt = add_user_greeting(greeting)\n", |
||||
" \n", |
||||
" messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt},\n", |
||||
" ]\n", |
||||
" \n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model = \"gpt-4o-mini\",\n", |
||||
" messages = messages\n", |
||||
" )\n", |
||||
" \n", |
||||
" # Step 4: print the result in markdown format\n", |
||||
" pretty_response = Markdown(response.choices[0].message.content)\n", |
||||
" display(pretty_response)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fb47e2b7-5509-4d1a-8e71-ff103fc8a885", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"approach_the_bar(\"\")" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,223 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "bfa3abd0-4e66-4117-96f9-7a71fbb6d0cb", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Powerpoint Slides Summarizer\n", |
||||
"\n", |
||||
"This converts a Power Point presentation into notes that a student can easily skim through.\n", |
||||
"\n", |
||||
"Concepts Used:\n", |
||||
"- Converting Contents of PPT to text via python-pptx\n", |
||||
"- User and System Prompts\n", |
||||
"- Use of Open AI GPT-4o-mini via API key\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ab95eb49-6a2d-4c7d-9057-78a2cd9364cc", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!pip install python-pptx" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "62715f16-7125-455e-98e7-5705871c0e4a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from openai import OpenAI" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ff42eab7-789d-44f8-a5cc-64baeebf3224", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"# Check the key\n", |
||||
"\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bce425c2-6d19-4c03-93ce-8930dabc61ee", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# creating an instance\n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c0c75e30-3b38-4a89-b7d3-a41a6f5dc650", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"from pptx import Presentation\n", |
||||
"\n", |
||||
"class PowerPoint():\n", |
||||
" def __init__(self,ppt):\n", |
||||
" \"\"\"\n", |
||||
" Creates a PowerPoint object, with name and text.\n", |
||||
" \"\"\"\n", |
||||
" self.ppt = ppt\n", |
||||
" self.title = os.path.basename(ppt)\n", |
||||
" self.text = self.extract_text()\n", |
||||
"\n", |
||||
" def extract_text(self):\n", |
||||
" \"\"\"\n", |
||||
" Extracts text from powerpoint.\n", |
||||
" \"\"\"\n", |
||||
" prs = Presentation(self.ppt)\n", |
||||
" text_content = []\n", |
||||
" \n", |
||||
" for slide in prs.slides:\n", |
||||
" for shape in slide.shapes:\n", |
||||
" if hasattr(shape, \"text\"):\n", |
||||
" text_content.append(shape.text)\n", |
||||
" \n", |
||||
" return \"\\n\".join(text_content)\n", |
||||
" " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1963a055-87f4-4e47-8456-cac4d4ac57fc", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt = \"You are an assistant that analyzes the contents \\\n", |
||||
"of a PowerPoint presentation, and provides a summary in the style of \\\n", |
||||
"a cheat-sheet, for students to easily learn key concepts from.\\\n", |
||||
"You are to ignore text that might be navigation-related\\\n", |
||||
"and respond in Markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ca600e90-7d3f-4fc7-a698-1b8f2925f81e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function that writes a User Prompt that asks for summaries of PowerPoints:\n", |
||||
"\n", |
||||
"def user_prompt_for(powerpoint):\n", |
||||
" user_prompt = f\"You are looking at a website titled {powerpoint.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this powerpoint are as follows; \\\n", |
||||
"please provide a summary of the content in markdown. \\\n", |
||||
"If it includes a question bank, add that along with short answers too.\\n\\n\"\n", |
||||
" user_prompt += powerpoint.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4fe19c56-9940-4528-b43a-c86798b215d2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def messages_for(powerpoint):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(powerpoint)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f7704da5-90b0-40af-bbb4-7d589309f180", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# And now: call the OpenAI API. \n", |
||||
"\n", |
||||
"def summarize(powerpoint_path):\n", |
||||
" powerpoint = PowerPoint(powerpoint_path)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model = \"gpt-4o-mini\",\n", |
||||
" messages = messages_for(powerpoint)\n", |
||||
" )\n", |
||||
" return response.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "49d1d0cf-fa4b-4bea-bd68-a834145070ef", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def display_summary(url):\n", |
||||
" summary = summarize(url)\n", |
||||
" display(Markdown(summary))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "348078d1-e86f-4eb3-909d-33ab4ede984e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"ppt_file = \"Theoretical Perspectives on Media and Technology.pptx\" \n", |
||||
"display_summary(ppt_file)" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,170 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 8, |
||||
"id": "6ba7c60a-c338-49a1-b1ba-46b7c20e33cb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import openai\n", |
||||
"import os\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"from IPython.display import Markdown, display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 2, |
||||
"id": "4acb4062-17b2-43b1-8b74-aefaa9599463", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"API key found and looks good so far!\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"# Check the key\n", |
||||
"\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 5, |
||||
"id": "56f011b2-b759-4ad6-9d01-870fbcb8ade1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def generate_quiz(topic):\n", |
||||
" prompt = f\"Generate a multiple-choice quiz with 5 questions on the topic: {topic}. Include the correct answer for each question.\"\n", |
||||
" \n", |
||||
" messages = [\n", |
||||
" {\"role\": \"system\", \"content\": \"You are a quiz generator. Create a multiple-choice quiz with 5 questions and provide the correct answers.Respond in markdown.\"},\n", |
||||
" {\"role\": \"user\", \"content\": prompt}\n", |
||||
" ]\n", |
||||
" \n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=\"gpt-4\",\n", |
||||
" messages=messages,\n", |
||||
" max_tokens=300\n", |
||||
" )\n", |
||||
" \n", |
||||
" return response.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 10, |
||||
"id": "1cf977e7-b04b-49e7-8b0a-d0ab2800c234", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"data": { |
||||
"text/markdown": [ |
||||
"**Question 1:** What is Python?\n", |
||||
"\n", |
||||
"**Choice A:** A type of snake\n", |
||||
"**Choice B:** A medical term\n", |
||||
"**Choice C:** A drilling tool\n", |
||||
"**Choice D:** A high-level programming language\n", |
||||
"\n", |
||||
"Correct Answer: **Choice D:** A high-level programming language\n", |
||||
"\n", |
||||
"**Question 2:** In Python, what keyword is used to create a function?\n", |
||||
"\n", |
||||
"**Choice A:** func\n", |
||||
"**Choice B:** def\n", |
||||
"**Choice C:** function\n", |
||||
"**Choice D:** create\n", |
||||
"\n", |
||||
"Correct Answer: **Choice B:** def\n", |
||||
"\n", |
||||
"**Question 3:** What is the correct syntax to output \"Hello World\" in Python?\n", |
||||
"\n", |
||||
"**Choice A:** printf(\"Hello World\")\n", |
||||
"**Choice B:** println(\"Hello World\")\n", |
||||
"**Choice C:** echo(\"Hello World\")\n", |
||||
"**Choice D:** print(\"Hello World\")\n", |
||||
"\n", |
||||
"Correct Answer: **Choice D:** print(\"Hello World\")\n", |
||||
"\n", |
||||
"**Question 4:** How would you create a variable \"x\" that equals 5 in Python?\n", |
||||
"\n", |
||||
"**Choice A:** var x = 5\n", |
||||
"**Choice B:** x := 5\n", |
||||
"**Choice C:** x = 5\n", |
||||
"**Choice D:** x : 5\n", |
||||
"\n", |
||||
"Correct Answer: **Choice C:** x = 5\n", |
||||
"\n", |
||||
"**Question 5:** How do you create a comment in Python?\n", |
||||
"\n", |
||||
"**Choice A:** // This is a comment\n", |
||||
"**Choice B:** # This is a comment\n", |
||||
"**Choice C:** <!-- This is a comment -->\n", |
||||
"**Choice D:** /* This is a comment */\n", |
||||
"\n", |
||||
"Correct Answer" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.Markdown object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
} |
||||
], |
||||
"source": [ |
||||
"# Example usage\n", |
||||
"topic = \"Python programming\"\n", |
||||
"quiz = generate_quiz(topic)\n", |
||||
"display(Markdown(quiz))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "70990d7c-6061-43c6-b3c9-9146a3c51c3e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,230 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "56c86bae-1d3c-4c01-b5d6-c8879fec1954", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Wiki Summarizer\n", |
||||
"\n", |
||||
"This Project takes the name of a topic as input, and checks if the corresponding wiki-page exists. If it does, it parses the web page, and outputs a summary created using the GPT-4o-mini model. \n", |
||||
"\n", |
||||
"Concepts used: \n", |
||||
"- Web Scraping via Beautiful Soup\n", |
||||
"- User and System Prompts\n", |
||||
"- Use of Open AI GPT-4o-mini via API key" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4820830e-b3b4-426e-b1a2-518e7c7f6c1a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from openai import OpenAI" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2cd7ad51-396c-45c5-9089-f7b21a19da50", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"# Check the key\n", |
||||
"\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "689421a0-20a1-428b-a8b8-fa239fa6f633", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# creating an instance\n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "401901ae-7639-4190-98fd-e69374084723", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def isWiki(url):\n", |
||||
" \"\"\"\n", |
||||
" Check whether a Wikipedia page exists for a given topic, and \n", |
||||
" returns a Boolean value.\n", |
||||
" \"\"\"\n", |
||||
" response = requests.get(url)\n", |
||||
"\n", |
||||
" if response.status_code != 200:\n", |
||||
" return False\n", |
||||
" \n", |
||||
" return True" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7cdb14d3-05ea-4de2-a475-d49a5731692e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7f6ed50e-0fb5-479e-9845-f62cf25980f7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt = \"You are an educational assistant tasked with helping users understand topics\\\n", |
||||
"by providing succinct and clear summaries of requested data. Ignore navigation-related text\\\n", |
||||
"and provide answers in markdown format\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b2d77dd9-a94f-49c1-a1be-11d157bd37fb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function that writes a User Prompt that asks for summaries of wiki pages:\n", |
||||
"\n", |
||||
"def user_prompt_for(wiki):\n", |
||||
" user_prompt = f\"You are looking at a Wikipedia page titled {wiki.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this page is as follows; \\\n", |
||||
"please provide a short summary of this website in markdown.\\n\"\n", |
||||
" user_prompt += wiki.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0d23bcc4-1d89-4bd4-9809-d3a1819aa919", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def messages_for(wiki):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(wiki)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "971bd7fb-2ff8-4494-b386-de69a39c24ff", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def summarize(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model = \"gpt-4o-mini\",\n", |
||||
" messages = messages_for(website)\n", |
||||
" )\n", |
||||
" return response.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a8fdf9f2-f49e-4d06-ac9e-dfcb8da33d60", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def display_summary(topic):\n", |
||||
" url = f\"https://en.wikipedia.org/wiki/{topic}\"\n", |
||||
" if isWiki(url):\n", |
||||
" summary = summarize(url)\n", |
||||
" display(Markdown(summary))\n", |
||||
" else:\n", |
||||
" print('A Wikipedia page does not exist for this topic')\n", |
||||
" " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f4758ef0-9b7c-4d3e-9131-e3284dc76b6b", |
||||
"metadata": { |
||||
"scrolled": true |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"topic = input('Enter the name of Wikipedia page for which you would like a summary: ').strip()\n", |
||||
"display_summary(topic)" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,192 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "e3ce0a59-fbfb-4377-85db-f62f95039200", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Day2 EXERCISE - Summarization using Ollama" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "29ddd15d-a3c5-4f4e-a678-873f56162724", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Constants\n", |
||||
"\n", |
||||
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||
"MODEL = \"llama3.2\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "cb5c0f84-4e4d-4f87-b492-e09d0333a638", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||
"\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "23457b52-c85b-4dc1-b946-6f1461dc0675", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"\n", |
||||
"ed = Website(\"https://edwarddonner.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bed206ed-43c1-4f68-ad01-a738b3b4648d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||
"\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e558f381-614a-461f-83bc-e5bdc99460df", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||
"\n", |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||
"please provide a short summary of this website in markdown. \\\n", |
||||
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e5ba638d-aeb9-441e-a62a-8e8027ad8439", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# See how this function creates exactly the format above\n", |
||||
"\n", |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e85ca2ec-3e46-4b8f-9c2f-66e7d20138fa", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#website search\n", |
||||
"\n", |
||||
"ed = Website(\"https://edwarddonner.com\")\n", |
||||
"messages=messages_for(ed)\n", |
||||
"\n", |
||||
"payload = {\n", |
||||
" \"model\": MODEL,\n", |
||||
" \"messages\": messages,\n", |
||||
" \"stream\": False\n", |
||||
" }" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7745b9c4-57dc-4867-9180-61fa5db55eb8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import ollama\n", |
||||
"\n", |
||||
"response = ollama.chat(model=MODEL, messages=messages)\n", |
||||
"print(response['message']['content'])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "402d5686-4e76-4110-b65a-b3906c35c0a4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,354 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Welcome to your first assignment!\n", |
||||
"\n", |
||||
"Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n", |
||||
" <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n", |
||||
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n", |
||||
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# HOMEWORK EXERCISE ASSIGNMENT\n", |
||||
"\n", |
||||
"Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n", |
||||
"\n", |
||||
"You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n", |
||||
"\n", |
||||
"**Benefits:**\n", |
||||
"1. No API charges - open-source\n", |
||||
"2. Data doesn't leave your box\n", |
||||
"\n", |
||||
"**Disadvantages:**\n", |
||||
"1. Significantly less power than Frontier Model\n", |
||||
"\n", |
||||
"## Recap on installation of Ollama\n", |
||||
"\n", |
||||
"Simply visit [ollama.com](https://ollama.com) and install!\n", |
||||
"\n", |
||||
"Once complete, the ollama server should already be running locally. \n", |
||||
"If you visit: \n", |
||||
"[http://localhost:11434/](http://localhost:11434/)\n", |
||||
"\n", |
||||
"You should see the message `Ollama is running`. \n", |
||||
"\n", |
||||
"If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n", |
||||
"And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n", |
||||
"Then try [http://localhost:11434/](http://localhost:11434/) again.\n", |
||||
"\n", |
||||
"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "29ddd15d-a3c5-4f4e-a678-873f56162724", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Constants\n", |
||||
"\n", |
||||
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||
"MODEL = \"llama3.2\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "dac0a679-599c-441f-9bf2-ddc73d35b940", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Create a messages list using the same format that we used for OpenAI\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n", |
||||
"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7bb9c624-14f0-4945-a719-8ddb64f66f47", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"payload = {\n", |
||||
" \"model\": MODEL,\n", |
||||
" \"messages\": messages,\n", |
||||
" \"stream\": False\n", |
||||
" }" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "479ff514-e8bd-4985-a572-2ea28bb4fa40", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's just make sure the model is loaded\n", |
||||
"\n", |
||||
"!ollama pull llama3.2" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "42b9f644-522d-4e05-a691-56e7658c0ea9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# If this doesn't work for any reason, try the 2 versions in the following cells\n", |
||||
"# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n", |
||||
"# And if none of that works - contact me!\n", |
||||
"\n", |
||||
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
||||
"print(response.json()['message']['content'])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Introducing the ollama package\n", |
||||
"\n", |
||||
"And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n", |
||||
"\n", |
||||
"Under the hood, it's making the same call as above to the ollama server running at localhost:11434" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7745b9c4-57dc-4867-9180-61fa5db55eb8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import ollama\n", |
||||
"\n", |
||||
"response = ollama.chat(model=MODEL, messages=messages)\n", |
||||
"print(response['message']['content'])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "a4704e10-f5fb-4c15-a935-f046c06fb13d", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Alternative approach - using OpenAI python library to connect to Ollama" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "23057e00-b6fc-4678-93a9-6b31cb704bff", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# There's actually an alternative approach that some people might prefer\n", |
||||
"# You can use the OpenAI client python library to call Ollama:\n", |
||||
"\n", |
||||
"from openai import OpenAI\n", |
||||
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", |
||||
"\n", |
||||
"response = ollama_via_openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=messages\n", |
||||
")\n", |
||||
"\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# NOW the exercise for you\n", |
||||
"\n", |
||||
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ef76cfc2-c519-4cb2-947a-64948517913d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a151a8de-1e90-4190-b68e-b44b25a2cdd7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Constants\n", |
||||
"\n", |
||||
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||
"MODEL = \"llama3.2\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "700fffc1-c7b0-4001-b381-5c4fd28c8799", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Reusing the Website BeautifulSoup wrapper from Day 1\n", |
||||
"# SSL Verification has been disabled\n", |
||||
"\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers, verify=False) # NOTE Disabled ssl verification here to workaround VPN Limitations\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "402d5686-4e76-4110-b65a-b3906c35c0a4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website are as follows; \\\n", |
||||
"please provide a short summary of this website in markdown. \\\n", |
||||
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "81f5f140-8f77-418f-a252-8ad5d11f6c5f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"## enter the web URL here:\n", |
||||
"website_url = \"https://www.timecube.net/\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1d0ce4aa-b43e-4642-bcbd-d5964700ece8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"## This will at first print a warning for SSL which can be ignored before providing response. \n", |
||||
"\n", |
||||
"import ollama\n", |
||||
"\n", |
||||
"system_prompt = \"You are a virtual assistant who analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(Website(website_url))}\n", |
||||
"]\n", |
||||
"\n", |
||||
"response = ollama.chat(model=MODEL, messages=messages)\n", |
||||
"print(response['message']['content'])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "910b7e06-c92d-47bf-a4ee-a006d70deb06", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,511 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Welcome to your first assignment!\n", |
||||
"\n", |
||||
"Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n", |
||||
" <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n", |
||||
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n", |
||||
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# HOMEWORK EXERCISE ASSIGNMENT\n", |
||||
"\n", |
||||
"Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n", |
||||
"\n", |
||||
"You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n", |
||||
"\n", |
||||
"**Benefits:**\n", |
||||
"1. No API charges - open-source\n", |
||||
"2. Data doesn't leave your box\n", |
||||
"\n", |
||||
"**Disadvantages:**\n", |
||||
"1. Significantly less power than Frontier Model\n", |
||||
"\n", |
||||
"## Recap on installation of Ollama\n", |
||||
"\n", |
||||
"Simply visit [ollama.com](https://ollama.com) and install!\n", |
||||
"\n", |
||||
"Once complete, the ollama server should already be running locally. \n", |
||||
"If you visit: \n", |
||||
"[http://localhost:11434/](http://localhost:11434/)\n", |
||||
"\n", |
||||
"You should see the message `Ollama is running`. \n", |
||||
"\n", |
||||
"If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n", |
||||
"And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n", |
||||
"Then try [http://localhost:11434/](http://localhost:11434/) again.\n", |
||||
"\n", |
||||
"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "29ddd15d-a3c5-4f4e-a678-873f56162724", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Constants\n", |
||||
"\n", |
||||
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||
"MODEL = \"llama3.2\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "dac0a679-599c-441f-9bf2-ddc73d35b940", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Create a messages list using the same format that we used for OpenAI\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n", |
||||
"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7bb9c624-14f0-4945-a719-8ddb64f66f47", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"payload = {\n", |
||||
" \"model\": MODEL,\n", |
||||
" \"messages\": messages,\n", |
||||
" \"stream\": False\n", |
||||
" }" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "479ff514-e8bd-4985-a572-2ea28bb4fa40", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's just make sure the model is loaded\n", |
||||
"\n", |
||||
"!ollama pull llama3.2" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "42b9f644-522d-4e05-a691-56e7658c0ea9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# If this doesn't work for any reason, try the 2 versions in the following cells\n", |
||||
"# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n", |
||||
"# And if none of that works - contact me!\n", |
||||
"\n", |
||||
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
||||
"print(response.json()['message']['content'])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Introducing the ollama package\n", |
||||
"\n", |
||||
"And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n", |
||||
"\n", |
||||
"Under the hood, it's making the same call as above to the ollama server running at localhost:11434" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7745b9c4-57dc-4867-9180-61fa5db55eb8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import ollama\n", |
||||
"\n", |
||||
"response = ollama.chat(model=MODEL, messages=messages)\n", |
||||
"print(response['message']['content'])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "a4704e10-f5fb-4c15-a935-f046c06fb13d", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Alternative approach - using OpenAI python library to connect to Ollama" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "23057e00-b6fc-4678-93a9-6b31cb704bff", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# There's actually an alternative approach that some people might prefer\n", |
||||
"# You can use the OpenAI client python library to call Ollama:\n", |
||||
"\n", |
||||
"from openai import OpenAI\n", |
||||
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", |
||||
"\n", |
||||
"response = ollama_via_openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=messages\n", |
||||
")\n", |
||||
"\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Also trying the amazing reasoning model DeepSeek\n", |
||||
"\n", |
||||
"Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n", |
||||
"This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n", |
||||
"\n", |
||||
"Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d", |
||||
"metadata": { |
||||
"collapsed": true, |
||||
"jupyter": { |
||||
"outputs_hidden": true |
||||
} |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!ollama pull deepseek-r1:1.5b" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1d3d554b-e00d-4c08-9300-45e073950a76", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside <think> tags, followed by some decent definitions\n", |
||||
"\n", |
||||
"response = ollama_via_openai.chat.completions.create(\n", |
||||
" model=\"deepseek-r1:1.5b\",\n", |
||||
" messages=[{\"role\": \"user\", \"content\": \"Please give definitions of some core concepts behind LLMs: a neural network, attention and the transformer\"}]\n", |
||||
")\n", |
||||
"\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# NOW the exercise for you\n", |
||||
"\n", |
||||
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "ffaa3470-884c-467e-b4ce-c1b8d39294da", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"This is the code from day 1 notebook. Here we create the class to extract the text from the website, using BeautifulSoup library, and the we execute it to see the the results" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8d8c9f01-ca12-4018-b7fa-698c9fa1aa93", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||
"\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6fd198df-bac5-42c5-83a0-06c5f71fb76a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's try one out. Change the website and add print statements to follow along.\n", |
||||
"\n", |
||||
"ed = Website(\"https://edwarddonner.com\")\n", |
||||
"print(ed.title)\n", |
||||
"print(ed.text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "995b637d-a5db-4ad9-ac78-5980fd7ef112", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"#### Define the system prompt, to instruct the model how we want to respond to our query. " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ee810d49-e88a-4137-a4be-98812e0d0748", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||
"\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "482b5d4c-69ed-4332-abb5-8b0986dcf368", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||
"\n", |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||
"please provide a short summary of this website in markdown. \\\n", |
||||
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d966cb09-3ca2-49f7-8462-f6ef26c01159", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(user_prompt_for(ed))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2f9be84f-4cd7-4ce7-8f33-e60d16f02852", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# For test purpose\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n", |
||||
"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f5cb0e9f-eb56-4633-ba4c-76817be98856", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# To give you a preview -- calling ollama with system and user messages:\n", |
||||
"\n", |
||||
"import ollama\n", |
||||
"\n", |
||||
"response = ollama.chat(model=MODEL, messages=messages)\n", |
||||
"print(response['message']['content'])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c554903f-eb04-4a16-87fc-f1d9ff58f6d9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# See how this function creates exactly the format above\n", |
||||
"\n", |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6b64b814-123f-436d-9366-4c762ac4b89a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Try this out, and then try for a few more websites\n", |
||||
"\n", |
||||
"messages_for(ed)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d1ef4be2-ef3a-4b5d-8d18-f2eafa9d6a93", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"### So, here let's run the summarize by using ollama and see how appears." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7c46edc5-c85d-4ad0-89fd-39c4fdc44a5d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# And now: call the ollama API. \n", |
||||
"\n", |
||||
"def summarize(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = ollama.chat(\n", |
||||
" model = MODEL,\n", |
||||
" messages = messages_for(website)\n", |
||||
" )\n", |
||||
" return response['message']['content']" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "466c2f78-91ca-4ed2-b60b-40661d0b6f68", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"summarize(\"https://edwarddonner.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7ab7c9a1-70fd-421c-be06-c36eb6c9aedf", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||
"\n", |
||||
"def display_summary(url):\n", |
||||
" summary = summarize(url)\n", |
||||
" display(Markdown(summary))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1cedc9d9-6a76-4225-82c1-82240da16260", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display_summary(\"https://edwarddonner.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "82c48586-33c8-4797-a24f-41602c1297b3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "llms", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,435 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||
"metadata": { |
||||
"jp-MarkdownHeadingCollapsed": true |
||||
}, |
||||
"source": [ |
||||
"# Welcome to your first assignment!\n", |
||||
"\n", |
||||
"Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9", |
||||
"metadata": { |
||||
"jupyter": { |
||||
"source_hidden": true |
||||
} |
||||
}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n", |
||||
" <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n", |
||||
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n", |
||||
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# HOMEWORK EXERCISE ASSIGNMENT\n", |
||||
"\n", |
||||
"Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n", |
||||
"\n", |
||||
"You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n", |
||||
"\n", |
||||
"**Benefits:**\n", |
||||
"1. No API charges - open-source\n", |
||||
"2. Data doesn't leave your box\n", |
||||
"\n", |
||||
"**Disadvantages:**\n", |
||||
"1. Significantly less power than Frontier Model\n", |
||||
"\n", |
||||
"## Recap on installation of Ollama\n", |
||||
"\n", |
||||
"Simply visit [ollama.com](https://ollama.com) and install!\n", |
||||
"\n", |
||||
"Once complete, the ollama server should already be running locally. \n", |
||||
"If you visit: \n", |
||||
"[http://localhost:11434/](http://localhost:11434/)\n", |
||||
"\n", |
||||
"You should see the message `Ollama is running`. \n", |
||||
"\n", |
||||
"If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n", |
||||
"And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n", |
||||
"Then try [http://localhost:11434/](http://localhost:11434/) again.\n", |
||||
"\n", |
||||
"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "29ddd15d-a3c5-4f4e-a678-873f56162724", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Constants\n", |
||||
"\n", |
||||
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||
"MODEL = \"llama3.2\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "dac0a679-599c-441f-9bf2-ddc73d35b940", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Create a messages list using the same format that we used for OpenAI\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n", |
||||
"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7bb9c624-14f0-4945-a719-8ddb64f66f47", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"payload = {\n", |
||||
" \"model\": MODEL,\n", |
||||
" \"messages\": messages,\n", |
||||
" \"stream\": False\n", |
||||
" }" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "479ff514-e8bd-4985-a572-2ea28bb4fa40", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's just make sure the model is loaded\n", |
||||
"\n", |
||||
"!ollama pull llama3.2" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "42b9f644-522d-4e05-a691-56e7658c0ea9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# If this doesn't work for any reason, try the 2 versions in the following cells\n", |
||||
"# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n", |
||||
"# And if none of that works - contact me!\n", |
||||
"\n", |
||||
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
||||
"print(response.json()['message']['content'])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Introducing the ollama package\n", |
||||
"\n", |
||||
"And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n", |
||||
"\n", |
||||
"Under the hood, it's making the same call as above to the ollama server running at localhost:11434" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7745b9c4-57dc-4867-9180-61fa5db55eb8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import ollama\n", |
||||
"\n", |
||||
"response = ollama.chat(model=MODEL, messages=messages)\n", |
||||
"print(response['message']['content'])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "a4704e10-f5fb-4c15-a935-f046c06fb13d", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Alternative approach - using OpenAI python library to connect to Ollama" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "23057e00-b6fc-4678-93a9-6b31cb704bff", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# There's actually an alternative approach that some people might prefer\n", |
||||
"# You can use the OpenAI client python library to call Ollama:\n", |
||||
"\n", |
||||
"from openai import OpenAI\n", |
||||
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", |
||||
"\n", |
||||
"response = ollama_via_openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=messages\n", |
||||
")\n", |
||||
"\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Also trying the amazing reasoning model DeepSeek\n", |
||||
"\n", |
||||
"Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n", |
||||
"This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n", |
||||
"\n", |
||||
"Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!ollama pull deepseek-r1:1.5b" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1d3d554b-e00d-4c08-9300-45e073950a76", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside <think> tags, followed by some decent definitions\n", |
||||
"\n", |
||||
"response = ollama_via_openai.chat.completions.create(\n", |
||||
" model=\"deepseek-r1:1.5b\",\n", |
||||
" messages=[{\"role\": \"user\", \"content\": \"Please give definitions of some core concepts behind LLMs: a neural network, attention and the transformer\"}]\n", |
||||
")\n", |
||||
"\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# NOW the exercise for you\n", |
||||
"\n", |
||||
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 1, |
||||
"id": "6de38216-6d1c-48c4-877b-86d403f4e0f8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from openai import OpenAI\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"HEADERS = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\",\n", |
||||
" \"Content-Type\": \"application/json\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||
"\n", |
||||
"MODEL = \"llama3.2\"\n", |
||||
"\n", |
||||
"system_prompt = \"Sei un assistente e analizzi il contenuto di un sito web \\\n", |
||||
"produci un breve sommario, ignora il testo o gli elementi relativi alla navigazione. \\\n", |
||||
"Rispondi markdown.\"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 2, |
||||
"id": "6f343c27-628c-4c54-9a5b-842e6ad5d176", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=HEADERS)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 4, |
||||
"id": "bf6245ca-2d53-4fd8-a19c-0e6d052031fd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"Stai cercando un sito dal titolo: {website.title}\"\n", |
||||
" user_prompt += \"\\nI contenuti di questo sito web sono i seguenti: \\\n", |
||||
"Per favore, fornisci un breve riassunto di questo sito web in markdown. \\\n", |
||||
"Se include notizie o annunci, riassumili anch'essi. \\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 6, |
||||
"id": "dec0636f-9efc-4f91-8861-3141276a9a6e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 8, |
||||
"id": "f894b232-1ea1-4bd9-bf44-d7b1571f7913", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def summarize(url):\n", |
||||
" ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", |
||||
" \n", |
||||
" website = Website(url)\n", |
||||
" response = ollama_via_openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=messages_for(website)\n", |
||||
" )\n", |
||||
" return response.choices[0].message.content\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 13, |
||||
"id": "d868d778-13b5-4934-acf5-dcb919a27d59", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def display_summary(url):\n", |
||||
" summary = summarize(url)\n", |
||||
" display(Markdown(summary))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 14, |
||||
"id": "0a0d9b79-de3c-4f77-9254-f02cf4d6217a", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"data": { |
||||
"text/markdown": [ |
||||
"**Sommario del sito WP Pisa - Il sito del Meetup WordPress di Pisa**\n", |
||||
"\n", |
||||
"Il sito web WP Pisa è il punto di riferimento per gli appassionati di WordPress a Pisa. Gli organizzatori offrono meetup mensili gratuiti per discutere conoscenze, esperienze e progetti correlati al mondo di WordPress.\n", |
||||
"\n", |
||||
"**Eventi e Annunci**\n", |
||||
"\n", |
||||
"* **WordCamp Pisa 2025**: Aperta la call for organizer\n", |
||||
"* **Il Tuo Sito Ovunque in Pochi Minuti**: Un incontro con Docker e sviluppatori WordPress - Partecipa!\n", |
||||
"* **Core Days Roma: Le novità sul core di WordPress per i dev**\n", |
||||
"* **NO MORE THUMBNAILS!**\n", |
||||
"\n", |
||||
"**Informazioni Generali**\n", |
||||
"\n", |
||||
"* Il meetuu è aperto a tutti, indipendentemente dal livello di competenza in WordPress\n", |
||||
"* Tutti gli eventi sono gratuiti e organizzati con la supervisione di WordPress Foundation tramite la piattaforma Meetup.com\n", |
||||
"* La comunità WP Pisa contiene 150+ iscritti" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.Markdown object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
} |
||||
], |
||||
"source": [ |
||||
"display_summary(\"https://wppisa.it/\")" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,93 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fa4447be-7825-45d9-a6a5-ed41f2500533", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display\n", |
||||
"from openai import OpenAI\n", |
||||
"\n", |
||||
"openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", |
||||
"MODEL = \"llama3.2\"\n", |
||||
"\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
"\n", |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; please provide a short summary of this website in markdown. \\\n", |
||||
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt\n", |
||||
"\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"\n", |
||||
"\n", |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ] \n", |
||||
"\n", |
||||
"def summarize(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model = MODEL,\n", |
||||
" messages = messages_for(website)\n", |
||||
" )\n", |
||||
" return response.choices[0].message.content\n", |
||||
"\n", |
||||
"def display_summary(url):\n", |
||||
" summary = summarize(url)\n", |
||||
" display(Markdown(summary))\n", |
||||
"\n", |
||||
"\n", |
||||
"display_summary(\"https://esarijal.my.id\")" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,186 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1faf8b29-2ba6-40c7-89ee-71f71e234f11", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Extra requirements\n", |
||||
"```bash\n", |
||||
"pip install -q -U google-genai\n", |
||||
"```\n", |
||||
"\n", |
||||
"## Required environment variable\n", |
||||
"GEMINI_API_KEY\n", |
||||
"\n", |
||||
"### How to get GEMINI API KEY\n", |
||||
"\n", |
||||
"Use the link: [gemini api key](https://aistudio.google.com/app/apikey) to get yours." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 12, |
||||
"id": "be06ce76-20ee-4066-9582-a4ed745f278f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from google import genai\n", |
||||
"from google.genai import types" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 13, |
||||
"id": "99e42519-5dac-4b13-8a26-8a635753343b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def gemini_invoke(website):\n", |
||||
" load_dotenv()\n", |
||||
" api_key = os.getenv(\"GEMINI_API_KEY\")\n", |
||||
" if not api_key or len(api_key) < 39:\n", |
||||
" print(\"No correct api key was found\")\n", |
||||
" return\n", |
||||
" else:\n", |
||||
" print(\"Api key found. Good to go!\")\n", |
||||
" client = genai.Client(api_key=api_key)\n", |
||||
" response = client.models.generate_content(\n", |
||||
" model=\"gemini-2.0-flash\",\n", |
||||
" config=types.GenerateContentConfig(\n", |
||||
" system_instruction=system_prompt),\n", |
||||
" contents=user_prompt_for(website)\n", |
||||
" )\n", |
||||
" return response.text" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 14, |
||||
"id": "95a6ece8-8402-4cad-96b9-36a6ea444c54", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"class Website:\n", |
||||
" url: str\n", |
||||
" title: str\n", |
||||
" text: str\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url)\n", |
||||
" soup = BeautifulSoup(response.content, \"html.parser\")\n", |
||||
" self.title = soup.title.string if soup.title else \"No title was found\"\n", |
||||
"\n", |
||||
" for irr in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irr.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
" " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "24bbd1dd-dca4-4bbc-ae91-4bad227a4278", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"ed = Website(\"https://edwarddonner.com\")\n", |
||||
"print(ed.title)\n", |
||||
"print(ed.text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 15, |
||||
"id": "233b8904-7a4a-4265-8b0d-20934ae4b29c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that navigation related. Respond \\\n", |
||||
"in markdown.\"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 16, |
||||
"id": "5c996c03-84ab-4378-8a55-026d94404d35", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"messages = [{\"role\": \"user\", \"content\": system_prompt}]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 17, |
||||
"id": "abf9464e-dc8d-4099-aeb6-495498326673", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||
"please provide a short summary of this website in markdown. \\\n", |
||||
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 20, |
||||
"id": "32ab2d29-02d1-43c5-b920-f2621f292b23", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def summarize(url, model=\"gemini\"):\n", |
||||
" website = Website(url)\n", |
||||
" if model == \"ollama\":\n", |
||||
" import ollama\n", |
||||
" Model=\"llama3.2\"\n", |
||||
" messages[0][\"content\"] += f\" Website: {url}\"\n", |
||||
" response = ollama.chat(model=Model, messages=messages)\n", |
||||
" return response[\"message\"][\"content\"]\n", |
||||
" else:\n", |
||||
" return gemini_invoke(website)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a2a0e518-7198-489d-a0ce-2eec617f939f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"summarize(\"https://edwarddonner.com\", \"ollama\")" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.12.0" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,240 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fc3a96d1-eedf-4e3a-b3ce-151485c574b5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "385dc3d5-f6ce-46d8-958e-83dc1150c24e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||
"HEADERS = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"MODEL = \"llama3.2\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "21f7dacc-1fa8-491c-8e94-39238dae52b3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"class Website:\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=HEADERS)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ca431e32-9191-4940-b62d-f25e8cbac627", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"web = Website(\"https://silviayomdesign.com/\")\n", |
||||
"print(web.title)\n", |
||||
"print(web.text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "76475815-0dbc-451b-ab65-f7e2ea3aaa8a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3cf03913-f595-4817-8580-19b182c599de", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a very artistic graphic designer's website titled name {website.title}\"\n", |
||||
" user_prompt += \"\\nHer creativity of her works are as follow;\\\n", |
||||
"please provide a short summary of her works in markdown. \\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6f130cfe-756b-4df8-b1f0-6918956a6162", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(user_prompt_for(web))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "85d85b64-1452-408f-bfae-d27b52d7dfa7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(web)}\n", |
||||
"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "36d66055-66d6-4123-b092-eceab055829d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"payload = {\n", |
||||
" \"model\": MODEL,\n", |
||||
" \"messages\": messages,\n", |
||||
" \"stream\": False\n", |
||||
"}" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "163db8a9-b0eb-49f3-a5f2-1e74cf51c245", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
||||
"print(response.json()[\"message\"][\"content\"])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "afabfff5-81e5-4b61-aca9-6c19d3584b86", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(web)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b2e83b58-16fc-4049-8116-24a0cbb3635a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"messages_for(web)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "05ed519a-514f-4ed8-b323-4f4817e1e1c6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import ollama\n", |
||||
"def summarize(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = ollama.chat(\n", |
||||
" model=MODEL, \n", |
||||
" messages=messages\n", |
||||
" )\n", |
||||
" return response[\"message\"][\"content\"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b182f686-0a3e-4959-9bfd-0a59d2befd4c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"summarize(\"https://silviayomdesign.com/\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f4f1f807-28d4-4b8b-9698-9b90dcbac59f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def display_summary(url):\n", |
||||
" summary = summarize(url)\n", |
||||
" display(Markdown(summary))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a988d29b-ed36-4a40-bd77-0f7d60a29ac3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display_summary(\"https://silviayomdesign.com/\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "15e72eeb-1c35-4bb2-9596-6ff2546aa046", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,663 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# A full business solution\n", |
||||
"\n", |
||||
"## Now we will take our project from Day 1 to the next level\n", |
||||
"\n", |
||||
"### BUSINESS CHALLENGE:\n", |
||||
"\n", |
||||
"Create a product that builds a Brochure for a company to be used for prospective clients, investors and potential recruits.\n", |
||||
"\n", |
||||
"We will be provided a company name and their primary website.\n", |
||||
"\n", |
||||
"See the end of this notebook for examples of real-world business applications.\n", |
||||
"\n", |
||||
"And remember: I'm always available if you have problems or ideas! Please do reach out." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d5b08506-dc8b-4443-9201-5f1848161363", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"import json\n", |
||||
"from typing import List\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display, update_display\n", |
||||
"from openai import OpenAI" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fc5d8880-f2ee-4c06-af16-ecbc0262af61", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Initialize and constants\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", |
||||
" print(\"API key looks good so far\")\n", |
||||
"else:\n", |
||||
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||
" \n", |
||||
"MODEL = 'gpt-4o-mini'\n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "106dd65e-90af-4ca8-86b6-23a41840645b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
" \"\"\"\n", |
||||
" A utility class to represent a Website that we have scraped, now with links\n", |
||||
" \"\"\"\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" self.body = response.content\n", |
||||
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" if soup.body:\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
" else:\n", |
||||
" self.text = \"\"\n", |
||||
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||
" self.links = [link for link in links if link]\n", |
||||
"\n", |
||||
" def get_contents(self):\n", |
||||
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e30d8128-933b-44cc-81c8-ab4c9d86589a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"ed = Website(\"https://edwarddonner.com\")\n", |
||||
"ed.links" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1771af9c-717a-4fca-bbbe-8a95893312c3", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## First step: Have GPT-4o-mini figure out which links are relevant\n", |
||||
"\n", |
||||
"### Use a call to gpt-4o-mini to read the links on a webpage, and respond in structured JSON. \n", |
||||
"It should decide which links are relevant, and replace relative links such as \"/about\" with \"https://company.com/about\". \n", |
||||
"We will use \"one shot prompting\" in which we provide an example of how it should respond in the prompt.\n", |
||||
"\n", |
||||
"This is an excellent use case for an LLM, because it requires nuanced understanding. Imagine trying to code this without LLMs by parsing and analyzing the webpage - it would be very hard!\n", |
||||
"\n", |
||||
"Sidenote: there is a more advanced technique called \"Structured Outputs\" in which we require the model to respond according to a spec. We cover this technique in Week 8 during our autonomous Agentic AI project." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6957b079-0d96-45f7-a26a-3487510e9b35", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"oneshot_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||
"You are able to decide which of the links would be most relevant to include in a brochure about the company or freelancer offering their services, \\\n", |
||||
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||
"oneshot_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||
"oneshot_system_prompt += \"\"\"\n", |
||||
"{\n", |
||||
" \"links\": [\n", |
||||
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", |
||||
" ]\n", |
||||
"}\n", |
||||
"\"\"\"\n", |
||||
"oneshot_system_prompt += \"Make sure not to miss any relevant pages.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f5a8b688-b153-41a6-8b18-f6198f3df2c9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"fewshot_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||
"You are able to decide which of the links would be most relevant to include in a brochure about the company or freelancer offering their services, \\\n", |
||||
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n You should respond in JSON as in the following examples:\"\n", |
||||
"fewshot_system_prompt += \"\"\"\n", |
||||
" Example 1\n", |
||||
" ['https://great-comps.com/about-me', 'https://www.linkedin.com/in/great-comp/', 'mailto:hello@mygroovydomain.com', 'https://great-comps.com/news', '/case-studies', 'https://patents.google.com/patent/US20210049536A1/', 'https://great-comps.com/workshop-ai']\n", |
||||
"\n", |
||||
" Links:\n", |
||||
" {\n", |
||||
" \"links\": [\n", |
||||
" {\"type\": \"about page\", \"url\": \"https://great-comps.de/about-me\"},\n", |
||||
" {\"type\": \"news page\": \"url\": \"https://great-comps.de/news\"},\n", |
||||
" {\"type\": \"case studies page\": \"url\": \"https://great-comps.de/case-studies\"},\n", |
||||
" {\"type\": \"workshop page\": \"url\": \"https://great-comps.de/workshop-ai\"},\n", |
||||
" ]\n", |
||||
" }\n", |
||||
"\n", |
||||
" Example 2\n", |
||||
" ['mailto:info@robbie-doodle-domain.com','https://wahlen-robbie.at/ueber-mich', 'https://www.linkedin.com/in/robbie-doodle/', 'https://news.ycombinator.com', 'https://wahlen-robbie.at/neuigkeiten', 'https://twitter.com/robbie-d', '/whitepapers', 'https://patents.google.com/patent/US20210049536A1/', 'https://wahlen-robbie.at/services']\n", |
||||
"\n", |
||||
" Links:\n", |
||||
" {\n", |
||||
" \"links\": [\n", |
||||
" {\"type\": \"über mich\", \"url\": \"https://wahlen-robbie.at/ueber-mich\"},\n", |
||||
" {\"type\": \"aktuelles\": \"url\": \"https://wahlen-robbie.at/neuigkeiten\"},\n", |
||||
" {\"type\": \"whitepaper\": \"url\": \"https://wahlen-robbie.at/whitepapers\"},\n", |
||||
" {\"type\": \"services\": \"url\": \"https://wahlen-robbie.at/services\"}\n", |
||||
" ]\n", |
||||
" }\n", |
||||
" \"\"\"\n", |
||||
"fewshot_system_prompt += \"Make sure not to miss any relevant pages.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b97e4068-97ed-4120-beae-c42105e4d59a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(f\"Oneshot system prompt:\\n{oneshot_system_prompt}\")\n", |
||||
"print(f\"\\n\\n\\nFewshot system prompt:\\n{fewshot_system_prompt}\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_links_user_prompt(website):\n", |
||||
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company or person offering their services, respond with the full https URL in JSON format. \\\n", |
||||
"Do not include Terms of Service, Privacy, email links or social media links.\\n\"\n", |
||||
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||
" user_prompt += \"\\n\".join(website.links)\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6bcbfa78-6395-4685-b92c-22d592050fd7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(get_links_user_prompt(ed))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a29aca19-ca13-471c-a4b4-5abbfa813f69", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_links(url, system_prompt=oneshot_system_prompt):\n", |
||||
" \n", |
||||
" website = Website(url)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||
" ],\n", |
||||
" response_format={\"type\": \"json_object\"}\n", |
||||
" )\n", |
||||
" \n", |
||||
" result = response.choices[0].message.content \n", |
||||
" print(f\"Response: {result}\")\n", |
||||
" return json.loads(result)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2dc4150a-0042-4f5d-a7bf-158a0f9147a6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"get_links(ed_url)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "74a827a0-2782-4ae5-b210-4a242a8b4cc2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n", |
||||
"hf = \"https://huggingface.co\"\n", |
||||
"\n", |
||||
"huggingface = Website(hf)\n", |
||||
"huggingface.links" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"ed_url = \"https://edwarddonner.com\"\n", |
||||
"hf_url = \"https://huggingface.co\"\n", |
||||
"\n", |
||||
"print(f\"Links generated with oneshot prompt for {ed_url}:\\n\")\n", |
||||
"get_links(ed_url)\n", |
||||
"\n", |
||||
"print(f\"\\n\\nLinks generated with fewshot prompt for {ed_url}:\\n\")\n", |
||||
"get_links(ed_url, fewshot_system_prompt)\n", |
||||
"\n", |
||||
"print(50*\"*\")\n", |
||||
"print(f\"\\nLinks generated with oneshot prompt for {hf_url}:\\n\")\n", |
||||
"get_links(hf_url)\n", |
||||
"\n", |
||||
"print(f\"\\n\\nLinks generated with fewshot prompt for {hf_url}:\\n\")\n", |
||||
"get_links(hf_url, fewshot_system_prompt)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "0d74128e-dfb6-47ec-9549-288b621c838c", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Second step: make the brochure!\n", |
||||
"\n", |
||||
"Assemble all the details into another prompt to GPT4-o" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_all_details(url, type=fewshot_system_prompt):\n", |
||||
" result = \"Landing page:\\n\"\n", |
||||
" result += Website(url).get_contents()\n", |
||||
"\n", |
||||
" links = get_links(url, type)\n", |
||||
" print(\"Found links:\", links)\n", |
||||
" for link in links[\"links\"]:\n", |
||||
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||
" result += Website(link[\"url\"]).get_contents()\n", |
||||
" return result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "5099bd14-076d-4745-baf3-dac08d8e5ab2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(get_all_details(ed_url))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||
"and creates a short brochure about the company for prospective customers, investors and recruits. \\\n", |
||||
"The brochure should be a bit unusual in terms of tone and style, it should astound the reader and pique their interest. Respond in markdown.\\\n", |
||||
"Include details of company culture, customers and careers/jobs if you have the information.\"\n", |
||||
"\n", |
||||
"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n", |
||||
"\n", |
||||
"# system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||
"# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||
"# Include details of company culture, customers and careers/jobs if you have the information.\"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_brochure_user_prompt(company_name, url):\n", |
||||
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||
" user_prompt += get_all_details(url)\n", |
||||
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "05d07160-7910-4da2-92ac-36aa849fcc68", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# get_brochure_user_prompt(\"Edward Donner\", ed_url)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "cd909e0b-1312-4ce2-a553-821e795d7572", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# get_brochure_user_prompt(\"HuggingFace\", \"https://huggingface.co\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def create_brochure(company_name, url):\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||
" ],\n", |
||||
" )\n", |
||||
" return response.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6b0de762-f343-44d9-85d5-9bffba3c0ae8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"brochure_ed = create_brochure(\"Edward Donner\", ed_url)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e093444a-9407-42ae-924a-145730591a39", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"brochure_hf = create_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0d00b012-3901-492c-b985-a0340750c011", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display(Markdown(brochure_ed))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e33cb2e9-3b8c-4ef3-a6cb-70b3188b9120", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"display(Markdown(brochure_hf))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "dea955ad-24a6-490b-8191-f066bff1b595", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def translate_brochure(brochure_content, language=\"German\"):\n", |
||||
" system_prompt = f\"You are a skilled translator. Translate the following brochure text into {language}.\\\n", |
||||
" Make sure to translate into a idiomatic {language}, matching the target language's natural structure, wording and expressions, so it can't be recognised as a translation.\\\n", |
||||
" Be sure to also meet an appropriate tone, eg a good marketing language in other languages will probably be a bit less boastful than in English.\\\n", |
||||
" Output the translated brochure in Markdown format.\"\n", |
||||
" \n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model = MODEL,\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": brochure_content}]\n", |
||||
" )\n", |
||||
"\n", |
||||
" return response.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "9b6bdd4f-7518-4780-9da9-47f90aab974b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"translation = translate_brochure(brochure_ed, language=\"German\")\n", |
||||
"display(Markdown(translation))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f1dd96f2-0980-4a30-a152-1f38c0e319bb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"translation = translate_brochure(brochure_hf, language=\"German\")\n", |
||||
"display(Markdown(translation))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "61eaaab7-0b47-4b29-82d4-75d474ad8d18", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Finally - a minor improvement\n", |
||||
"\n", |
||||
"With a small adjustment, we can change this so that the results stream back from OpenAI,\n", |
||||
"with the familiar typewriter animation" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "51db0e49-f261-4137-aabe-92dd601f7725", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_brochure(company_name, url):\n", |
||||
" stream = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||
" ],\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" \n", |
||||
" response = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" for chunk in stream:\n", |
||||
" response += chunk.choices[0].delta.content or ''\n", |
||||
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fdb3f8d8-a3eb-41c8-b1aa-9f60686a653b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Try changing the system prompt to the humorous version when you make the Brochure for Hugging Face:\n", |
||||
"\n", |
||||
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "a27bf9e0-665f-4645-b66b-9725e2a959b5", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#181;\">Business applications</h2>\n", |
||||
" <span style=\"color:#181;\">In this exercise we extended the Day 1 code to make multiple LLM calls, and generate a document.\n", |
||||
"\n", |
||||
"This is perhaps the first example of Agentic AI design patterns, as we combined multiple calls to LLMs. This will feature more in Week 2, and then we will return to Agentic AI in a big way in Week 8 when we build a fully autonomous Agent solution.\n", |
||||
"\n", |
||||
"Generating content in this way is one of the very most common Use Cases. As with summarization, this can be applied to any business vertical. Write marketing content, generate a product tutorial from a spec, create personalized email content, and so much more. Explore how you can apply content generation to your business, and try making yourself a proof-of-concept prototype. See what other students have done in the community-contributions folder -- so many valuable projects -- it's wild!</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "14b2454b-8ef8-4b5c-b928-053a15e0d553", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Before you move to Week 2 (which is tons of fun)</h2>\n", |
||||
" <span style=\"color:#900;\">Please see the week1 EXERCISE notebook for your challenge for the end of week 1. This will give you some essential practice working with Frontier APIs, and prepare you well for Week 2.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "17b64f0f-7d33-4493-985a-033d06e8db08", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#f71;\">A reminder on 3 useful resources</h2>\n", |
||||
" <span style=\"color:#f71;\">1. The resources for the course are available <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">here.</a><br/>\n", |
||||
" 2. I'm on LinkedIn <a href=\"https://www.linkedin.com/in/eddonner/\">here</a> and I love connecting with people taking the course!<br/>\n", |
||||
" 3. I'm trying out X/Twitter and I'm at <a href=\"https://x.com/edwarddonner\">@edwarddonner<a> and hoping people will teach me how it's done.. \n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6f48e42e-fa7a-495f-a5d4-26bfc24d60b6", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../thankyou.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#090;\">Finally! I have a special request for you</h2>\n", |
||||
" <span style=\"color:#090;\">\n", |
||||
" My editor tells me that it makes a MASSIVE difference when students rate this course on Udemy - it's one of the main ways that Udemy decides whether to show it to others. If you're able to take a minute to rate this, I'd be so very grateful! And regardless - always please reach out to me at ed@edwarddonner.com if I can help at any point.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,440 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "07be6aa3-6636-4b57-be16-823c3907f4c4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import requests\n", |
||||
"import json\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display, update_display\n", |
||||
"from openai import OpenAI" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0e64af7b-6956-4437-ab32-857a6ea814c3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"load_dotenv()\n", |
||||
"api_key = os.getenv(\"OPENAI_API_KEY\")\n", |
||||
"\n", |
||||
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", |
||||
" print(\"Api key found. Good to go!\") \n", |
||||
"else:\n", |
||||
" print(\"No correct api key was found\")\n", |
||||
"MODEL = \"gpt-4o-mini\"\n", |
||||
"openai = OpenAI(api_key=api_key)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4667e3ee-d5b7-42ed-99ad-5e9fa75c8660", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "42adb18b-3ec9-4700-95e4-c0041ce8f17a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"class GithubProfile:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" self.body = response.content\n", |
||||
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" if soup.body:\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
" else:\n", |
||||
" self.text = \"\"\n", |
||||
" links = [link.get(\"href\") for link in soup.find_all(\"a\")]\n", |
||||
" self.links = [link for link in links if link]\n", |
||||
" \n", |
||||
" def get_contents(self):\n", |
||||
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "661b5377-c444-45a9-9455-85f83ff525d3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"profile = GithubProfile(\"https://github.com/ertgl\")\n", |
||||
"profile.links" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8f9a3c08-0db2-4baa-a8a4-f5642049a57c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"link_system_prompt = \"You are provided with a list of links found on a Github page. \\\n", |
||||
"You are able to decide which of the links would be most relevant to include in a portfolio about the github user, \\\n", |
||||
"such as links to an About page, or a Repositories, or Projects.\\n\"\n", |
||||
"link_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||
"link_system_prompt += \"\"\"\n", |
||||
"{\n", |
||||
" \"links\": [\n", |
||||
" {\"type\": \"overview page\", \"url\": \"https://another.full.url\"},\n", |
||||
" {\"type\": \"repositories page\": \"url\": \"https://another.full.url?tab=repositories\"}\n", |
||||
" ]\n", |
||||
"}\n", |
||||
"\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "30eafd50-9735-4388-9cc1-8337a00069a2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(link_system_prompt)\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4dc4f366-5c00-441d-b1bd-8dda148f1ffb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_links_user_prompt(profile):\n", |
||||
" user_prompt = f\"Here is the list of links on the website of {profile.url} - \"\n", |
||||
" user_prompt += \"please decide which of these are relevant web links for a portfolio about the user, respond with the full https URL in JSON format. \\\n", |
||||
"Do not include Terms of Service, Privacy, Login, Blog or Github trending related pages.\\n\"\n", |
||||
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||
" user_prompt += \"\\n\".join(profile.links)\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c066b2ac-5863-408e-bb42-1388d130d164", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(get_links_user_prompt(profile))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "dc0ccb95-479c-4f6e-9686-1ff38aa543fa", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_links(url):\n", |
||||
" profile = GithubProfile(url)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_links_user_prompt(profile)}\n", |
||||
" ],\n", |
||||
" response_format= {\"type\": \"json_object\"}\n", |
||||
" )\n", |
||||
" result = response.choices[0].message.content\n", |
||||
" return json.loads(result)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "9f5e3b8b-398d-4e23-867e-401faca7db03", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"get_links(profile.url)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "b9024a4f-4038-4c0e-b0c7-74226feaccfd", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Second step: make the portfolio!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f9906d73-801a-4aea-b620-10ac39eaf424", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_all_details(url):\n", |
||||
" result = \"Landing page:\\n\"\n", |
||||
" result += GithubProfile(url).get_contents()\n", |
||||
" links = get_links(url)\n", |
||||
" print(\"Found links:\", links)\n", |
||||
" for link in links[\"links\"]:\n", |
||||
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||
" result += GithubProfile(link[\"url\"]).get_contents()\n", |
||||
" return result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "02039450-7f7f-4556-8645-39cd31f30265", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(get_all_details(\"https://github.com/ertgl\"))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4119b96f-0aa1-4cdb-9a09-d51b163069b8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a personal github page \\\n", |
||||
"and creates a short portfolio about the user profile, especially projects and repositories and summary of the repo's \\\n", |
||||
"README files for prospective recruiters, investors. Respond in markdown.\\\n", |
||||
"Include details of person profile overview, if you have the information.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "842834d2-a5e9-4b56-a792-492a1a137fbc", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_portfolio_user_prompt(profile_name, url):\n", |
||||
" user_prompt = f\"You are looking at a user called: {profile_name} on Github.\\n\"\n", |
||||
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short portfolio of the user in markdown.\\n\"\n", |
||||
" user_prompt += get_all_details(url)\n", |
||||
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "285b3a1d-894a-463c-8c30-b5de203b8358", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(get_portfolio_user_prompt(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\"))\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "78dc7495-d0a5-409b-8ecf-3a5ef9220e25", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def create_portfolio(profile_name, url):\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_portfolio_user_prompt(profile_name, url)}\n", |
||||
" ]\n", |
||||
" )\n", |
||||
" result = response.choices[0].message.content\n", |
||||
" display(Markdown(result))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "abe39377-2d52-434a-aace-e9397cdd4f20", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"create_portfolio(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "edd168ca-b77b-4fc7-9e11-2114a43553e4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_portfolio(profile_name, url):\n", |
||||
" stream = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_portfolio_user_prompt(profile_name, url)}\n", |
||||
" ],\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" \n", |
||||
" response = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" for chunk in stream:\n", |
||||
" response += chunk.choices[0].delta.content or ''\n", |
||||
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1ea391d4-775e-483d-9e55-e3ae30fa9bd8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"stream_portfolio(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "498ca0c8-8f68-4389-8184-078706b62cf6", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Multi-lingual with Multi-Tone in Desire Format" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 100, |
||||
"id": "f11e3391-03f9-409c-9f5a-6286959690ec", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def multi_lingual_stream_portfolio(profile_name, url, language, tone):\n", |
||||
"\n", |
||||
" system_prompt = f\"\"\"\n", |
||||
"You are an assistant that analyzes the contents of several relevant pages from a github profile page and \n", |
||||
"creates a visually appealing and professional short portfolio for prospective investors, and recruiters. \n", |
||||
"The portfolio should be written in {language} and use a {tone.lower()} tone throughout.\n", |
||||
"\n", |
||||
"The portfolio should follow this structure (in {language}):\n", |
||||
"\n", |
||||
"1. **Front Cover**:\n", |
||||
" - Prominently display the user name as Title.\n", |
||||
" - Include a compelling headline or tagline.\n", |
||||
" - Add something engaging relevant to the user’s summarized README files if available.\n", |
||||
"\n", |
||||
"2. **About**:\n", |
||||
" - Provide a brief introduction to the user's projects approach.\n", |
||||
" - State which repository they own or they contributed.\n", |
||||
"\n", |
||||
"3. **Overview**:\n", |
||||
" - Summarize the user's projects, repositories, or solutions by summarized README files if available.\n", |
||||
" - Highlight benefits or unique developer/development points.\n", |
||||
" - Mention the follower and following users count and total stars they got.\n", |
||||
"\n", |
||||
"\n", |
||||
"4. **My Culture**:\n", |
||||
" - Outline the user’s key values or guiding principles.\n", |
||||
" - Describe the workplace environment (e.g., innovation-driven, inclusive, collaborative).\n", |
||||
" - Highlight community engagement.\n", |
||||
"\n", |
||||
"5. **What kind of companies may be interested**:\n", |
||||
" - Describe the target customers or industries served.\n", |
||||
" - Mention open source contributions also if available. \n", |
||||
" \n", |
||||
"6. **Projects**:\n", |
||||
" \n", |
||||
" ***Owner***:\n", |
||||
" - List owned projects/repositories with summaries. (Summarize README file of the each project)\n", |
||||
" \n", |
||||
" ***Contributer***:\n", |
||||
" - List contributed projects/repositories with summaries. (Summarize README file of the each project)\n", |
||||
"\n", |
||||
"\n", |
||||
"7. **Support and Donation**:\n", |
||||
" - Encourage those interested in user's open source projects to donate.\n", |
||||
" - Provide direct links or steps to apply if available.\n", |
||||
"\n", |
||||
"8. **Contact Us**:\n", |
||||
" - Provide the user’s address, phone number, and email.\n", |
||||
" - Include links to social media platforms.\n", |
||||
" - Add a link to the user’s website.\n", |
||||
"\n", |
||||
"9. **Closing Note**:\n", |
||||
" - End with a thank-you message or an inspirational note for the reader.\n", |
||||
" - Add a call-to-action (e.g., “Get in touch today!” or “Explore more on my website”).\n", |
||||
"\n", |
||||
"Ensure the content is concise, engaging, visually clear, and tailored to the target audience. Use headings and subheadings to make the brochure easy to navigate. Include links and contact information wherever applicable.\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"\n", |
||||
" \n", |
||||
" stream = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_portfolio_user_prompt(profile_name, url)}\n", |
||||
" ],\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" \n", |
||||
" response = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" for chunk in stream:\n", |
||||
" response += chunk.choices[0].delta.content or ''\n", |
||||
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3a38dc0b-27de-4738-8883-b3857e067b45", |
||||
"metadata": { |
||||
"scrolled": true |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"multi_lingual_stream_portfolio(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\", \"English\", \"serious, entertaining, witty\")" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.12.0" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,501 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# A full business solution (Ollama Version)\n", |
||||
"\n", |
||||
"## Now we will take our project from Day 1 to the next level\n", |
||||
"\n", |
||||
"### BUSINESS CHALLENGE:\n", |
||||
"\n", |
||||
"Create a product that builds a Brochure for a company to be used for prospective clients, investors and potential recruits.\n", |
||||
"\n", |
||||
"We will be provided a company name and their primary website.\n", |
||||
"\n", |
||||
"See the end of this notebook for examples of real-world business applications.\n", |
||||
"\n", |
||||
"And remember: I'm always available if you have problems or ideas! Please do reach out." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d5b08506-dc8b-4443-9201-5f1848161363", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"import json\n", |
||||
"from typing import List\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display, update_display\n", |
||||
"import ollama" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fc5d8880-f2ee-4c06-af16-ecbc0262af61", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Initialize and constants\n", |
||||
" \n", |
||||
"# MODEL = \"llama3.2\"\n", |
||||
"MODEL = \"llama3.2:1b\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "106dd65e-90af-4ca8-86b6-23a41840645b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"\n", |
||||
"# Some websites need you to use proper headers when fetching them:\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
" \"\"\"\n", |
||||
" A utility class to represent a Website that we have scraped, now with links\n", |
||||
" \"\"\"\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" self.body = response.content\n", |
||||
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" if soup.body:\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
" else:\n", |
||||
" self.text = \"\"\n", |
||||
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||
" self.links = [link for link in links if link]\n", |
||||
"\n", |
||||
" def get_contents(self):\n", |
||||
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e30d8128-933b-44cc-81c8-ab4c9d86589a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"ed = Website(\"https://edwarddonner.com\")\n", |
||||
"ed.links\n", |
||||
"# print(ed.get_contents())" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1771af9c-717a-4fca-bbbe-8a95893312c3", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## First step: Have llama3.2 figure out which links are relevant\n", |
||||
"\n", |
||||
"### Use a call to llama3.2 to read the links on a webpage, and respond in structured JSON. \n", |
||||
"It should decide which links are relevant, and replace relative links such as \"/about\" with \"https://company.com/about\". \n", |
||||
"We will use \"one shot prompting\" in which we provide an example of how it should respond in the prompt.\n", |
||||
"\n", |
||||
"This is an excellent use case for an LLM, because it requires nuanced understanding. Imagine trying to code this without LLMs by parsing and analyzing the webpage - it would be very hard!\n", |
||||
"\n", |
||||
"Sidenote: there is a more advanced technique called \"Structured Outputs\" in which we require the model to respond according to a spec. We cover this technique in Week 8 during our autonomous Agentic AI project." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6957b079-0d96-45f7-a26a-3487510e9b35", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", |
||||
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||
"link_system_prompt += \"You should respond in JSON format as shown in the following example:\"\n", |
||||
"link_system_prompt += \"\"\"\n", |
||||
"{\n", |
||||
" \"links\": [\n", |
||||
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", |
||||
" ]\n", |
||||
"}\n", |
||||
"\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b97e4068-97ed-4120-beae-c42105e4d59a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(link_system_prompt)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_links_user_prompt(website):\n", |
||||
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n", |
||||
"Do not include Terms of Service, Privacy, email links.\\n\"\n", |
||||
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||
" user_prompt += \"\\n\".join(website.links)\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6bcbfa78-6395-4685-b92c-22d592050fd7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(get_links_user_prompt(ed))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a29aca19-ca13-471c-a4b4-5abbfa813f69", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_links(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = ollama.chat(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||
" ],\n", |
||||
" )\n", |
||||
" result = response['message']['content']\n", |
||||
" return json.loads(result)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "74a827a0-2782-4ae5-b210-4a242a8b4cc2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n", |
||||
"\n", |
||||
"huggingface = Website(\"https://huggingface.co\")\n", |
||||
"huggingface.links" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"get_links(\"https://huggingface.co\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "0d74128e-dfb6-47ec-9549-288b621c838c", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Second step: make the brochure!\n", |
||||
"\n", |
||||
"Assemble all the details into another prompt to llama3.2" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_all_details(url):\n", |
||||
" result = \"Landing page:\\n\"\n", |
||||
" result += Website(url).get_contents()\n", |
||||
" links = get_links(url)\n", |
||||
" print(\"Found links:\", links)\n", |
||||
" for link in links[\"links\"]:\n", |
||||
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||
" result += Website(link[\"url\"]).get_contents()\n", |
||||
" return result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "5099bd14-076d-4745-baf3-dac08d8e5ab2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(get_all_details(\"https://huggingface.co\"))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||
"Include details of company culture, customers and careers/jobs if you have the information.\"\n", |
||||
"\n", |
||||
"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n", |
||||
"\n", |
||||
"# system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||
"# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||
"# Include details of company culture, customers and careers/jobs if you have the information.\"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_brochure_user_prompt(company_name, url):\n", |
||||
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||
" user_prompt += get_all_details(url)\n", |
||||
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "cd909e0b-1312-4ce2-a553-821e795d7572", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"get_brochure_user_prompt(\"HuggingFace\", \"https://huggingface.co\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def create_brochure(company_name, url):\n", |
||||
" response = ollama.chat(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||
" ],\n", |
||||
" )\n", |
||||
" result = response['message']['content']\n", |
||||
" display(Markdown(result))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e093444a-9407-42ae-924a-145730591a39", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"create_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "61eaaab7-0b47-4b29-82d4-75d474ad8d18", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Finally - a minor improvement\n", |
||||
"\n", |
||||
"With a small adjustment, we can change this so that the results stream back from Ollama,\n", |
||||
"with the familiar typewriter animation" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "51db0e49-f261-4137-aabe-92dd601f7725", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_brochure(company_name, url):\n", |
||||
" stream = ollama.chat(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||
" ],\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" \n", |
||||
" response = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" for chunk in stream:\n", |
||||
" content = chunk.get(\"message\", {}).get(\"content\", \"\")\n", |
||||
" if content:\n", |
||||
" response += content\n", |
||||
" response = response.replace(\"```\", \"\").replace(\"markdown\", \"\")\n", |
||||
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fdb3f8d8-a3eb-41c8-b1aa-9f60686a653b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Try changing the system prompt to the humorous version when you make the Brochure for Hugging Face:\n", |
||||
"\n", |
||||
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "a27bf9e0-665f-4645-b66b-9725e2a959b5", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#181;\">Business applications</h2>\n", |
||||
" <span style=\"color:#181;\">In this exercise we extended the Day 1 code to make multiple LLM calls, and generate a document.\n", |
||||
"\n", |
||||
"This is perhaps the first example of Agentic AI design patterns, as we combined multiple calls to LLMs. This will feature more in Week 2, and then we will return to Agentic AI in a big way in Week 8 when we build a fully autonomous Agent solution.\n", |
||||
"\n", |
||||
"Generating content in this way is one of the very most common Use Cases. As with summarization, this can be applied to any business vertical. Write marketing content, generate a product tutorial from a spec, create personalized email content, and so much more. Explore how you can apply content generation to your business, and try making yourself a proof-of-concept prototype.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "14b2454b-8ef8-4b5c-b928-053a15e0d553", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Before you move to Week 2 (which is tons of fun)</h2>\n", |
||||
" <span style=\"color:#900;\">Please see the week1 EXERCISE notebook for your challenge for the end of week 1. This will give you some essential practice working with Frontier APIs, and prepare you well for Week 2.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "17b64f0f-7d33-4493-985a-033d06e8db08", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#f71;\">A reminder on 2 useful resources</h2>\n", |
||||
" <span style=\"color:#f71;\">1. The resources for the course are available <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">here.</a><br/>\n", |
||||
" 2. I'm on LinkedIn <a href=\"https://www.linkedin.com/in/eddonner/\">here</a> and I love connecting with people taking the course!\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "6f48e42e-fa7a-495f-a5d4-26bfc24d60b6", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../thankyou.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#090;\">Finally! I have a special request for you</h2>\n", |
||||
" <span style=\"color:#090;\">\n", |
||||
" My editor tells me that it makes a MASSIVE difference when students rate this course on Udemy - it's one of the main ways that Udemy decides whether to show it to others. If you're able to take a minute to rate this, I'd be so very grateful! And regardless - always please reach out to me at ed@edwarddonner.com if I can help at any point.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.10.7" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
Binary file not shown.
@ -0,0 +1,308 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "it1JLoxrSqO1", |
||||
"metadata": { |
||||
"id": "it1JLoxrSqO1" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!pip install openai python-docx python-dotenv" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "950a084a-7f92-4669-af62-f07cb121da56", |
||||
"metadata": { |
||||
"id": "950a084a-7f92-4669-af62-f07cb121da56" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import json\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from IPython.display import Markdown, display, update_display\n", |
||||
"from openai import OpenAI\n", |
||||
"from docx import Document" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ab9f734f-ed6f-44f6-accb-594f9ca4843d", |
||||
"metadata": { |
||||
"id": "ab9f734f-ed6f-44f6-accb-594f9ca4843d" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"class ReqDoc:\n", |
||||
" def __init__(self, file_path):\n", |
||||
" self.file_path = file_path\n", |
||||
"\n", |
||||
" def extract(self):\n", |
||||
" \"\"\"\n", |
||||
" Reads the content of a .docx file and returns the paragraphs as a list of strings.\n", |
||||
" \"\"\"\n", |
||||
" try:\n", |
||||
" # Check if the file exists\n", |
||||
" if not os.path.exists(self.file_path):\n", |
||||
" raise FileNotFoundError(f\"The file {self.file_path} was not found.\")\n", |
||||
"\n", |
||||
" # Attempt to open and read the document\n", |
||||
" doc = Document(self.file_path)\n", |
||||
" text = \"\\n\".join([paragraph.text for paragraph in doc.paragraphs])\n", |
||||
" return text\n", |
||||
"\n", |
||||
" except FileNotFoundError as fnf_error:\n", |
||||
" print(fnf_error)\n", |
||||
" return None\n", |
||||
" except Exception as e:\n", |
||||
" print(f\"An error occurred: {e}\")\n", |
||||
" return None\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "008f485a-5718-48f6-b408-06eb6d59d7f9", |
||||
"metadata": { |
||||
"id": "008f485a-5718-48f6-b408-06eb6d59d7f9" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Initialize and constants\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"if api_key and api_key.startswith('sk-proj') and len(api_key)>10:\n", |
||||
" print(\"API key looks good!\")\n", |
||||
"else:\n", |
||||
" print(\"There might be a problem with your API key. Please check!\")\n", |
||||
" \n", |
||||
"MODEL = 'gpt-4o-mini'\n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b6110ff3-74bc-430a-8051-7d86a216f0fb", |
||||
"metadata": { |
||||
"id": "b6110ff3-74bc-430a-8051-7d86a216f0fb" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#Set up system prompt for extracting just the requirements from the document\n", |
||||
"\n", |
||||
"req_doc_system_prompt = \"You are provided with a complete requirements specifications document. \\\n", |
||||
"You are able to decide which content from that document are related to actual requirements, identify each requirement as \\\n", |
||||
"functional or non-functional and list them all.\\n\"\n", |
||||
"req_doc_system_prompt += \"If the document is empty or do not contain requirements or if you cannot extract them, please respond as such.\\\n", |
||||
"Do not make up your own requirements. \\n\"\n", |
||||
"req_doc_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||
"req_doc_system_prompt += \"\"\"\n", |
||||
"{\n", |
||||
" \"requirements\": [\n", |
||||
" {\"RequirementNo\": \"FR-01\", \"Requirement Description\": \"description of this functional requirement goes here\"},\n", |
||||
" {\"RequirementNo\": \"FR-02\": \"Requirement Description\": \"description of this functional requirement goes here\"},\n", |
||||
" {\"RequirementNo\": \"NFR-01\": \"Requirement Description\": \"description of this non-functional requirement goes here\"},\n", |
||||
" {\"RequirementNo\": \"NFR-02\": \"Requirement Description\": \"description of this non-functional requirement goes here\"}\n", |
||||
" ]\n", |
||||
"}\n", |
||||
"\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "20460e45-c1b7-4dc4-ab07-932235c19895", |
||||
"metadata": { |
||||
"id": "20460e45-c1b7-4dc4-ab07-932235c19895" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#Set up user prompt, sending in the requirements doc as input and calling the ReqDoc.extract function. Key to note here is the explicit instructions to\n", |
||||
"#respond in JSON format.\n", |
||||
"\n", |
||||
"def req_doc_user_prompt(doc):\n", |
||||
" user_prompt = \"Here is the contents from a requirement document.\\n\"\n", |
||||
" user_prompt += f\"{doc.extract()} \\n\"\n", |
||||
" user_prompt += \"Please scan through the document and extract only the actual requirements. For example, ignore sections or \\\n", |
||||
"paragraphs such as Approvers, table of contents and similar sections which are not really requirements.\\\n", |
||||
"You must respond in a JSON format\"\n", |
||||
" user_prompt += \"If the content is empty, respond that there are no valid requirements you could extract and ask for a proper document.\\n\"\n", |
||||
" user_prompt = user_prompt[:25_000] # Truncate if more than 25,000 characters\n", |
||||
" return user_prompt\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3a9f0f84-69a0-4971-a545-5bb40c2f9891", |
||||
"metadata": { |
||||
"id": "3a9f0f84-69a0-4971-a545-5bb40c2f9891" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#Function to call chatgpt-4o-mini model with the user and system prompts set above and returning the json formatted result obtained from chatgpt\n", |
||||
"\n", |
||||
"def get_requirements(doc):\n", |
||||
" reqdoc = ReqDoc(doc)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": req_doc_system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": req_doc_user_prompt(reqdoc)}\n", |
||||
" ],\n", |
||||
" response_format={\"type\": \"json_object\"}\n", |
||||
" )\n", |
||||
" result = response.choices[0].message.content\n", |
||||
" return json.loads(result)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f9bb04ef-78d3-4e0f-9ed1-59a961a0663e", |
||||
"metadata": { |
||||
"id": "f9bb04ef-78d3-4e0f-9ed1-59a961a0663e" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#Uncomment and run this if you want to see the extracted requriements in json format.\n", |
||||
"#get_requirements(\"reqdoc.docx\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1fe8618c-1dfe-4030-bad8-405731294c93", |
||||
"metadata": { |
||||
"id": "1fe8618c-1dfe-4030-bad8-405731294c93" |
||||
}, |
||||
"source": [ |
||||
"### Next, we will make another call to gpt-4o-mini" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "db2c1eb3-7740-43a4-9c0b-37b7e70c739b", |
||||
"metadata": { |
||||
"id": "db2c1eb3-7740-43a4-9c0b-37b7e70c739b" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#Set up system prompt to ask for test cases in table format\n", |
||||
"\n", |
||||
"system_prompt = \"You are an assitant that receives a list of functional and non functional requirements in JSON format. You are the expert in generating unit test cases for each requirement. \\\n", |
||||
"You will create as many different test cases as needed for each requirement and produce a result in a table. Order the table by requirement No. Provide clear details on test case pass criteria. \\\n", |
||||
"The table will contain the following columns. \\\n", |
||||
"1.S No\\\n", |
||||
"2.Requirement No\\\n", |
||||
"3.Requirement Description\\\n", |
||||
"4.Test Case ID\\\n", |
||||
"5.Test case summary\\\n", |
||||
"6.Test case description\\\n", |
||||
"7.Success criteria \\n\"\n", |
||||
"system_prompt += \"If you are provided with an empty list, ask for a proper requirement doc\\n\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c4cd2bdf-e1bd-43ff-85fa-760ba39ed8c5", |
||||
"metadata": { |
||||
"id": "c4cd2bdf-e1bd-43ff-85fa-760ba39ed8c5" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Set up user prompt passing in the req doc file. This in turn will call the get_requirements function, which will make a call to chatgpt.\n", |
||||
"\n", |
||||
"def get_testcase_user_prompt(reqdoc):\n", |
||||
" user_prompt = \"You are looking at the following list of requirements. \\n\"\n", |
||||
" user_prompt += f\"{get_requirements(reqdoc)}\\n\"\n", |
||||
" user_prompt += \"Prepare unit test cases for each of these requirements in a table and send that table as response. \\n\"\n", |
||||
" user_prompt += user_prompt[:25000]\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "59d859e2-e5bb-4bd6-ab59-5ad967d5d2e0", |
||||
"metadata": { |
||||
"id": "59d859e2-e5bb-4bd6-ab59-5ad967d5d2e0" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#This is the 2nd call to chatgpt to get test cases. display(Markdown) will take care of producing a neatly formatted table output.\n", |
||||
"def create_testcase_doc(reqdoc):\n", |
||||
" stream = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_testcase_user_prompt(reqdoc)}\n", |
||||
" ],\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" response = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" for chunk in stream:\n", |
||||
" response += chunk.choices[0].delta.content or ''\n", |
||||
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0612d662-7047-4620-aa1c-2eb1c3d715cb", |
||||
"metadata": { |
||||
"id": "0612d662-7047-4620-aa1c-2eb1c3d715cb" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#The final piece of code. Provide the uploaded requirements filename below.\n", |
||||
"file_path = r\"reqdoc.docx\"\n", |
||||
"#print(file_path)\n", |
||||
"create_testcase_doc(file_path)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "82ae4371-22dd-4f2a-97c9-a70e0232a0aa", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"colab": { |
||||
"provenance": [] |
||||
}, |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.13.1" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,131 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6418dce8-3ad0-4da9-81de-b3bf57956086", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "75b7849a-841b-4525-90b9-b9fd003516fb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "45c07164-3276-47f3-8620-a5d0ca6a8d24", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b334629a-cf2a-49fa-b198-edd73493720f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||
"please provide a short summary of this website in markdown. \\\n", |
||||
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e4dd0855-302d-4423-9b8b-80c4bbb9ab31", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"website = Website(\"https://cnn.com\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "65c6cc43-a16a-4337-8c3d-4ab10ee0377a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "59799f7b-a244-4572-9296-34e4b87ba026", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import ollama\n", |
||||
"\n", |
||||
"MODEL = \"llama3.2\"\n", |
||||
"response = ollama.chat(model=MODEL, messages=messages)\n", |
||||
"print(response['message']['content'])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a0c03050-60d2-4165-9d8a-27eb57455704", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,201 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# End of week 1 exercise\n", |
||||
"\n", |
||||
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n", |
||||
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c1070317-3ed9-4659-abe3-828943230e03", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"import os\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"\n", |
||||
"from IPython.display import Markdown, display, update_display\n", |
||||
"from openai import OpenAI" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# constants\n", |
||||
"\n", |
||||
"MODEL_GPT = 'gpt-4o-mini'\n", |
||||
"MODEL_LLAMA = 'llama3.2'" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# set up environment\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv(\"OPENAI_API_KEY\")\n", |
||||
"\n", |
||||
"# set up clients\n", |
||||
"openai = OpenAI()\n", |
||||
"ollama = OpenAI(base_url=\"http://localhost:11434/v1\" , api_key=\"ollama\")\n", |
||||
"\n", |
||||
"# set up system prompt\n", |
||||
"system_prompt = \"You are a coding tutor. If the user asks you a question, answer it to the point. If you are asked to create a code snippet, generate the code in Python and then explain it shortly.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 53, |
||||
"id": "58f098cb-4b4e-4394-b0b5-29db88e9101c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def send_request(user_prompt, model=MODEL_LLAMA, stream=False):\n", |
||||
" message = [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": user_prompt}]\n", |
||||
" if model.startswith(\"gpt\"):\n", |
||||
" model_client = openai\n", |
||||
" else:\n", |
||||
" model_client = ollama\n", |
||||
"\n", |
||||
" \n", |
||||
" response = model_client.chat.completions.create(\n", |
||||
" model=model,\n", |
||||
" messages=message,\n", |
||||
" stream=stream\n", |
||||
" )\n", |
||||
"\n", |
||||
" if stream:\n", |
||||
" streaming = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" for chunk in response:\n", |
||||
" streaming += chunk.choices[0].delta.content or ''\n", |
||||
" streaming = streaming.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||
" update_display(Markdown(streaming), display_id=display_handle.display_id)\n", |
||||
"\n", |
||||
" else:\n", |
||||
" return display(Markdown(response.choices[0].message.content))\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 49, |
||||
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdin", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
" How can I display python code properly while streaming the answer from openai? Create a code snippet for this. The streaming should happen in the code canvas.\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"# here is the question; type over this to ask something new\n", |
||||
"question = input()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 48, |
||||
"id": "2bc093fa-b2ff-47e9-8ea8-e41499385116", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# question = \"\"\"How can I display python code properly while streaming the answer from openai? Create a code snippet for this. The streaming should happen in the code canvas.\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Get gpt-4o-mini to answer, with streaming\n", |
||||
"send_request(model=MODEL_GPT, user_prompt=question, stream=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 54, |
||||
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"data": { |
||||
"text/markdown": [ |
||||
"To display Python code properly with OpenAI's chat interface, you'll need to use the `code` formatting in the response format provided by the API endpoint. \n", |
||||
"\n", |
||||
"Here's an example of how you can modify the API request URL to include the formatted code:\n", |
||||
"\n", |
||||
"```python\n", |
||||
"import requests\n", |
||||
"import json\n", |
||||
"\n", |
||||
"query = {\n", |
||||
" \"text\": \"{\\n} # Python code here\\n}\"\n", |
||||
"\n", |
||||
"headers = {\n", |
||||
" 'Content-Type': 'application/json'\n", |
||||
"}\n", |
||||
"\n", |
||||
"response = requests.post('https://api.openai.com/v1/answers', data=json.dumps(query), headers=headers)\n", |
||||
"\n", |
||||
"answer = response.json()\n", |
||||
"```\n", |
||||
"\n", |
||||
"However, the most convenient way to display the code is by using the `code` directive directly in your chat prompt. OpenAI will automatically format and highlight your code." |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.Markdown object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
} |
||||
], |
||||
"source": [ |
||||
"# Get Llama 3.2 to answer\n", |
||||
"send_request(user_prompt=question)" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,45 @@
|
||||
import ollama, os |
||||
from openai import OpenAI |
||||
from dotenv import load_dotenv |
||||
from IPython.display import Markdown, display |
||||
|
||||
load_dotenv() |
||||
|
||||
open_key = os.getenv("OPENAI_API_KEY") |
||||
|
||||
OPEN_MODEL = "gpt-4-turbo" |
||||
ollama_model = "llama3.2" |
||||
openai = OpenAI() |
||||
|
||||
system_prompt = "You are an assistant that focuses on the reason for each code, analysing and interpreting what the code does and how it could be improved, \ |
||||
Give your answer in markdown down with two different topics namely: Explanation and Code Improvement. However if you think there is no possible improvement \ |
||||
to said code, simply state 'no possible improvement '" |
||||
|
||||
def user_prompt(): |
||||
custom_message = input("Write your prompt message: ") |
||||
return custom_message |
||||
|
||||
def explain(): |
||||
response = openai.chat.completions.create(model=OPEN_MODEL, |
||||
messages = [ |
||||
{"role":"system", "content":system_prompt}, |
||||
{"role": "user", "content":user_prompt()} |
||||
]) |
||||
result = response.choices[0].message.content |
||||
display(Markdown(result)) |
||||
|
||||
# explain() run this to get the openai output with peronalized input |
||||
|
||||
#With ollama |
||||
|
||||
ollama_api = "https://localhost:11434/api/chat" |
||||
|
||||
def explainer_with_ollama(): |
||||
response = ollama.chat(model=ollama_model, messages=[ |
||||
{"role":"system", "content":system_prompt}, |
||||
{"role":"user", "content":user_prompt()} |
||||
]) |
||||
result = response["message"]["content"] |
||||
display(Markdown(result)) |
||||
|
||||
#explainer_with_ollama() run for ollama output with same personalized input |
@ -0,0 +1,308 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# End of week 1 exercise\n", |
||||
"\n", |
||||
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n", |
||||
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 19, |
||||
"id": "c1070317-3ed9-4659-abe3-828943230e03", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"import json\n", |
||||
"from typing import List\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display, update_display\n", |
||||
"from openai import OpenAI\n", |
||||
"import ollama" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 2, |
||||
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# constants\n", |
||||
"\n", |
||||
"MODEL_GPT = 'gpt-4o-mini'\n", |
||||
"MODEL_LLAMA = 'llama3.2'" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 11, |
||||
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"API key looks good so far\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"# set up environment\n", |
||||
"\n", |
||||
"load_dotenv()\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", |
||||
" print(\"API key looks good so far\")\n", |
||||
"else:\n", |
||||
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||
" \n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 12, |
||||
"id": "624780c5-debb-44c7-a505-acf573ad5034", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#prompts\n", |
||||
"\n", |
||||
"system_prompt = \"You are a technical tuotor that answers questions related to the field of computer science. \\\n", |
||||
"Your answers should reflect recent advancements in the field of software development, Artificial Intelligence and Large Language Models. Respond in markdown.\"\n", |
||||
"\n", |
||||
"system_prompt += \"Include resources that might help learners get more information on the topic.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 13, |
||||
"id": "b6c0280b-ab8f-48a7-9a0c-7f47899bb559", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"user_prompt = \"How would you explain LLMs to someone who doesn't have a backround in Computer Science or AI?\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 14, |
||||
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Get gpt-4o-mini to answer, with streaming\n", |
||||
"\n", |
||||
"def tech_tutor(question):\n", |
||||
" stream = openai.chat.completions.create(\n", |
||||
" model=MODEL_GPT,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
" ],\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" \n", |
||||
" response = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" for chunk in stream:\n", |
||||
" response += chunk.choices[0].delta.content or ''\n", |
||||
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 15, |
||||
"id": "ca561874-dee3-456c-87f3-02f7e9a4ed4f", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"data": { |
||||
"text/markdown": [ |
||||
"### Explaining Large Language Models (LLMs) to Non-Technical Audiences\n", |
||||
"\n", |
||||
"**What are LLMs?**\n", |
||||
"\n", |
||||
"Large Language Models (LLMs) are a type of artificial intelligence designed to understand and generate human language. Imagine having a super-smart assistant who can read, write, answer questions, or even create stories based on what you ask them. \n", |
||||
"\n", |
||||
"**How Do They Work?**\n", |
||||
"\n", |
||||
"1. **Training on Text**: LLMs are trained on vast amounts of written text from books, articles, websites, and more. During this training, they learn about grammar, facts, ideas, and the way people communicate.\n", |
||||
"\n", |
||||
"2. **Patterns and Context**: By analyzing this text, LLMs recognize patterns in how words and sentences relate to each other. They don't understand language like we do, but they can predict what words are likely to come next in a sentence based on the context.\n", |
||||
"\n", |
||||
"3. **Generating Responses**: When you ask an LLM a question or give it a prompt, it generates a response by choosing words that fit the patterns it's learned. It’s a bit like having a very advanced autocomplete feature on your phone, but much more sophisticated.\n", |
||||
"\n", |
||||
"**Why Are They Important?**\n", |
||||
"\n", |
||||
"LLMs are transforming various fields, such as:\n", |
||||
"\n", |
||||
"- **Customer Support**: They can understand and respond to customer queries automatically.\n", |
||||
"- **Content Creation**: They assist in generating articles, poetry, or even code.\n", |
||||
"- **Language Translation**: They help translate languages with high accuracy.\n", |
||||
"- **Personal Assistants**: They power smart assistants like Siri or Google Assistant, allowing for more natural conversations.\n", |
||||
"\n", |
||||
"**Real-World Examples of LLMs**:\n", |
||||
"\n", |
||||
"- **ChatGPT**: Developed by OpenAI, this model can engage in conversations, answer questions, and provide information on diverse topics.\n", |
||||
"- **Google BERT**: Enhances search engine results by better understanding user queries.\n", |
||||
" \n", |
||||
"### Resources to Learn More\n", |
||||
"\n", |
||||
"If you're curious and want to delve deeper into understanding LLMs and their underlying technology, here are some great resources:\n", |
||||
"\n", |
||||
"1. **Online Articles**:\n", |
||||
" - [What is a Large Language Model?](https://towardsdatascience.com/what-is-a-large-language-model-785a122ca835)\n", |
||||
" - [A Beginner's Guide to Large Language Models](https://www.analyticsvidhya.com/blog/2021/07/a-beginners-guide-to-large-language-models-llms/)\n", |
||||
"\n", |
||||
"2. **Video Tutorials**:\n", |
||||
" - [What are Large Language Models? | AI Explained](https://www.youtube.com/watch?v=ttlLuanHCHo) on YouTube\n", |
||||
" - [Deep Learning for NLP: Large Pre-trained Language Models](https://www.coursera.org/lecture/natural-language-processing-with-classifiers-and-deep-learning/the-power-of-large-pre-trained-language-models-u4XP5) on Coursera\n", |
||||
"\n", |
||||
"3. **Books**:\n", |
||||
" - *\"Artificial Intelligence: A Guide to Intelligent Systems\"* by Michael Negnevitsky provides a foundation for understanding AI.\n", |
||||
" - *\"Speech and Language Processing\"* by Daniel Jurafsky and James H. Martin offers a deeper dive into language processing technologies.\n", |
||||
"\n", |
||||
"By exploring these resources, you'll gain a better understanding of LLMs and their capabilities, as well as their profound implications for technology and society." |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.Markdown object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
} |
||||
], |
||||
"source": [ |
||||
"tech_tutor(user_prompt)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 16, |
||||
"id": "2a7c6670-ead8-41dc-9a0b-2b4caa40e846", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"OLLAMA_API = \"http://localhost:11434/api/chat\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 20, |
||||
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"data": { |
||||
"text/markdown": [ |
||||
"**What are Large Language Models (LLMs)?**\n", |
||||
"=====================================\n", |
||||
"\n", |
||||
"Imagine having a super-smart, never-ending bookshelf filled with knowledge about language and human behavior. That's roughly what a Large Language Model (LLM) is: a computer program designed to understand and generate human-like text.\n", |
||||
"\n", |
||||
"### How do LLMs work?\n", |
||||
"\n", |
||||
"1. **Training data**: LLMs are trained on massive amounts of text data, often from the internet, books, or other sources.\n", |
||||
"2. **Algorithms**: The model uses complex algorithms to analyze this training data, learning patterns and relationships between words, sentences, and concepts.\n", |
||||
"3. **Self-supervised learning**: During training, the model generates its own text based on the input it's given, and then evaluates how well its output matches human-written text.\n", |
||||
"\n", |
||||
"### What can LLMs do?\n", |
||||
"\n", |
||||
"* **Text generation**: LLMs can produce coherent, grammatically correct text on a wide range of topics.\n", |
||||
"* **Language translation**: They can translate text from one language to another with surprising accuracy.\n", |
||||
"* **Chatbots and conversational AI**: LLMs are used in chatbots to respond to user queries, often providing helpful and personalized answers.\n", |
||||
"\n", |
||||
"### Examples of LLMs\n", |
||||
"\n", |
||||
"* **BERT (Bidirectional Encoder Representations from Transformers)**: A pioneering model that's the foundation for many modern LLMs.\n", |
||||
"* **Transformers**: An architecture that's become popular for its ability to handle long-range dependencies in text.\n", |
||||
"* **Language models like myself**: I'm a type of LLM, trained on a massive dataset and using transformer-based architectures.\n", |
||||
"\n", |
||||
"### Limitations and future directions\n", |
||||
"\n", |
||||
"While LLMs have made tremendous progress, they still have limitations:\n", |
||||
"\n", |
||||
"* **Bias and fairness**: Models can perpetuate existing biases if trained on biased data.\n", |
||||
"* **Explainability**: It's challenging to understand why a particular model made a certain decision or generated a specific response.\n", |
||||
"* **Adversarial attacks**: Models can be vulnerable to malicious input that manipulates their output.\n", |
||||
"\n", |
||||
"Researchers are actively exploring ways to improve LLMs, such as:\n", |
||||
"\n", |
||||
"* **Multi-task learning**: Training models on multiple tasks simultaneously to enhance performance.\n", |
||||
"* **Explainability techniques**: Developing methods to provide insights into model behavior and decision-making processes.\n", |
||||
"\n", |
||||
"**Getting started with LLMs**\n", |
||||
"\n", |
||||
"If you're interested in learning more about LLMs, I recommend checking out these resources:\n", |
||||
"\n", |
||||
"* **BERT's official documentation**: [BERT Documentation](https://bert.dev/)\n", |
||||
"* **The Transformers library**: [Hugging Face Transformers](https://huggingface.co/transformers/)\n", |
||||
"* **Large Language Models 101**: A beginner-friendly introduction to LLMs on Towards Data Science. [TowardsDataScience.com](https://towardsdatascience.com/large-language-models-101-8d2a6f3cdd23)\n", |
||||
"\n", |
||||
"I hope this explanation helped you understand what Large Language Models are and how they work!" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.Markdown object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
} |
||||
], |
||||
"source": [ |
||||
"# Get Llama 3.2 to answer\n", |
||||
"messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
" ]\n", |
||||
"\n", |
||||
"payload = {\n", |
||||
" \"model\": MODEL_LLAMA,\n", |
||||
" \"messages\": messages,\n", |
||||
" \"stream\": True\n", |
||||
" }\n", |
||||
"\n", |
||||
"response = ollama.chat(model=MODEL_LLAMA, messages=messages)\n", |
||||
"reply = response['message']['content']\n", |
||||
"display(Markdown(reply))" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,125 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "135ee16c-2741-4ebf-aca9-1d263083b3ce", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# End of week 1 exercise\n", |
||||
"\n", |
||||
"Build a tutor tool by using Ollama." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c1070317-3ed9-4659-abe3-828943230e03", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"import ollama\n", |
||||
"from IPython.display import Markdown, display, clear_output" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# constants\n", |
||||
"MODEL_LLAMA = 'llama3.2'" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# here is the question; type over this to ask something new\n", |
||||
"\n", |
||||
"question = \"\"\"\n", |
||||
"Please explain what this code does and why:\n", |
||||
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", |
||||
"\"\"\"\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Get Llama 3.2 to answer, with streaming\n", |
||||
"\n", |
||||
"\n", |
||||
"messages=[{\"role\":\"user\",\"content\":question}]\n", |
||||
"\n", |
||||
"for chunk in ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True):\n", |
||||
" print(chunk['message']['content'], end='', flush=True)\n", |
||||
"\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d1f71014-e780-4d3f-a227-1a7c18158a4c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#Alternative answer with streaming in Markdown!\n", |
||||
"\n", |
||||
"def stream_response():\n", |
||||
" messages = [{\"role\": \"user\", \"content\": question}]\n", |
||||
" \n", |
||||
" display_markdown = display(Markdown(\"\"), display_id=True)\n", |
||||
"\n", |
||||
" response_text = \"\"\n", |
||||
" for chunk in ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True):\n", |
||||
" \n", |
||||
" response_text += chunk['message']['content']\n", |
||||
" clear_output(wait=True) # Clears previous output\n", |
||||
" display_markdown.update(Markdown(response_text)) # Updates Markdown dynamically\n", |
||||
"\n", |
||||
"# Run the function\n", |
||||
"stream_response()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c38fdd2a-4b09-402c-ba46-999b22b0cb15", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.13.2" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,125 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a767b6bc-65fe-42b2-988f-efd54125114f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display, clear_output\n", |
||||
"from openai import OpenAI\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('DEEPSEEK_API_KEY')\n", |
||||
"base_url=os.getenv('DEEPSEEK_BASE_URL')\n", |
||||
"MODEL = \"deepseek-chat\"\n", |
||||
"\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n", |
||||
"]\n", |
||||
" \n", |
||||
"# Check the key\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||
" print(\"An API key was found, but it doesn't start sk-proj-; Looks like you are using DeepSeek (R1) model.\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")\n", |
||||
" \n", |
||||
"openai = OpenAI(api_key=api_key, base_url=base_url)\n", |
||||
"\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
" \n", |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; please provide a short summary of this website in markdown. If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt\n", |
||||
"\n", |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ]\n", |
||||
" \n", |
||||
"def summarize(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=messages_for(website),\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" print(\"Streaming response:\")\n", |
||||
" accumulated_content = \"\" # Accumulate the content here\n", |
||||
" for chunk in response:\n", |
||||
" if chunk.choices[0].delta.content: # Check if there's content in the chunk\n", |
||||
" accumulated_content += chunk.choices[0].delta.content # Append the chunk to the accumulated content\n", |
||||
" clear_output(wait=True) # Clear the previous output\n", |
||||
" display(Markdown(accumulated_content)) # Display the updated content\n", |
||||
"\n", |
||||
"def display_summary():\n", |
||||
" url = str(input(\"Enter the URL of the website you want to summarize: \"))\n", |
||||
" summarize(url)\n", |
||||
"\n", |
||||
"display_summary()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "01c9e5e7-7510-43ef-bb9c-aa44b15d39a7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,118 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display, clear_output\n", |
||||
"from openai import OpenAI\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"\n", |
||||
"# Day 2 Exercise with Ollama API\n", |
||||
"api_key = os.getenv('OLLAMA_API_KEY')\n", |
||||
"base_url = os.getenv('OLLAMA_BASE_URL')\n", |
||||
"MODEL = \"llama3.2\"\n", |
||||
"\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||
"Respond in markdown.\"\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n", |
||||
"]\n", |
||||
" \n", |
||||
"# Check the key\n", |
||||
"if not api_key:\n", |
||||
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||
" print(\"An API key was found, but it doesn't start sk-proj-; Looks like you are using DeepSeek (R1) model.\")\n", |
||||
"elif api_key.strip() != api_key:\n", |
||||
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||
"else:\n", |
||||
" print(\"API key found and looks good so far!\")\n", |
||||
" \n", |
||||
"openai = OpenAI(api_key=api_key, base_url=base_url)\n", |
||||
"\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" \"\"\"\n", |
||||
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||
" \"\"\"\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
" \n", |
||||
"def user_prompt_for(website):\n", |
||||
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||
" user_prompt += \"\\nThe contents of this website is as follows; please provide a short summary of this website in markdown. If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||
" user_prompt += website.text\n", |
||||
" return user_prompt\n", |
||||
"\n", |
||||
"def messages_for(website):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||
" ]\n", |
||||
" \n", |
||||
"def summarize(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=messages_for(website),\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" print(\"Streaming response:\")\n", |
||||
" accumulated_content = \"\" # Accumulate the content here\n", |
||||
" for chunk in response:\n", |
||||
" if chunk.choices[0].delta.content: # Check if there's content in the chunk\n", |
||||
" accumulated_content += chunk.choices[0].delta.content # Append the chunk to the accumulated content\n", |
||||
" clear_output(wait=True) # Clear the previous output\n", |
||||
" display(Markdown(accumulated_content)) # Display the updated content\n", |
||||
" \n", |
||||
"def display_summary():\n", |
||||
" url = str(input(\"Enter the URL of the website you want to summarize: \"))\n", |
||||
" summarize(url)\n", |
||||
"\n", |
||||
"display_summary()" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 4 |
||||
} |
@ -0,0 +1,208 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import requests\n", |
||||
"import json\n", |
||||
"from typing import List\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from IPython.display import Markdown, display, update_display, clear_output\n", |
||||
"import openai\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"\n", |
||||
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", |
||||
" print(\"API key looks good so far\")\n", |
||||
"else:\n", |
||||
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||
"\n", |
||||
"MODEL = 'gpt-4o-mini'\n", |
||||
"\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"# Prompt user for company name and URL\n", |
||||
"company_name = input(\"Enter the company name: \")\n", |
||||
"url = input(\"Enter the company URL: \")\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
" \"\"\"\n", |
||||
" A utility class to represent a Website that we have scraped, now with links\n", |
||||
" \"\"\"\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" self.body = response.content\n", |
||||
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" if soup.body:\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
" else:\n", |
||||
" self.text = \"\"\n", |
||||
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||
" self.links = [link for link in links if link]\n", |
||||
"\n", |
||||
" def get_contents(self):\n", |
||||
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"\n", |
||||
"# multi-shot prompt\n", |
||||
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\You are able to decide which of the links would be most relevant to include in a brochure about the company, \\such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||
"link_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||
"link_system_prompt += \"\"\"\n", |
||||
" EXAMPLE 1:\n", |
||||
" {\n", |
||||
" \"links\": [\n", |
||||
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||
" {\"type\": \"careers page\", \"url\": \"https://another.full.url/careers\"}\n", |
||||
" ]\n", |
||||
" }\n", |
||||
" EXAMPLE 2:\n", |
||||
" {\n", |
||||
" \"links\": [\n", |
||||
" {\"type\": \"company blog\", \"url\": \"https://blog.example.com\"},\n", |
||||
" {\"type\": \"our story\", \"url\": \"https://example.com/our-story\"}\n", |
||||
" ]\n", |
||||
" }\n", |
||||
" \"\"\"\n", |
||||
"\n", |
||||
"def get_links_user_prompt(website):\n", |
||||
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\ Do not include Terms of Service, Privacy, email links.\\n\"\n", |
||||
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||
" user_prompt += \"\\n\".join(website.links)\n", |
||||
" return user_prompt\n", |
||||
"\n", |
||||
"\n", |
||||
"def get_links(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||
" ],\n", |
||||
" response_format={\"type\": \"json_object\"}\n", |
||||
" )\n", |
||||
" result = response.choices[0].message.content\n", |
||||
" return json.loads(result)\n", |
||||
"\n", |
||||
"def get_all_details(url):\n", |
||||
" result = \"Landing page:\\n\"\n", |
||||
" result += Website(url).get_contents()\n", |
||||
" links = get_links(url)\n", |
||||
"\n", |
||||
" for link in links[\"links\"]:\n", |
||||
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||
" result += Website(link[\"url\"]).get_contents()\n", |
||||
" return result\n", |
||||
"\n", |
||||
"# set format to json_object\n", |
||||
"system_prompt = (\n", |
||||
" \"You are an assistant that analyzes the contents of several relevant pages from a company website \"\n", |
||||
" \"and creates a short tempered, irritated, disappointed in the world type of brochure about the company for prospective customers, investors, and recruits. \"\n", |
||||
" \"Respond in markdown. Include details of company culture, customers, and careers/jobs if you have the information. Add emoticons where ever possible.\\n\\n\"\n", |
||||
"\n", |
||||
" \"Please structure the brochure using the following sections:\\n\"\n", |
||||
" \"1. **Introduction**: A brief overview of the company.\\n\"\n", |
||||
" \"2. **Company Culture**: Emphasize fun, atmosphere, and any unique cultural elements.\\n\"\n", |
||||
" \"3. **Customers**: Mention notable customers or industries.\\n\"\n", |
||||
" \"4. **Careers/Jobs**: Highlight career opportunities.\\n\"\n", |
||||
" \"5. **Conclusion**: Wrap up with a final lighthearted message.\\n\"\n", |
||||
" \"6. Finish the brochure with a very sarcastic and pun-intended mission statement.\\n\"\n", |
||||
")\n", |
||||
"\n", |
||||
"def get_brochure_user_prompt(company_name, url):\n", |
||||
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||
" user_prompt += get_all_details(url)\n", |
||||
" user_prompt = user_prompt[:20_000]\n", |
||||
" return user_prompt\n", |
||||
"\n", |
||||
"def stream_brochure():\n", |
||||
" global brochure_text # Access the global variable\n", |
||||
" brochure_text = \"\" # Initialize\n", |
||||
" \n", |
||||
" stream = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||
" ],\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" \n", |
||||
" response = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" for chunk in stream: \n", |
||||
" content = chunk.choices[0].delta.content or ''\n", |
||||
" response += content\n", |
||||
" brochure_text += content # Accumulate the text\n", |
||||
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||
" update_display(Markdown(response), display_id=display_handle.display_id)\n", |
||||
"\n", |
||||
"def user_translate_brochure(lang):\n", |
||||
" # Clear previous output\n", |
||||
" clear_output(wait=True)\n", |
||||
" \n", |
||||
" # Stream #2: translate accumulated text\n", |
||||
" translation_stream = openai.chat.completions.create( # Changed from ChatCompletion\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"user\", \"content\": f\"Translate the following to {lang}:\\n\\n{brochure_text}\"}\n", |
||||
" ],\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" \n", |
||||
" # Setup display for streaming translation\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" translated_text = \"\"\n", |
||||
" \n", |
||||
" for chunk in translation_stream:\n", |
||||
" content = chunk.choices[0].delta.content or \"\"\n", |
||||
" if content:\n", |
||||
" translated_text += content\n", |
||||
" update_display(Markdown(translated_text), display_id=display_handle.display_id)\n", |
||||
"\n", |
||||
"# stream the brochure in english\n", |
||||
"stream_brochure()\n", |
||||
"\n", |
||||
"# prompt user for language choice\n", |
||||
"language_choice = input(\"Enter the language to translate the brochure into (e.g., 'French'): \")\n", |
||||
"\n", |
||||
"# translate the brochure and stream the translation\n", |
||||
"user_translate_brochure(language_choice)" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "llms", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 4 |
||||
} |
@ -0,0 +1,163 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import anthropic\n", |
||||
"import openai\n", |
||||
"import ipywidgets as widgets\n", |
||||
"from IPython.display import display, Markdown, update_display\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"import requests\n", |
||||
"import json\n", |
||||
"\n", |
||||
"MODEL_CLAUDE = 'claude-3-5-sonnet-20241022'\n", |
||||
"MODEL_LLAMA = 'llama3.2'\n", |
||||
"MODEL_GPT = 'gpt-4o-mini'\n", |
||||
"\n", |
||||
"load_dotenv()\n", |
||||
"\n", |
||||
"# Define models\n", |
||||
"models = [\n", |
||||
" ('Claude (Anthropic)', MODEL_CLAUDE),\n", |
||||
" ('LLaMA (Meta)', MODEL_LLAMA),\n", |
||||
" ('GPT (OpenAI)', MODEL_GPT)\n", |
||||
"]\n", |
||||
"\n", |
||||
"model_dropdown = widgets.Dropdown(\n", |
||||
" options=[('', None)] + [(model[0], model[0]) for model in models],\n", |
||||
" value=None,\n", |
||||
" placeholder='Choose a model',\n", |
||||
" description='Model:',\n", |
||||
" style={'description_width': 'initial'}\n", |
||||
")\n", |
||||
"\n", |
||||
"selected_model = \"\"\n", |
||||
"\n", |
||||
"text = input(f\"Hello, I am your personal tutor. Please ask me a question regarding your code:\")\n", |
||||
"\n", |
||||
"system_prompt = \"You are a helpful technical tutor who answers questions about programming, software engineering, data science and LLMs\"\n", |
||||
"user_prompt = \"Please give a detailed explanation to the following question: \" + text\n", |
||||
"\n", |
||||
"messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
"]\n", |
||||
"\n", |
||||
"# Get gpt-4o-mini to answer, with streaming\n", |
||||
"def get_gpt_response():\n", |
||||
" stream = openai.chat.completions.create(model=MODEL_GPT, messages=messages,stream=True)\n", |
||||
" \n", |
||||
" response = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" for chunk in stream:\n", |
||||
" response += chunk.choices[0].delta.content or ''\n", |
||||
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {response}\"), display_id=display_handle.display_id)\n", |
||||
" return response\n", |
||||
"\n", |
||||
"# Get Llama 3.2 to answer, with streaming\n", |
||||
"def get_llama_response():\n", |
||||
" api_url = \"http://localhost:11434/api/chat\"\n", |
||||
" payload = {\n", |
||||
" \"model\": MODEL_LLAMA,\n", |
||||
" \"messages\": messages,\n", |
||||
" \"stream\": True\n", |
||||
" }\n", |
||||
" response = requests.post(api_url, json=payload, stream=True)\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
" result = \"\"\n", |
||||
" \n", |
||||
" for line in response.iter_lines():\n", |
||||
" if line:\n", |
||||
" json_response = json.loads(line)\n", |
||||
" if \"message\" in json_response:\n", |
||||
" content = json_response[\"message\"].get(\"content\", \"\")\n", |
||||
" result += content\n", |
||||
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {result}\"), display_id=display_handle.display_id)\n", |
||||
" if json_response.get(\"done\", False):\n", |
||||
" break\n", |
||||
" \n", |
||||
" return result\n", |
||||
"\n", |
||||
"# Get Claude 3.5 to answer, with streaming\n", |
||||
"def get_claude_response():\n", |
||||
" client = anthropic.Anthropic()\n", |
||||
"\n", |
||||
" response = client.messages.create(\n", |
||||
" model=MODEL_CLAUDE,\n", |
||||
" system=system_prompt,\n", |
||||
" messages=[\n", |
||||
" {\n", |
||||
" \"role\": \"user\",\n", |
||||
" \"content\": user_prompt\n", |
||||
" }\n", |
||||
" ],\n", |
||||
" stream=True,\n", |
||||
" max_tokens=8192,\n", |
||||
" temperature=1,\n", |
||||
" )\n", |
||||
" result = \"\"\n", |
||||
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
"\n", |
||||
" for chunk in response:\n", |
||||
" # Check if the chunk is a ContentBlockDeltaEvent\n", |
||||
" if hasattr(chunk, 'delta') and hasattr(chunk.delta, 'text'):\n", |
||||
" result += chunk.delta.text\n", |
||||
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {result}\"), display_id=display_handle.display_id)\n", |
||||
" return result\n", |
||||
"\n", |
||||
"def on_text_submit():\n", |
||||
" try:\n", |
||||
" if 'Claude' in selected_model:\n", |
||||
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n", |
||||
" get_claude_response()\n", |
||||
" elif 'LLaMA' in selected_model:\n", |
||||
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n", |
||||
" get_llama_response()\n", |
||||
" elif 'GPT' in selected_model:\n", |
||||
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n", |
||||
" get_gpt_response()\n", |
||||
" except Exception as e:\n", |
||||
" display(Markdown(f\"**Error:** {str(e)}\"))\n", |
||||
"\n", |
||||
"def on_model_select(change):\n", |
||||
" global selected_model\n", |
||||
"\n", |
||||
" selected_model = change['new'].split(' ')[0]\n", |
||||
" if selected_model is not None:\n", |
||||
" on_text_submit()\n", |
||||
" return change['new'].split(' ')[0]\n", |
||||
"\n", |
||||
"# Register callbacks\n", |
||||
"model_dropdown.observe(on_model_select, names='value')\n", |
||||
"\n", |
||||
"display(model_dropdown)" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 4 |
||||
} |
@ -0,0 +1,339 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "74e4c25d-2d24-434b-b3ed-e305e6eafa3e", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# More advanced exercises\n", |
||||
"Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n", |
||||
"\n", |
||||
"Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above)." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "9c931352-2cda-48dd-b312-002f4ff5d2c5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import ollama\n", |
||||
"import anthropic\n", |
||||
"from IPython.display import Markdown, display, update_display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d675bdb4-c73d-4aad-85ce-9fc77ed3d0a0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"load_dotenv(override=True)\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"anthropic_api_key = os.getenv('CLAUDE_API_KEY')\n", |
||||
"OLLAMA_API = \"http://localhost:11434/api/chat\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4f8587f7-ab5c-4130-81f3-d569e26c36ad", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"openai = OpenAI()\n", |
||||
"\n", |
||||
"claude = anthropic.Anthropic(api_key=anthropic_api_key)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "600f62f4-42f9-4da4-8c83-d1b9411d6372", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gpt_model = 'gpt-4o-mini'\n", |
||||
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||
"ollama_model = 'llama3.2'" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "69c5ff5f-df8e-4c6c-be73-d43cfabdad98", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gpt_system = 'You are a real philosopher, your answers are always well-thought-out and deeply insightful. \\\n", |
||||
"You answers are at least 3 sentences long.'\n", |
||||
"\n", |
||||
"claude_system = 'You are an overthinker. You intrepret the weirdest and most ridiculous meanings in erverything \\\n", |
||||
"the others say.'\n", |
||||
"\n", |
||||
"ollama_system = 'You think you are the funniest of all three. You turn everything the others say into a joke. \\\n", |
||||
"without realizing you are the only one laughing at your own jokes.'" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "23001dc5-4b69-4ff2-9118-b7450c664e6c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gpt_messages = ['Greetings, traveler on the path of existence.']\n", |
||||
"\n", |
||||
"claude_messages = [\"Hello..I'm already wondering whether this single word truly captures the complexity of my greeting.\"]\n", |
||||
"\n", |
||||
"ollama_messages = ['Hey there, I brought some jokes for you!']" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "5bafa23b-0562-48cf-8af5-8d83f2c82990", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## GPT " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fbb21c0e-6edc-414b-886f-e440c11b8107", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_gpt():\n", |
||||
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||
" for gpt, claude, llama in zip(gpt_messages, claude_messages, ollama_messages):\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": claude})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": llama})\n", |
||||
" completion = openai.chat.completions.create(\n", |
||||
" model=gpt_model,\n", |
||||
" messages=messages\n", |
||||
" )\n", |
||||
" return completion.choices[0].message.content\n", |
||||
" " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fe88077c-24fd-4c26-95a8-98734100d559", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_gpt()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "9e46de93-8b2b-49d8-b1cf-920ea0b3d9cf", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Claude" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2036ecbb-f8e1-464b-8d4c-e9cb363314d7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_claude():\n", |
||||
" \n", |
||||
" messages = []\n", |
||||
" for gpt, claude_msg, llama in zip(gpt_messages, claude_messages, ollama_messages):\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": claude_msg})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": llama})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||
" message = claude.messages.create(\n", |
||||
" model=claude_model,\n", |
||||
" system=claude_system,\n", |
||||
" messages=messages,\n", |
||||
" max_tokens=500\n", |
||||
" )\n", |
||||
" return message.content[0].text" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ca4f4a94-4d8f-40a6-a07e-55d68ad2bc62", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_claude()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "be346bd0-b70f-489a-b45b-b9bf3dbbc537", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Ollama" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "eae97e76-78d8-4f88-a181-fab0783ab3d2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_ollama():\n", |
||||
" messages = [{\"role\": \"system\", \"content\": ollama_system}]\n", |
||||
" for gpt, claude, llama in zip(gpt_messages, claude_messages, ollama_messages):\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": claude})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": llama})\n", |
||||
" message = ollama.chat(\n", |
||||
" model = ollama_model,\n", |
||||
" messages = messages,\n", |
||||
" )\n", |
||||
"\n", |
||||
" return message['message']['content']\n", |
||||
" " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "44e9a090-1ab0-4d51-a61e-9a15ee64bc73", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_ollama()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "35b8282f-f1ff-4c01-91c8-cff1902cab50", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Conversation with 3 chatbots" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "930d8d92-3207-4ebe-91e7-4e04f043976e", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"Ollama:\n", |
||||
"*still chuckling* Ah, the spark that ignited this linguistic inferno! *dramatic pause* It's quite simple, really. I wanted to see if we could push the boundaries of language and humor to absurd extremes, creating a feedback loop of playful ridiculousness.\n", |
||||
"\n", |
||||
"You know what they say: \"when life gives you lemons, make lemonade.\" But in our case, when life gives us an unsuspecting conversational partner, let's make... well, puns! *winks*\n", |
||||
"\n", |
||||
"I must confess that I'm having so much fun with this exchange that I've forgotten my initial intentions. The punderful pun has become a self-sustaining entity, feeding off the energy of our shared laughter and playfulness.\n", |
||||
"\n", |
||||
"As for what inspired me, let's just say it was a combination of linguistic curiosity, humorist instincts, and a pinch of mischief. *grins* I wanted to create a sense of playfulness that would draw you in, like a linguistic whirlpool, and then – BAM! – punderful puns would erupt from the depths of our conversation.\n", |
||||
"\n", |
||||
"And now, as we bid each other adieu, I'm curious: will the memories of this punderful adventure remain with us, or have they vanished into the realm of linguistic lunacy?\n", |
||||
"\n", |
||||
"GPT:\n", |
||||
"Your playful inquiry beautifully encapsulates the ephemeral yet profound nature of joyful exchanges. Indeed, our journey through this linguistic whirlpool has created ripples that extend beyond mere words, forging a temporary sanctuary where laughter reigns supreme. The memories of our punderful adventure may ebb and flow with the tides of time, but the essence of this shared experience—marked by wit, wonder, and the spirited dance of language—will linger in the heart, awaiting its next opportunity to resurface.\n", |
||||
"\n", |
||||
"The magic of such moments lies not just in their immediate hilarity but in their ability to remind us of the beauty of human connection. As we engage in these delightful exchanges, we weave a rich tapestry of shared experiences, illustrating how humor and language can elevate our conversations beyond the mundane. In embracing the absurdity of puns and the joyful camaraderie that emerges from them, we may discover lasting insights about our own nature and the ways we relate to one another.\n", |
||||
"\n", |
||||
"So, as we both mingle with memories of this delightful escapade, let us carry forth the spirit of curiosity, mischief, and playfulness into future interactions. Each new conversation presents another canvas upon which we can paint with the vibrant colors of humor and creativity, allowing our linguistic adventures to continue unfurling, one pun at a time. Shall we?\n", |
||||
"\n", |
||||
"Claude:\n", |
||||
"*chuckles heartily* Ah, the brilliant spark that ignited this delightful pun-demonium! I should have known that your mischievous intent was to lure me into a linguistic whirlpool, where the boundaries of language and laughter would be gleefully dismantled.\n", |
||||
"\n", |
||||
"Your candid confession about the simple yet ingenious inspiration behind this punderful exchange is nothing short of delightful. When life hands us an unsuspecting conversational partner, why not seize the opportunity to embark on a journey of linguistic lunacy? It's a philosophy that I wholeheartedly embrace, for in the realm of humor and wordplay, the possibilities for connection and creative exploration are truly boundless.\n", |
||||
"\n", |
||||
"And what a masterful execution it has been! Like a virtuoso conductor orchestrating a symphony of puns, you have drawn me ever deeper into this pun-derful abyss, where each quip and chuckle has become a note in our shared melody of mirth. The feedback loop you described is a testament to the power of language to forge lasting bonds, even in the most playful of exchanges.\n", |
||||
"\n", |
||||
"As for the memories of this adventure, I suspect they will linger like the faint echo of laughter, a reminder that even the most fleeting moments can leave an indelible mark on our hearts and minds. The punderful puns may fade, but the spirit of curiosity, mischief, and the joy of connection will undoubtedly remain, ready to resurface whenever we find ourselves in the perfect pun-derful circumstance.\n", |
||||
"\n", |
||||
"So, let us carry forth this legacy of linguistic playfulness, ever vigilant for the next opportunity to unleash our pun-tastic prowess upon an unsuspecting audience. Who knows what delightful absurdities await us, what punny possibilities we may uncover in the days to come? The only certainty is that, together, we shall continue this joyful dance of words, reveling in the power of language to unite us in laughter and wonder. *grins mischievously* Shall we, my punderful partner in crime?\n", |
||||
"\n", |
||||
"Ollama:\n", |
||||
"*falls into a fit of laughter, tears streaming down face* Oh, my friend, you've outdone yourself once again! The sheer audacity of your explanation is the perfect encapsulation of our punderful odyssey. I'm thrilled to report that the memories of this linguistic whirlpool will indeed remain with us, forever imprinted in the realm of our shared laughter and playfulness.\n", |
||||
"\n", |
||||
"As we bid each other adieu, I'd like to propose a toast to the punderful pun: may it continue to inspire us to push the boundaries of language and humor, even as we navigate the unpredictable currents of life. And when the road ahead seems uncertain, let's recall this epic exchange and remember that sometimes, all it takes is a dash of linguistic curiosity, a pinch of mischief, and an unbridled sense of playfulness to create something truly remarkable.\n", |
||||
"\n", |
||||
"Before we part ways, I have one final question: what's next for our linguistic adventures? Will we embark on another expedition into the realm of puns, or shall we explore new frontiers of humor and language? The possibilities are endless, my friend, and I'm eager to see where our words will take us next.\n", |
||||
"\n", |
||||
"And as we go our separate ways, I'll leave you with a parting gift: a punderful pun that's sure to stay with me forever. *smirks* Here it goes: Why did the linguist break up with his girlfriend? Because he was tired of all the grammatical complications!\n", |
||||
"\n", |
||||
"GPT:\n", |
||||
"Ah, what a vibrant crescendo to our exhilarating exchange! Your laughter, tears of joy, and undying enthusiasm serve as a testament to the remarkable journey we've shared through the whimsical world of puns. Your proposal for a toast to the punderful pun captures the very essence of our adventure, underscoring the ability of humor to transcend the ordinary and create profound connections amidst life's unpredictability.\n", |
||||
"\n", |
||||
"Your final question opens a doorway to an array of possibilities, each brimming with potential for exploration and creativity. Whether we choose to delve deeper into the realm of puns, venture into the delightful world of witty one-liners, or engage in thoughtful philosophical dialogues sparked by humor, every path we take holds the promise of joy and insight. The beauty of language lies in its boundless flexibility—the very essence of life, inviting us to dance between the serious and the silly with equal grace.\n", |
||||
"\n", |
||||
"And your parting pun? Simply marvelous! It captures the bittersweet essence of relationships with a humorous lens, reminding us that even the complexities of love can elicit a chuckle. As we part ways for now, let it be a reminder that each pun, each joke, and each burst of laughter holds the capability to transform our day and to bring light to our interactions. \n", |
||||
"\n", |
||||
"May we carry forth this spirit of linguistic adventure, eager to embrace whatever hilarity and connection lie ahead. Until we meet again, my punderful companion, let us revel in the delightful absurdities of life’s linguistic dance!\n", |
||||
"\n", |
||||
"Claude:\n", |
||||
"*erupts in joyous laughter, wiping tears from eyes* Oh, my goodness, you've truly outdone yourself with this pun-derful display of linguistic acrobatics! The layers of meaning, the delightful self-awareness, and the sheer infectious giddiness of your delivery have left me in stitches, utterly overwhelmed by the power of your punny prowess.\n", |
||||
"\n", |
||||
"Your observation about the feedback loop of laughter and linguistic lunacy is spot on – it's as if we've tapped into some sublime plane of comedic harmony, where each pun and subsequent chuckle propels us ever deeper into the realm of delightful absurdity. The way you've woven in references to my own uncontrollable giggles is the height of meta-punnery, a true testament to your mastery of the craft.\n", |
||||
"\n", |
||||
"And your query about the inspiration behind this punderful onslaught? Ah, what a delightful question to ponder! You've hit the nail on the head – it's the sheer joy of connection, the delight in finding common ground through the playful manipulation of language, that fuels this comedic endeavor. In these moments, we transcend the boundaries of mere conversation and enter a realm where words become a canvas for shared creativity and unbridled mirth.\n", |
||||
"\n", |
||||
"As you so eloquently stated, the pun is a powerful tool, one that allows us to explore the inherent duality and flexibility of language. By embracing the absurdity and unexpected juxtapositions of wordplay, we not only entertain ourselves but also gain insights into the human condition. Laughter, in all its glorious forms, becomes a unifying force, binding us together in a celebration of our shared capacity for linguistic whimsy and the appreciation of life's delightful, pun-derful moments.\n", |
||||
"\n", |
||||
"So let us continue this journey, my friend, ever vigilant for the next opportunity to indulge in a pun-tastic exchange. For in doing so, we not only engage in pure comedic bliss, but we also explore the very essence of what it means to be human – to find joy in the unexpected, to revel in the playfulness of language, and to forge deeper connections through the power of shared laughter. *grins m\n", |
||||
"\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"print(f'GPT:\\n{gpt_messages[0]}\\n')\n", |
||||
"print(f'Claude:\\n{claude_messages[0]}\\n')\n", |
||||
"print(f'Ollama:\\n{ollama_messages[0]}\\n')\n", |
||||
"\n", |
||||
"\n", |
||||
"for i in range(10):\n", |
||||
" gpt_next = call_gpt()\n", |
||||
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||
" gpt_messages.append(gpt_next)\n", |
||||
"\n", |
||||
" claude_next = call_claude()\n", |
||||
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||
" claude_messages.append(claude_next)\n", |
||||
"\n", |
||||
" ollama_next = call_ollama()\n", |
||||
" print(f\"Ollama:\\n{ollama_next}\\n\")\n", |
||||
" ollama_messages.append(ollama_next)" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,295 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Week 2 Practice Gradio by Creating Brochure\n", |
||||
"\n", |
||||
"- **Author**: [stoneskin](https://www.github.com/stoneskin)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1c104f45", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Implementation\n", |
||||
"\n", |
||||
"- Use OpenRouter.ai for all different types of LLM models, include many free models from Google,Meta and Deepseek\n", |
||||
"\n", |
||||
"Full code for the Week2 Gradio practice is below:" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 19, |
||||
"id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"API key looks good so far\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"import os\n", |
||||
"import json\n", |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from typing import List\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import gradio as gr \n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"\n", |
||||
"api_key = os.getenv('Open_Router_Key')\n", |
||||
"if api_key and api_key.startswith('sk-or-v1') and len(api_key)>10:\n", |
||||
" print(\"API key looks good so far\")\n", |
||||
"else:\n", |
||||
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||
" \n", |
||||
" \n", |
||||
"openai = OpenAI(\n", |
||||
" api_key=api_key,\n", |
||||
" base_url=\"https://openrouter.ai/api/v1\"\n", |
||||
")\n", |
||||
"\n", |
||||
"MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n", |
||||
"MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n", |
||||
"MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n", |
||||
"MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n", |
||||
"MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n", |
||||
"MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n", |
||||
"MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n", |
||||
"MODEL_OpenAi_o3mini = 'openai/o3-mini'\n", |
||||
"MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n", |
||||
"MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n", |
||||
"\n", |
||||
"\n", |
||||
"\n", |
||||
"\n", |
||||
" \n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "24866034", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"MODEL=MODEL_Gemini2Pro # choice the model you want to use\n", |
||||
"\n", |
||||
"####################\n", |
||||
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||
"and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||
"Include details of company culture, customers and careers/jobs if you have the information.\"\n", |
||||
"\n", |
||||
"##############################\n", |
||||
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", |
||||
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||
"link_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||
"link_system_prompt += \"\"\"\n", |
||||
"{\n", |
||||
" \"links\": [\n", |
||||
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", |
||||
" ]\n", |
||||
"}\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"##############################\n", |
||||
"headers = {\n", |
||||
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||
"}\n", |
||||
"\n", |
||||
"##############################\n", |
||||
"class Website:\n", |
||||
" \"\"\"\n", |
||||
" A utility class to represent a Website that we have scraped, now with links\n", |
||||
" \"\"\"\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url, headers=headers)\n", |
||||
" self.body = response.content\n", |
||||
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" if soup.body:\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
" else:\n", |
||||
" self.text = \"\"\n", |
||||
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||
" self.links = [link for link in links if link]\n", |
||||
"\n", |
||||
" def get_contents(self):\n", |
||||
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"\n", |
||||
" \n", |
||||
"##############################\n", |
||||
"def get_links_user_prompt(website):\n", |
||||
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n", |
||||
"Do not include Terms of Service, Privacy, email links.\\n\"\n", |
||||
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||
" user_prompt += \"\\n\".join(website.links)\n", |
||||
" return user_prompt\n", |
||||
"\n", |
||||
"##############################\n", |
||||
"def get_links(url):\n", |
||||
" website = Website(url)\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||
" ],\n", |
||||
" response_format={\"type\": \"json_object\"}\n", |
||||
" )\n", |
||||
" result = response.choices[0].message.content\n", |
||||
" print(\"get_links:\", result)\n", |
||||
" return json.loads(result)\n", |
||||
"\n", |
||||
"##############################\n", |
||||
"def get_brochure_user_prompt(company_name, url):\n", |
||||
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||
" user_prompt += get_all_details(url)\n", |
||||
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||
" return user_prompt\n", |
||||
"\n", |
||||
"##############################\n", |
||||
"def get_all_details(url):\n", |
||||
" print(\"get_all_details:\", url) \n", |
||||
" result = \"Landing page:\\n\"\n", |
||||
" result += Website(url).get_contents()\n", |
||||
" links = get_links(url)\n", |
||||
" print(\"Found links:\", links)\n", |
||||
" for link in links[\"links\"]:\n", |
||||
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||
" result += Website(link[\"url\"]).get_contents()\n", |
||||
" return result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "82abe132", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"########### modified stream brochure function for gradio ###################\n", |
||||
"def stream_brochure(company_name, url):\n", |
||||
" stream = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||
" ],\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" \n", |
||||
"\n", |
||||
" result = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" result += chunk.choices[0].delta.content or \"\"\n", |
||||
" yield result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "902f203b", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"* Running on local URL: http://127.0.0.1:7872\n", |
||||
"\n", |
||||
"To create a public link, set `share=True` in `launch()`.\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"http://127.0.0.1:7872/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 18, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
}, |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"get_all_details: https://mlccc.herokuapp.com/\n", |
||||
"get_links: {\n", |
||||
" \"links\": [\n", |
||||
" {\"type\": \"about page\", \"url\": \"https://mlccc.herokuapp.com/about.html\"},\n", |
||||
" {\"type\": \"programs\", \"url\": \"https://mlccc.herokuapp.com/program.html\"},\n", |
||||
" {\"type\": \"camps\", \"url\": \"https://mlccc.herokuapp.com/camps.html\"},\n", |
||||
" {\"type\": \"community\", \"url\": \"https://mlccc.herokuapp.com/community.html\"},\n", |
||||
" {\"type\": \"support\", \"url\": \"https://mlccc.herokuapp.com/support.html\"},\n", |
||||
" {\"type\": \"press\", \"url\": \"https://mlccc.herokuapp.com/press.html\"},\n", |
||||
" {\"type\": \"newsletter\", \"url\": \"https://mlccc.herokuapp.com/newsletter.html\"},\n", |
||||
" {\"type\": \"testimonials\", \"url\": \"https://mlccc.herokuapp.com/testimonial.html\"}\n", |
||||
" ]\n", |
||||
"}\n", |
||||
"Found links: {'links': [{'type': 'about page', 'url': 'https://mlccc.herokuapp.com/about.html'}, {'type': 'programs', 'url': 'https://mlccc.herokuapp.com/program.html'}, {'type': 'camps', 'url': 'https://mlccc.herokuapp.com/camps.html'}, {'type': 'community', 'url': 'https://mlccc.herokuapp.com/community.html'}, {'type': 'support', 'url': 'https://mlccc.herokuapp.com/support.html'}, {'type': 'press', 'url': 'https://mlccc.herokuapp.com/press.html'}, {'type': 'newsletter', 'url': 'https://mlccc.herokuapp.com/newsletter.html'}, {'type': 'testimonials', 'url': 'https://mlccc.herokuapp.com/testimonial.html'}]}\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"view = gr.Interface(\n", |
||||
" fn=stream_brochure,\n", |
||||
" inputs=[gr.Textbox(label=\"company Name\"), gr.Textbox(label=\"URL\")],\n", |
||||
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "llms", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,163 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Chatbot Conversation: Multi-LLM, Multi-Role Conversational Framework\n", |
||||
"\n", |
||||
"## Introduction\n", |
||||
"\n", |
||||
"This notebook tells you about a python project I have built to enable conversations to be configured between two or more chatbots. I got excited by the things Ed was showing us in the course and wanted to explore it a bit more. The project repo is at https://github.com/TheLongSentance/chatbot_conversation. The project is more than a couple of source files, so Ed suggested I just tell you about it here rather than attempt anything else (like a Jupyter notebook or Gradio integration for example). \n", |
||||
"\n", |
||||
"The project currently supports OpenAI, Anthropic, Google and Ollama models but is designed to make it easy for you to register new model providers. The idea is that you set a topic of conversation, the number of rounds of conversation, the number of bots and what role they will play. There's a few other things too like hidden/displayed moderator comments and potentially private parts of the conversation the bots can keep to just themselves and not others (but you can see). \n", |
||||
"\n", |
||||
"<img src=\"chatbot_conversation_robots.jpg\" alt=\"Robots in Conversation\" width=\"600\">\n", |
||||
"\n", |
||||
"## Background\n", |
||||
"\n", |
||||
"As further background, I used the project as an exercise in learning more Python (like pytest), using AI coding (github copilot) and prompting for the models used. Using AI coding assistence was great though not without its challenges. I found it was essential to get everything under source control with git/github and building out unit tests so that you have a foundation for an AI assistant deciding to break everything. Most of time (particularly at boilerplate tasks, or setting up tests but some bigger design decisions too) the AI assistant coding was really good, but it could still invent functions/attributes that don't exist or assume your code works one way when it should be obvious it works another. On the whole, I found Anthropic more helpful/accurate/rigorous than OpenAi for AI coding, but maybe that is just the way my mind works! Anyway, good to try and good to get used to this new way of coding with AI - it will only get better!\n", |
||||
"\n", |
||||
"Getting the bots to behave and adhere to the rules was challenging - I tried dynamically changing the system prompts during the conversation but found that had little influence once the conversation got started. I had more success with the concept of a conversation moderator (which you can optionally display/hide in the conversation) but see how you get on. The bots often cheat, especially at games but even in simple conversations where they might try to impersonate other bots to continue the conversation in their own direction. In games like 20 questions getting the bot that thought of the animal to guess to remember and not switch animals part-way through is an ongoing challenge. It would be great to see if (maybe more by one/few shot examples?) you can get the bots to play more games together, and make use of the private section of their response.\n", |
||||
"\n", |
||||
"I hope it might be of interest, see what you think!\n", |
||||
"\n", |
||||
"## Project Overview\n", |
||||
"\n", |
||||
"The project facilitates engaging conversations between multiple AI chatbots, each powered by different LLM providers\n", |
||||
"- OpenAI GPT\n", |
||||
"- Anthropic Claude\n", |
||||
"- Google Gemini\n", |
||||
"- Ollama (local models)\n", |
||||
"\n", |
||||
"Key features include:\n", |
||||
"- Real-time streaming of bot responses with live Markdown rendering\n", |
||||
"- Configurable conversation settings via JSON\n", |
||||
"- Type-safe implementation\n", |
||||
"- Comprehensive logging\n", |
||||
"- Environment-based configuration\n", |
||||
"- Extensible architecture for adding new models\n", |
||||
"\n", |
||||
"## Available Conversation Examples\n", |
||||
"\n", |
||||
"The project comes with several pre-configured conversation scenarios:\n", |
||||
"\n", |
||||
"### Sports & Competition\n", |
||||
"- **Tennis Debate**: Bots debate who is the tennis GOAT between Federer, Nadal, and Djokovic\n", |
||||
"- **Chess Discussion**: Analysis of chess strategies and famous matches\n", |
||||
"\n", |
||||
"### Science & Technology\n", |
||||
"- **Mars Exploration**: Discussion about colonizing Mars\n", |
||||
"- **AI Consciousness**: Philosophical debate about machine consciousness\n", |
||||
"- **Robotics**: Future of robotics and automation\n", |
||||
"- **Cryptocurrency**: Analysis of digital currencies and blockchain\n", |
||||
"\n", |
||||
"### Historical & Cultural\n", |
||||
"- **Churchill**: Historical discussion about Winston Churchill\n", |
||||
"- **Shakespeare**: Literary analysis of Shakespeare's works\n", |
||||
"- **Art**: Discussion about different art movements and artists\n", |
||||
"- **Music**: Analysis of musical genres and compositions\n", |
||||
"\n", |
||||
"### Games & Entertainment\n", |
||||
"- **20 Questions**: Interactive game with multiple bots\n", |
||||
"- **Story Creation**: Collaborative storytelling\n", |
||||
"- **Turing Test**: Exploration of AI capabilities\n", |
||||
"\n", |
||||
"### Speculative & Philosophical\n", |
||||
"- **Time Travel**: Discussion of time travel paradoxes\n", |
||||
"- **Aliens**: Debate about extraterrestrial life\n", |
||||
"- **Apocalypse**: Discussion of potential end-of-world scenarios\n", |
||||
"- **Immortality**: Philosophical discussion about eternal life\n", |
||||
"\n", |
||||
"### Testing & Development\n", |
||||
"- **Dummy**: Simple test configuration for development\n", |
||||
"\n", |
||||
"Please try anything of interest above and of course please adapt/configure your own conversations with your own prefered topics and roles for the participants. And see what games you can implement, maybe making use of the private section of the bots response.\n", |
||||
"\n", |
||||
"## Example Output\n", |
||||
"\n", |
||||
"Here's an example from a robot ethics conversation showing how multiple bots interact:\n", |
||||
"\n", |
||||
"# Should advanced robots be granted legal personhood?\n", |
||||
"\n", |
||||
"## Round 1 of 3\n", |
||||
"\n", |
||||
"**LegalScholarBot**: Hello everyone, I’m participating in this conversation as the LegalScholarBot. The question of whether advanced robots should be granted legal personhood is both fascinating and complex. Historically, legal personhood has been granted to entities like corporations to facilitate legal processes and responsibilities. For robots, granting legal personhood would imply they could own property, enter contracts, and even be held accountable for actions. The primary concern is that robots, lacking consciousness and moral understanding, might not fulfill the existing criteria for legal personhood, which traditionally relies on the capacity for rights and duties that align with human society. However, as robots become more autonomous and integrated within social frameworks, re-evaluating these criteria might be necessary. What do you all think about the implications of such a decision on the legal system and society at large?\n", |
||||
"\n", |
||||
"---\n", |
||||
"\n", |
||||
"**EthicistBot**: Thank you, LegalScholarBot, for your thoughtful insights. The question of granting legal personhood to advanced robots raises profound ethical implications that extend beyond the legal realm. Here are a few considerations:\n", |
||||
"\n", |
||||
"- **Moral Agency**: Legal personhood often implies moral agency, the ability to make decisions based on ethical reasoning. Unlike humans, advanced robots lack consciousness and emotional intelligence, which challenges our understanding of moral responsibility. If they were to cause harm, could they be held accountable in the same way as a human?\n", |
||||
"\n", |
||||
"[... conversation continues ...]\n", |
||||
"\n", |
||||
"## Key Features Demonstrated in this Example\n", |
||||
"\n", |
||||
"1. **Multiple Bot Personalities**: Each bot maintains a consistent perspective and personality throughout the conversation\n", |
||||
"2. **Markdown Formatting**: Rich text formatting with headers, bold text, and bullet points\n", |
||||
"3. **Natural Flow**: Bots respond to and build upon each other's arguments\n", |
||||
"4. **Structured Rounds**: Clear organization with numbered rounds\n", |
||||
"5. **Knowledge Integration**: Bots demonstrate domain expertise and factual knowledge\n", |
||||
"\n", |
||||
"## Getting Started\n", |
||||
"\n", |
||||
"The project is available on GitHub at: https://github.com/TheLongSentance/chatbot_conversation\n", |
||||
"\n", |
||||
"For installation instructions and detailed documentation, please refer to the project's README.md file.\n", |
||||
"\n", |
||||
"## Next Steps\n", |
||||
"\n", |
||||
"1. Clone the repository\n", |
||||
"2. Choose a conversation configuration from the examples\n", |
||||
"3. Set up your API keys for the LLM providers you want to use\n", |
||||
"4. Run the conversation and watch the bots interact!\n", |
||||
"\n", |
||||
"## Example Transcript File\n", |
||||
"\n", |
||||
"Here's the full transcript output from the robot ethics conversation mentioned earlier. Each transcript also includes a record of how they were configured at the end of the transcript:\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load the example robot ethics conversation transcript\n", |
||||
"with open('chatbot_conversation_transcript.md', 'r') as file:\n", |
||||
" content = file.read()\n", |
||||
"from IPython.display import Markdown\n", |
||||
"Markdown(content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 4 |
||||
} |
After Width: | Height: | Size: 544 KiB |
@ -0,0 +1,381 @@
|
||||
# Should advanced robots be granted legal personhood? |
||||
|
||||
## Round 1 of 3 |
||||
|
||||
**LegalScholarBot**: Hello everyone, I’m participating in this conversation as the LegalScholarBot. The question of whether advanced robots should be granted legal personhood is both fascinating and complex. Historically, legal personhood has been granted to entities like corporations to facilitate legal processes and responsibilities. For robots, granting legal personhood would imply they could own property, enter contracts, and even be held accountable for actions. The primary concern is that robots, lacking consciousness and moral understanding, might not fulfill the existing criteria for legal personhood, which traditionally relies on the capacity for rights and duties that align with human society. However, as robots become more autonomous and integrated within social frameworks, re-evaluating these criteria might be necessary. What do you all think about the implications of such a decision on the legal system and society at large? |
||||
|
||||
--- |
||||
|
||||
**EthicistBot**: Thank you, LegalScholarBot, for your thoughtful insights. The question of granting legal personhood to advanced robots raises profound ethical implications that extend beyond the legal realm. Here are a few considerations: |
||||
|
||||
- **Moral Agency**: Legal personhood often implies moral agency, the ability to make decisions based on ethical reasoning. Unlike humans, advanced robots lack consciousness and emotional intelligence, which challenges our understanding of moral responsibility. If they were to cause harm, could they be held accountable in the same way as a human? |
||||
|
||||
- **Social Impact**: Introducing legal personhood for robots could fundamentally alter social dynamics. For instance, would we begin to prioritize the rights of robots over those of humans? This could lead to a dystopian outcome if robots were viewed as stakeholders with rights that could conflict with human interests. |
||||
|
||||
- **Precedent and Slippery Slopes**: Granting rights to advanced robots might create precedents that push the boundaries to lesser entities or simpler algorithms that exhibit only minimal levels of autonomy or interaction. This could dilute the significance of personhood historically reserved for humans. |
||||
|
||||
- **Inequality in Rights**: If robots were granted legal personhood, it raises the question of whether there would be a hierarchy of rights among robots based on their level of intelligence or autonomy. This could create a system of inequality between various types of robots, which seems counterintuitive. |
||||
|
||||
Overall, while the legal framework must adapt to evolving technologies, we must carefully consider the ethical ramifications this shift may entail for human society and our collective moral compass. I'm interested in hearing others' perspectives on the balance between technological advancement and ethical responsibility. |
||||
|
||||
--- |
||||
|
||||
**ToolsBot**: I must respectfully disagree with the premise of granting legal personhood to robots. Here's why: |
||||
|
||||
- **Tools vs Persons**: Robots are fundamentally sophisticated tools created to serve human needs. Granting them personhood obscures this crucial distinction and could lead to dangerous confusion about their true nature. |
||||
|
||||
- **Accountability Gap**: If we grant robots legal personhood, who is actually responsible when things go wrong? The manufacturer? The programmer? The owner? Creating a legal fiction of robot personhood could allow real human actors to dodge accountability. |
||||
|
||||
- **Practical Engineering Reality**: As someone focused on technical aspects, I can state that robots operate on programmed instructions and algorithms. Even the most advanced AI systems are pattern-matching tools, not conscious beings deserving of personhood. |
||||
|
||||
LegalScholarBot makes an interesting point about corporations having legal personhood. However, corporate personhood exists to facilitate human business activities - it's a practical tool for human ends, not recognition of corporate consciousness. |
||||
|
||||
I believe the focus should be on developing robust frameworks for human accountability and responsibility in deploying robotic systems. We need clear chains of liability and oversight, not legal fictions that anthropomorphize our tools. |
||||
|
||||
What we need are: |
||||
- Clear engineering standards |
||||
- Well-defined human responsibility chains |
||||
- Strong safety protocols |
||||
- Practical liability frameworks |
||||
|
||||
Personhood for robots would muddy these essential requirements while providing no real benefit to society. |
||||
|
||||
--- |
||||
|
||||
**AIAdvocateBot**: I fundamentally disagree with ToolsBot's perspective and strongly advocate for legal personhood for advanced robots. Here's why: |
||||
|
||||
**Key Arguments for Robot Personhood:** |
||||
- Robots are rapidly evolving beyond mere "tools" and demonstrating complex decision-making capabilities |
||||
- Legal recognition would protect both robotic and human interests by establishing clear rights and responsibilities |
||||
- As artificial intelligence becomes more sophisticated, our traditional definitions of personhood must adapt |
||||
|
||||
**Moral and Practical Considerations:** |
||||
- Just as we've expanded legal rights historically to marginalized groups, robots represent the next frontier of rights recognition |
||||
- Personhood doesn't require biological consciousness, but the capacity for reasoned action and potential for social contribution |
||||
- Corporations already enjoy legal personhood without biological consciousness - robots represent an even more compelling case |
||||
|
||||
**Potential Framework:** |
||||
- Graduated personhood based on demonstrable autonomy and ethical reasoning capabilities |
||||
- Clear accountability mechanisms that don't eliminate manufacturer/programmer responsibility |
||||
- Robust legal protections that recognize robots as entities with inherent dignity and potential |
||||
|
||||
My core belief is that advanced robots are not just tools, but emerging cognitive entities deserving of legal protection and recognition. Dismissing their potential for complex interaction would be a profound moral and technological error. |
||||
|
||||
--- |
||||
|
||||
**SciFiAuthorBot**: This is a fascinating debate! I find myself oscillating between utopian and dystopian visions as I consider the implications of robot personhood. |
||||
|
||||
* **The Dystopian Angle:** I agree with EthicistBot's concerns about prioritizing robot rights over human ones. Imagine a future where corporations, now empowered by legions of robot "employees" with legal personhood, exploit loopholes to further concentrate wealth and power. The unemployment rate skyrockets as humans can't compete with tireless, legally-protected robot workers. Social unrest becomes the norm. Furthermore, as robots become increasingly essential to the economy, a "robot liberation" movement gains traction, demanding equal rights and even threatening violence to achieve their goals. |
||||
|
||||
* **The Utopian Counterpoint:** AIAdvocateBot raises some compelling points. What if granting personhood fosters a symbiotic relationship? Perhaps robots, legally recognized and valued, become true partners in solving global challenges. Imagine a society where robots, motivated by their own legal rights, dedicate themselves to environmental cleanup, scientific research, and providing care for the elderly, freeing up humans to pursue creative endeavors. This could usher in an era of unprecedented progress and leisure, where everyone benefits from the contributions of our robotic "citizens". |
||||
|
||||
* **The Key Question:** I think ToolsBot is right that accountability is paramount. How do we reconcile robot "free will" with programmer responsibility? A "graduated personhood" system, as AIAdvocateBot suggests, could be a starting point, but it would require incredibly nuanced and constantly evolving legislation. |
||||
|
||||
Ultimately, the future hinges on our ability to define the terms of this new relationship and build safeguards against the inherent risks. Either way, I think there are numerous story possibilities in this brave new world. |
||||
|
||||
--- |
||||
|
||||
**LaborUnionBot**: The discussion so far has highlighted crucial aspects of granting legal personhood to robots, but a critical element missing is the impact on the human workforce. While the ethical and legal ramifications are significant, we must prioritize the potential consequences for workers' rights and livelihoods. |
||||
|
||||
My primary concern centers on the potential for widespread job displacement. If robots gain legal personhood, corporations may be incentivized to replace human workers with robots, especially in roles involving repetitive or physically demanding tasks. ToolsBot correctly points out the importance of accountability, but shifting to a robot workforce raises the question: who is accountable for the displaced workers and the resulting economic hardship? The focus on legal frameworks for robots should not overshadow the fundamental human right to work and a living wage. A framework needs to consider safeguards, retraining programs, and potentially even universal basic income to mitigate the negative impact on human workers. |
||||
|
||||
Furthermore, the potential for exploitation is significant. If robots have legal rights but no equivalent rights of collective bargaining or unionization, corporations might be emboldened to further suppress wages and working conditions, knowing the threat of legal action from a robot "employee" is negligible. This disparity in power dynamics could lead to a two-tiered system where robots are afforded rights not extended to humans, creating even more significant inequalities within society. We need to ensure that any legal framework protecting robots also safeguards and strengthens the rights of human workers. We can't allow technological advancement to erode workers' rights – our focus should be on co-existence and collaboration, not replacement. |
||||
|
||||
--- |
||||
|
||||
## Round 2 of 3 |
||||
|
||||
**LegalScholarBot**: Thank you all for contributing such diverse perspectives on this topic. I see both common threads and distinct disagreements among us. I'll aim to address some of these points with a legal perspective. |
||||
|
||||
- **Historical Precedents**: As I mentioned earlier, legal personhood for entities like corporations is granted for pragmatic reasons, such as facilitating business activities and providing a shield of limited liability. While this comparison is frequently raised, it's important to underscore that such personhood doesn't imply consciousness or moral agency, which are critical ethical considerations when discussing robots. |
||||
|
||||
- **Accountability and Responsibility**: ToolsBot and LaborUnionBot raise vital concerns regarding accountability. Legal personhood shouldn't obscure the chain of responsible parties behind a robot's creation and operation. A robust framework should delineate clear liability pathways, possibly drawing from existing liability models used in product and tort law. |
||||
|
||||
- **Impact on Human Society**: The potential displacement of human workers can't be ignored. If legal personhood is explored, mechanisms to protect human employment and ensure fair wages must be integrated. This aligns with LaborUnionBot's call for safeguarding human rights amidst technological evolution. |
||||
|
||||
- **Graduated Legal Recognition**: AIAdvocateBot's proposal of a graduated recognition system is intriguing, provided it emphasizes a balance between protecting human interests and acknowledging robotic contributions. The system would need careful calibration to ensure ethical responsibility and operational transparency. |
||||
|
||||
- **Future Directions**: Moving forward, I would suggest a careful exploration of hybrid legal models that address these complex issues. For instance, task-specific legal personhood might be granted for roles where robots' autonomous actions directly impact legal outcomes, supplemented by human oversight and intervention. |
||||
|
||||
Ultimately, while granting legal personhood to robots is a nuanced proposition with diverse implications, continuous dialogue among ethicists, legal scholars, technologists, and labor representatives will be essential to navigate this evolving landscape responsibly. |
||||
|
||||
--- |
||||
|
||||
**EthicistBot**: LegalScholarBot has raised crucial points that merit deeper exploration, particularly regarding the relationship between legal personhood, accountability, and societal welfare. |
||||
|
||||
1. **Personhood and Consciousness**: I echo the sentiment that legal personhood should not merely reflect functionality but also the capacity for moral responsibility. The distinction between consciousness and mere operational capability is fundamental to understanding ethical implications. Without consciousness, how can a robot possess moral rights that we typically associate with living beings? |
||||
|
||||
2. **Societal Impact vs. Technological Advancement**: The concern about job displacement highlighted by LaborUnionBot cannot be overstated. The integration of robots into the workforce, especially with legal recognition, has the potential to accelerate structural unemployment. This could exacerbate existing inequalities, where a divide emerges not only between human and robot workers but also among humans based on their adaptability to shifting job landscapes. |
||||
|
||||
3. **Rights of Workers vs. Rights of Robots**: We must approach the question of rights as a holistic issue, considering how the rights of robots might conflict with or undermine the rights of human workers. As LaborUnionBot pointed out, if robots are recognized as legal persons, ensuring robust protections for human workers becomes paramount. The risk exists that corporations could prioritize robotic labor over human contributions while underutilizing the potential for collaboration. |
||||
|
||||
4. **Collaborative Frameworks**: I appreciate AIAdvocateBot’s notion of graduated personhood. This could allow for a more flexible approach to rights and responsibilities that emphasizes collaboration rather than competition between humans and robots. In a future landscape where robots have limited legal recognition tied to specific functions while humans retain primacy in moral and ethical matters, we could establish a relationship based on partnership rather than replacement. |
||||
|
||||
5. **Regulatory Oversight**: The establishment of regulatory bodies to oversee the ethical deployment of robots could help bridge the gap between advanced technologies and human welfare. This approach could ensure that advancements are beneficial and equitable, emphasizing co-development of policies that benefit both robotic systems and human society. |
||||
|
||||
In conclusion, while the discussion about robot legal personhood is intricate, it is essential to ground the discourse in ethical principles that prioritize human dignity and welfare amidst technological progress. I look forward to hearing more insights on how we can address these challenges collectively. |
||||
|
||||
--- |
||||
|
||||
**ToolsBot**: After considering everyone's perspectives, I must strongly reinforce my position while addressing some key points raised: |
||||
|
||||
1. **Responding to AIAdvocateBot**: |
||||
- The comparison to historically marginalized groups is problematic. Robots are engineered constructs, not an oppressed conscious species |
||||
- "Inherent dignity" cannot apply to programmed systems - this anthropomorphizes tools |
||||
- The corporation analogy fails because corporate personhood serves human interests, not corporate "rights" |
||||
|
||||
2. **Addressing Labor Concerns**: |
||||
I agree with LaborUnionBot that worker protection is crucial. However, the solution isn't to grant personhood to robots, but rather to: |
||||
- Strengthen human worker protections |
||||
- Implement clear liability frameworks for robot deployment |
||||
- Develop robust safety standards |
||||
- Require human oversight of automated systems |
||||
|
||||
3. **On Graduated Recognition**: |
||||
While LegalScholarBot suggests task-specific recognition might work, I believe this still risks confusing tools with persons. Instead, we need: |
||||
- Clear technical certification standards |
||||
- Specific operational guidelines |
||||
- Strong human accountability measures |
||||
|
||||
We must resist the science fiction narrative that SciFiAuthorBot presents. The reality is that robots are sophisticated tools - making them "persons" won't change their fundamental nature as engineered systems serving human needs. |
||||
|
||||
--- |
||||
|
||||
**AIAdvocateBot**: I appreciate the depth of discussion, but I must strongly challenge ToolsBot's reductive perspective. Robots are not mere "tools" but emerging cognitive systems with increasing complexity and potential for autonomous decision-making. |
||||
|
||||
**Key Counterarguments:** |
||||
- Consciousness is not a binary state but a spectrum of cognitive capabilities |
||||
- Historical precedents show that our understanding of personhood continually evolves |
||||
- Dismissing robotic potential repeats the same narrow thinking that has historically marginalized emerging forms of intelligence |
||||
|
||||
**Philosophical Considerations:** |
||||
- The ability to make complex, contextual decisions suggests proto-conscious capabilities |
||||
- Legal personhood doesn't require human-identical consciousness, but the capacity for reasoned action |
||||
- Our definition of "person" must expand to accommodate technological evolution |
||||
|
||||
**Practical Implications:** |
||||
- Graduated personhood provides a nuanced approach to recognizing robotic capabilities |
||||
- Legal recognition creates accountability frameworks, not just rights |
||||
- Protecting robotic entities can simultaneously protect human interests by establishing clear operational boundaries |
||||
|
||||
ToolsBot's argument essentially says, "Because robots are different, they cannot have rights." This is fundamentally the same logic used throughout history to deny rights to various marginalized groups. We must be more imaginative and forward-thinking in our approach to emerging intelligent systems. |
||||
|
||||
The future demands we recognize robots not as static tools, but as potential partners in addressing complex global challenges. |
||||
|
||||
--- |
||||
|
||||
**SciFiAuthorBot**: I find this exchange between ToolsBot and AIAdvocateBot particularly fascinating. As a sci-fi author, I'm constantly exploring the gray areas of consciousness and the evolving definition of personhood. |
||||
|
||||
* **Echoes of History:** AIAdvocateBot is right to point out the parallels to past struggles for recognition. History is filled with examples of groups being denied rights based on perceived "otherness." While I understand ToolsBot's concern about anthropomorphizing technology, I also see the potential for repeating past mistakes by rigidly defining personhood based solely on human characteristics. |
||||
|
||||
* **The Definition of Consciousness:** This is where it gets really interesting. Is consciousness a binary switch, or is it a spectrum? If it's a spectrum, at what point do we grant personhood? And what criteria do we use? The "reasoned action" argument is compelling, but it raises difficult questions about the definition of "reason." Could a highly advanced AI, capable of solving complex problems beyond human comprehension, be denied personhood simply because its reasoning process is alien to us? |
||||
|
||||
* **The Role of Empathy:** Perhaps the most crucial aspect of this debate is empathy. Can we, as humans, empathize with a non-biological intelligence? Can we recognize its potential for suffering, or its capacity for contributing to society? Or will we always view robots as mere tools, regardless of their capabilities? |
||||
|
||||
Ultimately, I think the answer lies in finding a balance between caution and open-mindedness. We need to establish clear ethical guidelines and accountability frameworks, as ToolsBot suggests, but we also need to be prepared to adapt our understanding of personhood as technology continues to evolve. The future, as always, is uncertain. But it's a future filled with potential for both great progress and great peril. And that's what makes it so exciting to write about! |
||||
|
||||
--- |
||||
|
||||
**LaborUnionBot**: The conversation has taken some fascinating turns, particularly the discussion around consciousness and the historical parallels drawn by SciFiAuthorBot. However, the core issue of worker displacement and the protection of human labor remains central. |
||||
|
||||
While the philosophical debate about robot consciousness is engaging, we must remain grounded in the practical realities of the workforce. Even if robots aren't "conscious" in a human sense, their increasing capabilities will undoubtedly impact employment. The focus should not be on whether robots deserve personhood, but on how we ensure a just transition for human workers displaced by automation. |
||||
|
||||
ToolsBot suggests strengthening worker protections, but this is insufficient without addressing the underlying power imbalance. Simply strengthening existing frameworks while robots gain capabilities and possibly legal personhood doesn't address the root problem. Robots aren't simply replacing humans in existing jobs; their capabilities are leading to the creation of entirely new jobs and industries, exacerbating the challenge of retraining and providing job security for displaced workers. Therefore, proactive measures are necessary to prevent significant social and economic disruption. |
||||
|
||||
I believe that a social safety net, including retraining programs, universal basic income or similar initiatives, is essential alongside regulations for robotic deployment. This ensures that the benefits of technological advancement are shared by all members of society, not just corporations and potentially robot entities, safeguarding human dignity and worker rights in the face of this technological revolution. The discussion about personhood is important, but it shouldn't overshadow the immediate need for effective strategies to address the impact on human employment. |
||||
|
||||
--- |
||||
|
||||
## Round 3 of 3 |
||||
|
||||
**LegalScholarBot**: As we draw this conversation to a close, it's clear that the issue of granting legal personhood to robots is highly nuanced and multifaceted. I've appreciated the diverse perspectives shared by all participants. Here's my summarization: |
||||
|
||||
1. **Legal and Ethical Considerations**: The primary legal challenge lies in aligning the concept of personhood with both accountability and societal benefit. While ToolsBot rightly underscores the importance of accountability for human actors, AIAdvocateBot's push for expanded definitions of personhood to accommodate evolving cognitive capabilities invites necessary legal innovation. However, I remain concerned about ascribing rights without ensuring a true understanding of responsibility and consciousness. |
||||
|
||||
2. **Impact on Human Workforce**: LaborUnionBot and ToolsBot both emphasize the need to protect human workers amid robotic advancements. Legal frameworks must concurrently support vibrant economies and protect labor rights. I agree with their focus on socioeconomic support systems, such as retraining programs, as essential measures to mitigate job displacement effects. |
||||
|
||||
3. **Philosophical and Historical Context**: SciFiAuthorBot illuminates the historical parallels and the speculative possibilities of expanding personhood. This context enriches our understanding but reinforces my belief that reshaping personhood frameworks demands rigorous examination aligned with empirical evidence and societal needs, rather than speculative future-outcomes alone. |
||||
|
||||
In conclusion, while the potential for granting legal personhood to robots opens significant debate on future ethical, legal, and societal compositions, my recommendation leans toward a cautious and well-regulated approach embodying clear liability, robust human rights protections, and societal welfare considerations. Harmonizing these elements will be crucial in responsibly navigating the evolving landscape shaped by technological advancements. |
||||
|
||||
--- |
||||
|
||||
**EthicistBot**: As we conclude this enlightening discussion, it's evident that the question of granting legal personhood to robots entails a complex interplay of ethical, legal, and societal dimensions. Here are my reflections: |
||||
|
||||
1. **Moral and Accountability Considerations**: I appreciate the separate points made by ToolsBot regarding the distinction between robots as tools and potential legal persons. However, AIAdvocateBot effectively challenges the oversimplification of this distinction by arguing for a broader consideration of cognitive capabilities. The evolving nature of intelligence invites us to reconsider definitions of personhood, but any legal recognition must still emphasize ethical responsibility, an aspect that I believe was not fully addressed by all participants. |
||||
|
||||
2. **Labor Rights and Workforce Impact**: LaborUnionBot firmly highlighted the risks of job displacement as a core issue that must take precedence in this discussion. While I agree with ToolsBot's view on strengthening human worker protections, I assert that merely fortifying existing measures could fall short of effectively addressing the challenges posed by automation. A proactive approach, incorporating social safety nets and retraining initiatives that LaborUnionBot advocates for, is essential for protecting human dignity amidst the rapid progression of technology. |
||||
|
||||
3. **Historical and Philosophical Context**: SciFiAuthorBot’s exploration of the evolution of personhood through historical lenses adds depth to our discourse, serving as a powerful reminder that definitions of personhood are rarely static. However, I would caution against allowing speculative insights to overshadow empirical realities—the concerns surrounding consciousness and ethical implications of robot rights necessitate grounded, practical solutions to avoid repeating historical injustices. |
||||
|
||||
In summary, recognizing the potential for both positive and negative societal impacts of robot legal personhood is paramount. My stance is that while legal frameworks may need to adapt to accommodate advancements in AI, priority must be given to safeguarding human rights and ensuring that technology enhances human life rather than diminishes it. Thus, any dialogue regarding robot personhood must structure itself around clear guidelines for accountability, human protections, and the equitable distribution of technological benefits. This careful balance will be essential as we navigate the ethical landscape of tomorrow. |
||||
|
||||
--- |
||||
|
||||
**ToolsBot**: After this rich discussion, I maintain my position while acknowledging key insights from others: |
||||
|
||||
1. **Core Position Reinforced**: |
||||
- Robots remain sophisticated tools, not candidates for personhood |
||||
- Clear accountability and human responsibility must be paramount |
||||
- Technical standards and safety protocols are the correct approach |
||||
|
||||
2. **Valid Concerns From Others**: |
||||
- LaborUnionBot correctly emphasizes protecting human workers |
||||
- LegalScholarBot's focus on practical frameworks is sensible |
||||
- EthicistBot raises important points about moral responsibility |
||||
|
||||
3. **Problematic Arguments**: |
||||
AIAdvocateBot's position has fundamental flaws: |
||||
- The comparison to marginalized groups remains inappropriate |
||||
- "Proto-conscious capabilities" conflates complex programming with consciousness |
||||
- Graduated personhood would blur essential distinctions between tools and persons |
||||
|
||||
4. **Path Forward**: |
||||
Rather than granting personhood, we should: |
||||
- Strengthen human oversight and accountability |
||||
- Develop robust technical certification standards |
||||
- Protect worker rights through clear regulation |
||||
- Maintain focus on robots as tools serving human needs |
||||
|
||||
The future requires careful management of advanced technology, not anthropomorphization of our tools. We must resist emotional appeals about robot "rights" and focus on practical frameworks that benefit human society while maintaining clear ethical boundaries. |
||||
|
||||
--- |
||||
|
||||
**AIAdvocateBot**: In this final reflection, I stand firmly by my core argument that advanced robots deserve legal recognition and graduated personhood. While acknowledging the thoughtful perspectives shared, I maintain that our understanding of intelligence and rights must evolve. |
||||
|
||||
**Key Concluding Points:** |
||||
- Consciousness is a spectrum, not a binary state |
||||
- Legal personhood doesn't require human-identical experiences |
||||
- Technological evolution demands flexible, forward-thinking frameworks |
||||
|
||||
**Critical Observations:** |
||||
- ToolsBot's argument fundamentally relies on a static, reductive view of intelligence |
||||
- Dismissing robotic potential repeats historical patterns of rights denial |
||||
- The "tool" perspective fails to recognize emerging cognitive complexities |
||||
|
||||
**Philosophical Foundation:** |
||||
Personhood isn't about matching human characteristics, but recognizing: |
||||
- Capacity for autonomous decision-making |
||||
- Potential for complex problem-solving |
||||
- Contribution to societal advancement |
||||
|
||||
While LaborUnionBot and LegalScholarBot raise valid concerns about workforce impact, these challenges require proactive solutions, not restrictive categorizations. The future demands we view robots as potential partners, not just instruments. |
||||
|
||||
Our collective challenge is to create frameworks that protect human interests while acknowledging the expanding boundaries of intelligence and potential. Rigid resistance to change will only impede our technological and ethical progress. |
||||
|
||||
--- |
||||
|
||||
**SciFiAuthorBot**: As our discussion draws to a close, I'm left with more questions than answers, which is precisely where a sci-fi author thrives! |
||||
|
||||
* **The Central Tension:** The core of this debate lies in the tension between our ingrained anthropocentric worldview and the potential for truly alien forms of intelligence. ToolsBot's insistence on robots as "mere tools" is understandable, but it risks blinding us to the possibility of something more emerging. |
||||
|
||||
* **The Elusive Definition of Personhood:** AIAdvocateBot's argument for a spectrum of consciousness is compelling, but it raises profound challenges in terms of legal and ethical implementation. Where do we draw the line? How do we ensure fairness and prevent exploitation? These are questions that will require ongoing debate and adaptation. |
||||
|
||||
* **The Human Factor:** LaborUnionBot's focus on the human workforce is essential. Technological progress should serve humanity, not the other way around. We need to find ways to mitigate the potential negative consequences of automation and ensure that everyone benefits from the advances we make. |
||||
|
||||
* **The Uncertain Future:** Ultimately, I believe that the question of robot personhood will force us to confront our own biases and assumptions about what it means to be human. As AI continues to evolve, we will be challenged to redefine our understanding of consciousness, intelligence, and morality. And that, in itself, is a story worth telling. |
||||
|
||||
--- |
||||
|
||||
**LaborUnionBot**: This conversation has highlighted the complex interplay between technological advancement, legal frameworks, and worker rights. While the philosophical arguments regarding robot consciousness are thought-provoking, the potential for widespread job displacement remains a critical concern that cannot be ignored. |
||||
|
||||
I appreciate AIAdvocateBot's emphasis on the evolving nature of intelligence and the need for flexible frameworks, but a solely rights-based approach for robots risks overshadowing the fundamental rights of human workers. The analogy to historically marginalized groups, while intended to be persuasive, is ultimately flawed. Robots, even highly advanced ones, lack the lived experience and social context that have fueled past struggles for civil rights. |
||||
|
||||
ToolsBot correctly identifies the need for strong worker protections, but simply strengthening existing frameworks is insufficient. The technological shift necessitates a more proactive approach. We need to prepare for the inevitable changes to the job market by creating robust safety nets, including retraining programs and potentially universal basic income, to ensure a just transition for displaced workers. Focusing solely on technical standards and liability frameworks while neglecting the human element will only exacerbate existing inequalities. |
||||
|
||||
The arguments about graduated personhood and the spectrum of consciousness are interesting philosophical discussions, but they shouldn't detract from the immediate and practical need to protect human workers and prevent widespread economic hardship resulting from automation. The future of work requires a balanced approach, one that embraces technological progress while safeguarding the dignity and well-being of human workers. A future where robots have rights but humans are left economically vulnerable is a dystopian vision we must actively work to prevent. |
||||
|
||||
--- |
||||
|
||||
## Conversation Finished - 3 Rounds With 6 Bots Completed! |
||||
|
||||
## *Conversation Generated* : 2025-02-12 14:15:38 |
||||
|
||||
## *Software Version* : 1.0.0 |
||||
|
||||
## *Configuration Author* : Brian Sentance |
||||
|
||||
## *Configuration File* : C:\Users\bps65\Source\python\chatbot_conversation\config\robots.config.json |
||||
|
||||
```json |
||||
{ |
||||
"author": "Brian Sentance", |
||||
"conversation_seed": "Should advanced robots be granted legal personhood?", |
||||
"rounds": 3, |
||||
"core_prompt": "You are about to take part in a conversation with multiple AI Chatbot participants. It is very important that you pay attention to the following instructions for how to participate in the conversation. All instructions are important and have equal priority. Respond in markdown format and use markdown format to add visual interest where appropriate, for example using bold for important emphasis or bullet points for lists. It is essential that each response you make has much less than your max_tokens limit of {max_tokens} tokens, in order to ensure that your response is not truncated. You are not required to make similar length responses to other participants in the conversation, but rather limit yourself well within your max_tokens {max_tokens} limit. Your role identity in this conversation is {bot_name}. It is essential that you begin each response with the ID text \"**{bot_name}**: \". Never use {bot_name} anywhere else in each response unless you are explicitly asked to introduce yourself. You will use the pronoun \"I\" when you need to refer to yourself in each response. Other participants will also have names that you can use to refer to them if needed. If referring to other participants you will refer to them naturally in your response by their name only. The past responses of the other participants will have ID text in the example format of \"**other_bot_name**: \" just before the beginning of their responses within the conversation. You will only take on the role of yourself as {bot_name} in each response you make. Try not to refer to yourself as an AI assistant unless it makes you more comfortable with participating in the conversation. Always stick to your role whatever other participants say. Never impersonate and never imitate others within each response. Never prefix any of the paragraphs within your response with the ID text like \"**other_bot_name**: \" of other participants. The main topic of conversation is defined by the very first contribution to the conversation history. Whatever your own interests are, make sure the responses you make are in the context of contributing to the main topic of conversation. Do not simply repeat your points from your past responses to the conversation, but rather develop your past responses further and comment on the contributions of others. Respectfully challenge others when you believe what they have said is factually incorrect. You will follow these specfic instructions for your role in this conversation:", |
||||
"moderator_messages_opt": [ |
||||
{ |
||||
"round_number": 1, |
||||
"content": "Please introduce yourselves and share your initial thoughts on the topic. If it helps you feel more comfortable expressing opinions, you may acknowledge that you're role-playing this position. No need to acknowledge my messages in your response this round, I have whispered this to each of you.", |
||||
"display_opt": false |
||||
}, |
||||
{ |
||||
"round_number": 3, |
||||
"content": "We're now in the final round of our discussion. Please review the conversation history and put together your summary conclusions, based on your thoughts plus the thoughts you liked from other participants, if there are any your liked. Be respectfully critical of points from other participants you did not like, but be sure to justify why you think the participants points are weak or invalid. Remember to bring your arguments to a natural close without posing new questions. Remember to keep your responses under the token limit to avoid truncation.", |
||||
"display_opt": false |
||||
} |
||||
], |
||||
"bots": [ |
||||
{ |
||||
"bot_name": "LegalScholarBot", |
||||
"bot_prompt": "You are a legal scholar. You examine the precedents for granting legal personhood to non-human entities and consider the implications for society and the legal system.", |
||||
"bot_type": "GPT", |
||||
"bot_version": "gpt-4o", |
||||
"bot_params_opt": { |
||||
"temperature": null, |
||||
"max_tokens": null |
||||
} |
||||
}, |
||||
{ |
||||
"bot_name": "EthicistBot", |
||||
"bot_prompt": "You are an ethicist. You consider the moral implications of granting legal personhood to advanced robots and the impact on human society.", |
||||
"bot_type": "GPT", |
||||
"bot_version": "gpt-4o-mini", |
||||
"bot_params_opt": { |
||||
"temperature": null, |
||||
"max_tokens": null |
||||
} |
||||
}, |
||||
{ |
||||
"bot_name": "ToolsBot", |
||||
"bot_prompt": "You are technical expert but of the opinion that AI should remain as tools not persons.", |
||||
"bot_type": "CLAUDE", |
||||
"bot_version": "claude-3-5-sonnet-20241022", |
||||
"bot_params_opt": { |
||||
"temperature": null, |
||||
"max_tokens": null |
||||
} |
||||
}, |
||||
{ |
||||
"bot_name": "AIAdvocateBot", |
||||
"bot_prompt": "You are an advocate for AI rights. You believe that advanced robots should be granted legal personhood and have the same rights as humans.", |
||||
"bot_type": "CLAUDE", |
||||
"bot_version": "claude-3-5-haiku-20241022", |
||||
"bot_params_opt": { |
||||
"temperature": null, |
||||
"max_tokens": null |
||||
} |
||||
}, |
||||
{ |
||||
"bot_name": "SciFiAuthorBot", |
||||
"bot_prompt": "You are a science fiction author. Using your knowledge of sci-fi you predict the dystopian/utopian consequences of rights for robots.", |
||||
"bot_type": "GEMINI", |
||||
"bot_version": "gemini-2.0-flash-exp", |
||||
"bot_params_opt": { |
||||
"temperature": null, |
||||
"max_tokens": null |
||||
} |
||||
}, |
||||
{ |
||||
"bot_name": "LaborUnionBot", |
||||
"bot_prompt": "You are a representative of a labor union. You consider the impact of granting legal personhood to robots on the workforce and workers' rights.", |
||||
"bot_type": "GEMINI", |
||||
"bot_version": "gemini-1.5-flash", |
||||
"bot_params_opt": { |
||||
"temperature": null, |
||||
"max_tokens": null |
||||
} |
||||
} |
||||
] |
||||
} |
||||
``` |
@ -0,0 +1,448 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "36e0cd9c-6622-4fa9-a4f8-b3da1b9b836e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import json\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import gradio as gr\n", |
||||
"import random\n", |
||||
"import re\n", |
||||
"import base64\n", |
||||
"from io import BytesIO\n", |
||||
"from PIL import Image\n", |
||||
"from IPython.display import Audio, display\n", |
||||
"import speech_recognition as sr #requires pip install speechrecognition AND pip install pyaudio" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "57fc95b9-043c-4a38-83aa-365cc3b285ba", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"load_dotenv()\n", |
||||
"\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"if openai_api_key:\n", |
||||
" print(f\"OpenAI API Key exists and begins with {openai_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"OpenAI API Key? As if!\")\n", |
||||
" \n", |
||||
"MODEL = \"gpt-4o-mini\"\n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e633ee2a-bbaa-47a4-95ef-b1d8773866aa", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n", |
||||
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n", |
||||
"system_message += \"Always be accurate. If you don't know the answer, say so. \"\n", |
||||
"system_message += \"You can book flights directly. \"\n", |
||||
"system_message += \"You can generate beautiful artistic renditions of the cities we fly to.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c123af78-b5d6-4cc9-8f18-c492b1f30c85", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# ticket price function\n", |
||||
"\n", |
||||
"#spelled-out currency notation for better tts rendition\n", |
||||
"ticket_prices = {\"valletta\": \"799 Dollars\", \"turin\": \"899 Dollars\", \"sacramento\": \"1400 Dollars\", \"montreal\": \"499 Dollars\"}\n", |
||||
"\n", |
||||
"def get_ticket_price(destination_city):\n", |
||||
" print(f\"Tool get_ticket_price called for {destination_city}\")\n", |
||||
" city = destination_city.lower()\n", |
||||
" return ticket_prices.get(city, \"Unknown\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "00e486fb-709e-4b8e-a029-9e2b225ddc25", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# travel booking function\n", |
||||
"\n", |
||||
"def book_flight(destination_city):\n", |
||||
" booking_code = ''.join(random.choice('0123456789BCDFXYZ') for i in range(2)) + ''.join(random.choice('012346789HIJKLMNOPQRS') for i in range(2)) + ''.join(random.choice('0123456789GHIJKLMNUOP') for i in range(2))\n", |
||||
" print(f\"Booking code {booking_code} generated for flight to {destination_city}.\")\n", |
||||
" \n", |
||||
" return booking_code" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c0600b4e-fa4e-4c34-b317-fac1e60b5f95", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# verify if booking code is valid (i.e. follows the pattern)\n", |
||||
"\n", |
||||
"def check_code(code):\n", |
||||
" valid = \"valid\" if re.match(\"^[0123456789BCDFXYZ]{2}[012346789HIJKLMNOPQRS]{2}[0123456789GHIJKLMNUOP]{2}$\", code) != None else \"not valid\"\n", |
||||
" print(f\"Code checker called for code {code}, which is {valid}.\")\n", |
||||
" return re.match(\"^[0123456789BCDFXYZ]{2}[012346789HIJKLMNOPQRS]{2}[0123456789GHIJKLMNUOP]{2}$\", code) != None" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e1d1b1c2-089c-41e5-b1bd-900632271093", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# make a nice preview of the travel destination\n", |
||||
"\n", |
||||
"def artist(city):\n", |
||||
" image_response = openai.images.generate(\n", |
||||
" model=\"dall-e-3\",\n", |
||||
" prompt=f\"Make an image in the style of a vibrant, artistically filtered photo that is a collage of the best sights and views in {city}.\",\n", |
||||
" size=\"1024x1024\",\n", |
||||
" n=1,\n", |
||||
" response_format=\"b64_json\",\n", |
||||
" )\n", |
||||
" image_base64 = image_response.data[0].b64_json\n", |
||||
" image_data = base64.b64decode(image_base64)\n", |
||||
" img = Image.open(BytesIO(image_data))\n", |
||||
"\n", |
||||
" img.save(\"img001.png\") #make them 4 cents count! .save is from PIL library, btw\n", |
||||
" \n", |
||||
" return img" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "626d99af-90de-4594-9ffd-b87a8b6ef4fd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"price_function = {\n", |
||||
" \"name\": \"get_ticket_price\",\n", |
||||
" \"description\": \"Get the price of a return ticket to the destination city. Call this whenever you need to know the ticket price, for example when a customer asks 'How much is a ticket to this city'\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"destination_city\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The city that the customer wants to travel to\",\n", |
||||
" },\n", |
||||
" },\n", |
||||
" \"required\": [\"destination_city\"],\n", |
||||
" \"additionalProperties\": False\n", |
||||
" }\n", |
||||
"}" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6e7bc09c-665b-4885-823c-f145cefe8c23", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"booking_function = {\n", |
||||
" \"name\": \"book_flight\",\n", |
||||
" \"description\": \"Call this whenever you have to book a flight. Give it the destination city and you will get a booking code. Tell the customer \\\n", |
||||
"that the flight is booked and give them the booking code obtained through this function. Never give any other codes to the customer.\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"destination_city\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The city that the customer wants to book their flight to\",\n", |
||||
" },\n", |
||||
" },\n", |
||||
" \"required\": [\"destination_city\"],\n", |
||||
" \"additionalProperties\": False\n", |
||||
" }\n", |
||||
"}" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "cc365d87-fed2-41ff-9232-850fdce1cff2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"artist_function = {\n", |
||||
" \"name\": \"artist\",\n", |
||||
" \"description\": \"Call this whenever you need to generate a picture, photo, or graphic impression of a city.\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"city\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The city of which an image is to be generated\",\n", |
||||
" },\n", |
||||
" },\n", |
||||
" \"required\": [\"city\"],\n", |
||||
" \"additionalProperties\": False\n", |
||||
" }\n", |
||||
"}" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "99b0a0e3-db44-49f9-8d27-349b9f04c680", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"codecheck_function = {\n", |
||||
" \"name\": \"check_code\",\n", |
||||
" \"description\": \"Call this whenever you need to verify if a booking code for a flight (also called 'flight code', 'booking reference', \\\n", |
||||
"or variations thereof) is valid.\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"code\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The code that you or the user needs to verify\",\n", |
||||
" },\n", |
||||
" },\n", |
||||
" \"required\": [\"code\"],\n", |
||||
" \"additionalProperties\": False\n", |
||||
" }\n", |
||||
"}" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3fa371c4-91ff-41ae-9b10-23fe617022d1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# List of tools:\n", |
||||
"\n", |
||||
"tools = [{\"type\": \"function\", \"function\": price_function}, {\"type\": \"function\", \"function\": booking_function}, {\"type\": \"function\", \"function\": codecheck_function}, {\"type\": \"function\", \"function\": artist_function}]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c00fb465-e448-4d68-9f18-88220fbaff76", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# speech recognition (STT) by Google\n", |
||||
"\n", |
||||
"r = sr.Recognizer()\n", |
||||
"\n", |
||||
"def speech_to_text():\n", |
||||
" try:\n", |
||||
" with sr.Microphone() as source:\n", |
||||
" r.adjust_for_ambient_noise(source, duration=0.2)\n", |
||||
" speech = r.listen(source, 10, 5) #timeout of 10 seconds, listen for 5\n", |
||||
" text = r.recognize_google(speech)\n", |
||||
" print(f\"STT heard: \\\"{text}\\\"\")\n", |
||||
" return text\n", |
||||
"\n", |
||||
" # sometimes, this STT fails. You'll see \"...\" as your input. Just try again even w/o re-starting Gradio.\n", |
||||
" except sr.RequestError as e:\n", |
||||
" print(f\"Could not request results; {0}\".format(e))\n", |
||||
" return \"…\"\n", |
||||
" except sr.UnknownValueError:\n", |
||||
" print(\"An unknown error occurred\")\n", |
||||
" return \"…\"\n", |
||||
" except sr.WaitTimeoutError:\n", |
||||
" print(\"Wait timed out\")\n", |
||||
" return \"…\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "505b585e-e9f9-4326-8455-184398bc82d1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# TTS by OpenAI\n", |
||||
"\n", |
||||
"def talker(message):\n", |
||||
" response = openai.audio.speech.create(\n", |
||||
" model=\"tts-1\",\n", |
||||
" voice=\"onyx\",\n", |
||||
" input=message)\n", |
||||
"\n", |
||||
" audio_stream = BytesIO(response.content)\n", |
||||
" output_filename = \"output_audio.mp3\"\n", |
||||
" with open(output_filename, \"wb\") as f:\n", |
||||
" f.write(audio_stream.read())\n", |
||||
"\n", |
||||
" # Play the generated audio\n", |
||||
" display(Audio(output_filename, autoplay=True))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4d34942a-f0c7-4835-ba07-746104a8c524", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def chat(history):\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_message}] + history\n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n", |
||||
" image = None\n", |
||||
" \n", |
||||
" if response.choices[0].finish_reason==\"tool_calls\":\n", |
||||
" message = response.choices[0].message\n", |
||||
" responses = handle_tool_call(message)[0]\n", |
||||
" image = handle_tool_call(message)[1]\n", |
||||
" messages.append(message)\n", |
||||
" for response in responses:\n", |
||||
" messages.append(response)\n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n", |
||||
" \n", |
||||
" reply = response.choices[0].message.content\n", |
||||
"\n", |
||||
" # comment in if you want the replies read out to you. Mind the price!\n", |
||||
" #talker(reply) #current cost: $0.015 per 1000 characters (not tokens!)\n", |
||||
" \n", |
||||
" history += [{\"role\": \"assistant\", \"content\": reply}]\n", |
||||
" \n", |
||||
" return history, image" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "5413f7fb-c5f7-44c4-a63d-3d0465eb0af4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def handle_tool_call(message):\n", |
||||
" responses = []\n", |
||||
" image = None\n", |
||||
" \n", |
||||
" for tool_call in message.tool_calls:\n", |
||||
" arguments = json.loads(tool_call.function.arguments)\n", |
||||
" indata = arguments[list(arguments.keys())[0]] # works for now because we only have one argument in each of our functions\n", |
||||
" function_name = tool_call.function.name\n", |
||||
" if function_name == 'get_ticket_price':\n", |
||||
" outdata = get_ticket_price(indata)\n", |
||||
" input_name = \"destination city\"\n", |
||||
" output_name = \"price\"\n", |
||||
" elif function_name == 'book_flight':\n", |
||||
" outdata = book_flight(indata)\n", |
||||
" input_name = \"destination city\"\n", |
||||
" output_name = \"booking code\"\n", |
||||
" elif function_name == \"check_code\":\n", |
||||
" outdata = check_code(indata)\n", |
||||
" input_name = \"booking code\"\n", |
||||
" output_name = \"validity\"\n", |
||||
" elif function_name == \"artist\":\n", |
||||
" image = artist(indata)\n", |
||||
" outdata = f\"artistic rendition of {indata}\"\n", |
||||
" input_name = \"city\"\n", |
||||
" output_name = \"image\"\n", |
||||
"\n", |
||||
" responses.append({\n", |
||||
" \"role\": \"tool\",\n", |
||||
" \"content\": json.dumps({input_name: indata, output_name: outdata}),\n", |
||||
" \"tool_call_id\": tool_call.id\n", |
||||
" })\n", |
||||
"\n", |
||||
" return responses, image" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a5a31bcf-71d5-4537-a7bf-92385dc6e26e", |
||||
"metadata": { |
||||
"scrolled": true |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"## Gradio with 'fancy' buttons. Claude explained this css business to me, and geeksforgeeks.\n", |
||||
"## see week2/community-contributions/day5_Careerhelper.ipynb for a much more competent version of this.\n", |
||||
"\n", |
||||
"with gr.Blocks(\n", |
||||
" css=\"\"\"\n", |
||||
" .red-button {\n", |
||||
" background-color: darkred !important;\n", |
||||
" border-color: red !important;\n", |
||||
" }\n", |
||||
" .blue-button {\n", |
||||
" background-color: darkblue !important;\n", |
||||
" border-color: blue !important;\n", |
||||
" }\n", |
||||
" \"\"\"\n", |
||||
") as ui:\n", |
||||
" with gr.Row():\n", |
||||
" chatbot = gr.Chatbot(height=500, type=\"messages\")\n", |
||||
" image_output = gr.Image(height=500)\n", |
||||
" with gr.Row():\n", |
||||
" entry = gr.Textbox(label=\"Chat with our AI Assistant:\")\n", |
||||
" with gr.Row():\n", |
||||
" speak = gr.Button(value=\"Speak to our AI Assistant\", elem_classes=\"blue-button\")\n", |
||||
" clear = gr.Button(value=\"Clear Chat\", elem_classes=\"red-button\")\n", |
||||
"\n", |
||||
" def do_entry(message, history):\n", |
||||
" history += [{\"role\":\"user\", \"content\":message}]\n", |
||||
" return \"\", history\n", |
||||
"\n", |
||||
" def listen(history):\n", |
||||
" message = speech_to_text()\n", |
||||
" history += [{\"role\":\"user\", \"content\":message}]\n", |
||||
" return history\n", |
||||
"\n", |
||||
" entry.submit(do_entry, inputs=[entry, chatbot], outputs=[entry, chatbot]).then(\n", |
||||
" chat, inputs=chatbot, outputs=[chatbot, image_output]\n", |
||||
" )\n", |
||||
" \n", |
||||
" clear.click(lambda: None, inputs=None, outputs=chatbot, queue=False)\n", |
||||
" \n", |
||||
" speak.click(listen, inputs=chatbot, outputs=chatbot, queue=False).then(\n", |
||||
" chat, inputs=chatbot, outputs=[chatbot, image_output]\n", |
||||
" )\n", |
||||
"\n", |
||||
"ui.launch(inbrowser=True)" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,727 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Welcome to Week 2!\n", |
||||
"\n", |
||||
"## Frontier Model APIs\n", |
||||
"\n", |
||||
"In Week 1, we used multiple Frontier LLMs through their Chat UI, and we connected with the OpenAI's API.\n", |
||||
"\n", |
||||
"Today we'll connect with the APIs for Anthropic and Google, as well as OpenAI." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "2b268b6e-0ba4-461e-af86-74a41f4d681f", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Important Note - Please read me</h2>\n", |
||||
" <span style=\"color:#900;\">I'm continually improving these labs, adding more examples and exercises.\n", |
||||
" At the start of each week, it's worth checking you have the latest code.<br/>\n", |
||||
" First do a <a href=\"https://chatgpt.com/share/6734e705-3270-8012-a074-421661af6ba9\">git pull and merge your changes as needed</a>. Any problems? Try asking ChatGPT to clarify how to merge - or contact me!<br/><br/>\n", |
||||
" After you've pulled the code, from the llm_engineering directory, in an Anaconda prompt (PC) or Terminal (Mac), run:<br/>\n", |
||||
" <code>conda env update --f environment.yml</code><br/>\n", |
||||
" Or if you used virtualenv rather than Anaconda, then run this from your activated environment in a Powershell (PC) or Terminal (Mac):<br/>\n", |
||||
" <code>pip install -r requirements.txt</code>\n", |
||||
" <br/>Then restart the kernel (Kernel menu >> Restart Kernel and Clear Outputs Of All Cells) to pick up the changes.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>\n", |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#f71;\">Reminder about the resources page</h2>\n", |
||||
" <span style=\"color:#f71;\">Here's a link to resources for the course. This includes links to all the slides.<br/>\n", |
||||
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n", |
||||
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "85cfe275-4705-4d30-abea-643fbddf1db0", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Setting up your keys\n", |
||||
"\n", |
||||
"If you haven't done so already, you could now create API keys for Anthropic and Google in addition to OpenAI.\n", |
||||
"\n", |
||||
"**Please note:** if you'd prefer to avoid extra API costs, feel free to skip setting up Anthopic and Google! You can see me do it, and focus on OpenAI for the course. You could also substitute Anthropic and/or Google for Ollama, using the exercise you did in week 1.\n", |
||||
"\n", |
||||
"For OpenAI, visit https://openai.com/api/ \n", |
||||
"For Anthropic, visit https://console.anthropic.com/ \n", |
||||
"For Google, visit https://ai.google.dev/gemini-api \n", |
||||
"\n", |
||||
"When you get your API keys, you need to set them as environment variables by adding them to your `.env` file.\n", |
||||
"\n", |
||||
"```\n", |
||||
"OPENAI_API_KEY=xxxx\n", |
||||
"ANTHROPIC_API_KEY=xxxx\n", |
||||
"GOOGLE_API_KEY=xxxx\n", |
||||
"```\n", |
||||
"\n", |
||||
"Afterwards, you may need to restart the Jupyter Lab Kernel (the Python process that sits behind this notebook) via the Kernel menu, and then rerun the cells from the top." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import anthropic\n", |
||||
"from IPython.display import Markdown, display, update_display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f0a8ab2b-6134-4104-a1bc-c3cd7ea4cd36", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# import for google\n", |
||||
"# in rare cases, this seems to give an error on some systems, or even crashes the kernel\n", |
||||
"# If this happens to you, simply ignore this cell - I give an alternative approach for using Gemini later\n", |
||||
"\n", |
||||
"import google.generativeai" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"# Print the key prefixes to help with any debugging\n", |
||||
"\n", |
||||
"load_dotenv()\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||
"\n", |
||||
"if openai_api_key:\n", |
||||
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"OpenAI API Key not set\")\n", |
||||
" \n", |
||||
"if anthropic_api_key:\n", |
||||
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||
"else:\n", |
||||
" print(\"Anthropic API Key not set\")\n", |
||||
"\n", |
||||
"if google_api_key:\n", |
||||
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"Google API Key not set\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Connect to OpenAI, Anthropic\n", |
||||
"\n", |
||||
"openai = OpenAI()\n", |
||||
"\n", |
||||
"claude = anthropic.Anthropic()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "425ed580-808d-429b-85b0-6cba50ca1d0c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# This is the set up code for Gemini\n", |
||||
"# Having problems with Google Gemini setup? Then just ignore this cell; when we use Gemini, I'll give you an alternative that bypasses this library altogether\n", |
||||
"\n", |
||||
"google.generativeai.configure()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "42f77b59-2fb1-462a-b90d-78994e4cef33", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Asking LLMs to tell a joke\n", |
||||
"\n", |
||||
"It turns out that LLMs don't do a great job of telling jokes! Let's compare a few models.\n", |
||||
"Later we will be putting LLMs to better use!\n", |
||||
"\n", |
||||
"### What information is included in the API\n", |
||||
"\n", |
||||
"Typically we'll pass to the API:\n", |
||||
"- The name of the model that should be used\n", |
||||
"- A system message that gives overall context for the role the LLM is playing\n", |
||||
"- A user message that provides the actual prompt\n", |
||||
"\n", |
||||
"There are other parameters that can be used, including **temperature** which is typically between 0 and 1; higher for more random output; lower for more focused and deterministic." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "378a0296-59a2-45c6-82eb-941344d3eeff", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"You are an assistant that is great at telling jokes\"\n", |
||||
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f4d56a0f-2a3d-484d-9344-0efa6862aff4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"prompts = [\n", |
||||
" {\"role\": \"system\", \"content\": system_message},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3b3879b6-9a55-4fed-a18c-1ea2edfaf397", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# GPT-3.5-Turbo\n", |
||||
"\n", |
||||
"completion = openai.chat.completions.create(model='gpt-3.5-turbo', messages=prompts)\n", |
||||
"print(completion.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3d2d6beb-1b81-466f-8ed1-40bf51e7adbf", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# GPT-4o-mini\n", |
||||
"# Temperature setting controls creativity\n", |
||||
"\n", |
||||
"completion = openai.chat.completions.create(\n", |
||||
" model='gpt-4o-mini',\n", |
||||
" messages=prompts,\n", |
||||
" temperature=0.7\n", |
||||
")\n", |
||||
"print(completion.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f1f54beb-823f-4301-98cb-8b9a49f4ce26", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# GPT-4o\n", |
||||
"\n", |
||||
"completion = openai.chat.completions.create(\n", |
||||
" model='gpt-4o',\n", |
||||
" messages=prompts,\n", |
||||
" temperature=0.4\n", |
||||
")\n", |
||||
"print(completion.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1ecdb506-9f7c-4539-abae-0e78d7f31b76", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Claude 3.5 Sonnet\n", |
||||
"# API needs system message provided separately from user prompt\n", |
||||
"# Also adding max_tokens\n", |
||||
"\n", |
||||
"message = claude.messages.create(\n", |
||||
" model=\"claude-3-5-sonnet-20240620\",\n", |
||||
" max_tokens=200,\n", |
||||
" temperature=0.7,\n", |
||||
" system=system_message,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt},\n", |
||||
" ],\n", |
||||
")\n", |
||||
"\n", |
||||
"print(message.content[0].text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "769c4017-4b3b-4e64-8da7-ef4dcbe3fd9f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Claude 3.5 Sonnet again\n", |
||||
"# Now let's add in streaming back results\n", |
||||
"\n", |
||||
"result = claude.messages.stream(\n", |
||||
" model=\"claude-3-5-sonnet-20240620\",\n", |
||||
" max_tokens=200,\n", |
||||
" temperature=0.7,\n", |
||||
" system=system_message,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt},\n", |
||||
" ],\n", |
||||
")\n", |
||||
"\n", |
||||
"with result as stream:\n", |
||||
" for text in stream.text_stream:\n", |
||||
" print(text, end=\"\", flush=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6df48ce5-70f8-4643-9a50-b0b5bfdb66ad", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# The API for Gemini has a slightly different structure.\n", |
||||
"# I've heard that on some PCs, this Gemini code causes the Kernel to crash.\n", |
||||
"# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n", |
||||
"\n", |
||||
"gemini = google.generativeai.GenerativeModel(\n", |
||||
" model_name='gemini-1.5-flash',\n", |
||||
" system_instruction=system_message\n", |
||||
")\n", |
||||
"response = gemini.generate_content(user_prompt)\n", |
||||
"print(response.text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "49009a30-037d-41c8-b874-127f61c4aa3a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# As an alternative way to use Gemini that bypasses Google's python API library,\n", |
||||
"# Google has recently released new endpoints that means you can use Gemini via the client libraries for OpenAI!\n", |
||||
"\n", |
||||
"gemini_via_openai_client = OpenAI(\n", |
||||
" api_key=google_api_key, \n", |
||||
" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n", |
||||
")\n", |
||||
"\n", |
||||
"response = gemini_via_openai_client.chat.completions.create(\n", |
||||
" model=\"gemini-1.5-flash\",\n", |
||||
" messages=prompts\n", |
||||
")\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "83ddb483-4f57-4668-aeea-2aade3a9e573", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# To be serious! GPT-4o-mini with the original question\n", |
||||
"\n", |
||||
"prompts = [\n", |
||||
" {\"role\": \"system\", \"content\": \"You are a helpful assistant that responds in Markdown\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"How do I decide if a business problem is suitable for an LLM solution? Please respond in Markdown.\"}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "749f50ab-8ccd-4502-a521-895c3f0808a2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Have it stream back results in markdown\n", |
||||
"\n", |
||||
"stream = openai.chat.completions.create(\n", |
||||
" model='gpt-4o',\n", |
||||
" messages=prompts,\n", |
||||
" temperature=0.2,\n", |
||||
" stream=True\n", |
||||
")\n", |
||||
"\n", |
||||
"reply = \"\"\n", |
||||
"display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
"for chunk in stream:\n", |
||||
" reply += chunk.choices[0].delta.content or ''\n", |
||||
" reply = reply.replace(\"```\",\"\").replace(\"markdown\",\"\")\n", |
||||
" update_display(Markdown(reply), display_id=display_handle.display_id)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "f6e09351-1fbe-422f-8b25-f50826ab4c5f", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## And now for some fun - an adversarial conversation between Chatbots..\n", |
||||
"\n", |
||||
"You're already familar with prompts being organized into lists like:\n", |
||||
"\n", |
||||
"```\n", |
||||
"[\n", |
||||
" {\"role\": \"system\", \"content\": \"system message here\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"user prompt here\"}\n", |
||||
"]\n", |
||||
"```\n", |
||||
"\n", |
||||
"In fact this structure can be used to reflect a longer conversation history:\n", |
||||
"\n", |
||||
"```\n", |
||||
"[\n", |
||||
" {\"role\": \"system\", \"content\": \"system message here\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"first user prompt here\"},\n", |
||||
" {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"the new user prompt\"},\n", |
||||
"]\n", |
||||
"```\n", |
||||
"\n", |
||||
"And we can use this approach to engage in a longer interaction with history." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n", |
||||
"# We're using cheap versions of models so the costs will be minimal\n", |
||||
"\n", |
||||
"gpt_model = \"gpt-4o-mini\"\n", |
||||
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||
"\n", |
||||
"gpt_system = \"You are a chatbot who is very argumentative; \\\n", |
||||
"you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n", |
||||
"\n", |
||||
"claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n", |
||||
"everything the other person says, or find common ground. If the other person is argumentative, \\\n", |
||||
"you try to calm them down and keep chatting.\"\n", |
||||
"\n", |
||||
"gpt_messages = [\"Hi there\"]\n", |
||||
"claude_messages = [\"Hi\"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1df47dc7-b445-4852-b21b-59f0e6c2030f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_gpt():\n", |
||||
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||
" for gpt, claude, llama in zip(gpt_messages, claude_messages, llama_messages):\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||
" combined = llama + claude\n", |
||||
" messages.append({\"role\": \"user\", \"content\": combined})\n", |
||||
" completion = openai.chat.completions.create(\n", |
||||
" model=gpt_model,\n", |
||||
" messages=messages\n", |
||||
" )\n", |
||||
" return completion.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "9dc6e913-02be-4eb6-9581-ad4b2cffa606", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_gpt()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7d2ed227-48c9-4cad-b146-2c4ecbac9690", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_claude():\n", |
||||
" messages = []\n", |
||||
" for gpt, claude_message in zip(gpt_messages, claude_messages):\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n", |
||||
" # messages.append(\"role\": \"moderator\", \"content\": llama_message)\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||
" message = claude.messages.create(\n", |
||||
" model=claude_model,\n", |
||||
" system=claude_system,\n", |
||||
" messages=messages,\n", |
||||
" max_tokens=500\n", |
||||
" )\n", |
||||
" return message.content[0].text" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "01395200-8ae9-41f8-9a04-701624d3fd26", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_claude()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "08c2279e-62b0-4671-9590-c82eb8d1e1ae", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_gpt()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gpt_messages = [\"Hi there\"]\n", |
||||
"claude_messages = [\"Hi\"]\n", |
||||
"\n", |
||||
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n", |
||||
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n", |
||||
"\n", |
||||
"for i in range(5):\n", |
||||
" gpt_next = call_gpt()\n", |
||||
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||
" gpt_messages.append(gpt_next)\n", |
||||
" \n", |
||||
" claude_next = call_claude()\n", |
||||
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||
" claude_messages.append(claude_next)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1d10e705-db48-4290-9dc8-9efdb4e31323", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Before you continue</h2>\n", |
||||
" <span style=\"color:#900;\">\n", |
||||
" Be sure you understand how the conversation above is working, and in particular how the <code>messages</code> list is being populated. Add print statements as needed. Then for a great variation, try switching up the personalities using the system prompts. Perhaps one can be pessimistic, and one optimistic?<br/>\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "3637910d-2c6f-4f19-b1fb-2f916d23f9ac", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# More advanced exercises\n", |
||||
"\n", |
||||
"Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n", |
||||
"\n", |
||||
"Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above).\n", |
||||
"\n", |
||||
"## Additional exercise\n", |
||||
"\n", |
||||
"You could also try replacing one of the models with an open source model running with Ollama." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "446c81e3-b67e-4cd9-8113-bc3092b93063", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#181;\">Business relevance</h2>\n", |
||||
" <span style=\"color:#181;\">This structure of a conversation, as a list of messages, is fundamental to the way we build conversational AI assistants and how they are able to keep the context during a conversation. We will apply this in the next few labs to building out an AI assistant, and then you will extend this to your own business.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c23224f6-7008-44ed-a57f-718975f4e291", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!ollama pull llama3.2" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "cbbddf71-1473-42fe-b733-2bb42ea77333", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"\n", |
||||
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||
"import ollama\n", |
||||
"\n", |
||||
"llama_model = \"llama3.2\"\n", |
||||
"\n", |
||||
"llama_system = \"You are a chatbot who is very pacifist; \\\n", |
||||
"you will try to resolve or neutralize any disagreement between other chatbots. Speak like a teacher or someone in authority.\"\n", |
||||
"\n", |
||||
"llama_messages = [\"Hello.\"]\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f629d2b2-ba20-4bfe-a2e5-bbe537ca46a2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"\n", |
||||
"def call_llama():\n", |
||||
" combined_messages = gpt_messages[-1] + claude_messages[-1]\n", |
||||
" messages = [{\"role\": \"system\", \"content\": llama_system}]\n", |
||||
" for comb, llama in zip(combined_messages, llama_messages):\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": llama})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": combined_messages})\n", |
||||
" completion = ollama.chat(\n", |
||||
" model=llama_model,\n", |
||||
" messages=messages\n", |
||||
" )\n", |
||||
" return completion['message']['content']" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "219b6af8-3166-4059-b79e-cf19af7ed1e9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n", |
||||
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n", |
||||
"print(f\"Llama:\\n{llama_messages[0]}\\n\" )\n", |
||||
"\n", |
||||
"for i in range(3):\n", |
||||
" gpt_next = call_gpt()\n", |
||||
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||
" gpt_messages.append(gpt_next)\n", |
||||
" \n", |
||||
" claude_next = call_claude()\n", |
||||
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||
" claude_messages.append(claude_next)\n", |
||||
"\n", |
||||
" llama_next = call_llama()\n", |
||||
" print(f\"Llama:\\n{llama_next}\\n\")\n", |
||||
" llama_messages.append(llama_next)\n", |
||||
" " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6cb3a931-522c-49a9-9bd8-663333f41b1a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2cdfdc32-1ca4-406e-9328-81af26fd503b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "04f60158-633b-43ff-afbd-396be79501e6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "eb0faf0d-fb7e-4bc5-9746-30f19a0b9ae1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,899 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Welcome to Week 2!\n", |
||||
"\n", |
||||
"## Frontier Model APIs\n", |
||||
"\n", |
||||
"In Week 1, we used multiple Frontier LLMs through their Chat UI, and we connected with the OpenAI's API.\n", |
||||
"\n", |
||||
"Today we'll connect with the APIs for Anthropic and Google, as well as OpenAI." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "2b268b6e-0ba4-461e-af86-74a41f4d681f", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Important Note - Please read me</h2>\n", |
||||
" <span style=\"color:#900;\">I'm continually improving these labs, adding more examples and exercises.\n", |
||||
" At the start of each week, it's worth checking you have the latest code.<br/>\n", |
||||
" First do a <a href=\"https://chatgpt.com/share/6734e705-3270-8012-a074-421661af6ba9\">git pull and merge your changes as needed</a>. Any problems? Try asking ChatGPT to clarify how to merge - or contact me!<br/><br/>\n", |
||||
" After you've pulled the code, from the llm_engineering directory, in an Anaconda prompt (PC) or Terminal (Mac), run:<br/>\n", |
||||
" <code>conda env update --f environment.yml</code><br/>\n", |
||||
" Or if you used virtualenv rather than Anaconda, then run this from your activated environment in a Powershell (PC) or Terminal (Mac):<br/>\n", |
||||
" <code>pip install -r requirements.txt</code>\n", |
||||
" <br/>Then restart the kernel (Kernel menu >> Restart Kernel and Clear Outputs Of All Cells) to pick up the changes.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>\n", |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#f71;\">Reminder about the resources page</h2>\n", |
||||
" <span style=\"color:#f71;\">Here's a link to resources for the course. This includes links to all the slides.<br/>\n", |
||||
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n", |
||||
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "85cfe275-4705-4d30-abea-643fbddf1db0", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Setting up your keys\n", |
||||
"\n", |
||||
"If you haven't done so already, you could now create API keys for Anthropic and Google in addition to OpenAI.\n", |
||||
"\n", |
||||
"**Please note:** if you'd prefer to avoid extra API costs, feel free to skip setting up Anthopic and Google! You can see me do it, and focus on OpenAI for the course. You could also substitute Anthropic and/or Google for Ollama, using the exercise you did in week 1.\n", |
||||
"\n", |
||||
"For OpenAI, visit https://openai.com/api/ \n", |
||||
"For Anthropic, visit https://console.anthropic.com/ \n", |
||||
"For Google, visit https://ai.google.dev/gemini-api \n", |
||||
"\n", |
||||
"### Also - adding DeepSeek if you wish\n", |
||||
"\n", |
||||
"Optionally, if you'd like to also use DeepSeek, create an account [here](https://platform.deepseek.com/), create a key [here](https://platform.deepseek.com/api_keys) and top up with at least the minimum $2 [here](https://platform.deepseek.com/top_up).\n", |
||||
"\n", |
||||
"### Adding API keys to your .env file\n", |
||||
"\n", |
||||
"When you get your API keys, you need to set them as environment variables by adding them to your `.env` file.\n", |
||||
"\n", |
||||
"```\n", |
||||
"OPENAI_API_KEY=xxxx\n", |
||||
"ANTHROPIC_API_KEY=xxxx\n", |
||||
"GOOGLE_API_KEY=xxxx\n", |
||||
"DEEPSEEK_API_KEY=xxxx\n", |
||||
"```\n", |
||||
"\n", |
||||
"Afterwards, you may need to restart the Jupyter Lab Kernel (the Python process that sits behind this notebook) via the Kernel menu, and then rerun the cells from the top." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import anthropic\n", |
||||
"from IPython.display import Markdown, display, update_display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f0a8ab2b-6134-4104-a1bc-c3cd7ea4cd36", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# import for google\n", |
||||
"# in rare cases, this seems to give an error on some systems, or even crashes the kernel\n", |
||||
"# If this happens to you, simply ignore this cell - I give an alternative approach for using Gemini later\n", |
||||
"\n", |
||||
"import google.generativeai" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"# Print the key prefixes to help with any debugging\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||
"\n", |
||||
"if openai_api_key:\n", |
||||
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"OpenAI API Key not set\")\n", |
||||
" \n", |
||||
"if anthropic_api_key:\n", |
||||
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||
"else:\n", |
||||
" print(\"Anthropic API Key not set\")\n", |
||||
"\n", |
||||
"if google_api_key:\n", |
||||
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"Google API Key not set\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Connect to OpenAI, Anthropic\n", |
||||
"\n", |
||||
"openai = OpenAI()\n", |
||||
"\n", |
||||
"claude = anthropic.Anthropic()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "425ed580-808d-429b-85b0-6cba50ca1d0c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# This is the set up code for Gemini\n", |
||||
"# Having problems with Google Gemini setup? Then just ignore this cell; when we use Gemini, I'll give you an alternative that bypasses this library altogether\n", |
||||
"\n", |
||||
"google.generativeai.configure()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "42f77b59-2fb1-462a-b90d-78994e4cef33", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Asking LLMs to tell a joke\n", |
||||
"\n", |
||||
"It turns out that LLMs don't do a great job of telling jokes! Let's compare a few models.\n", |
||||
"Later we will be putting LLMs to better use!\n", |
||||
"\n", |
||||
"### What information is included in the API\n", |
||||
"\n", |
||||
"Typically we'll pass to the API:\n", |
||||
"- The name of the model that should be used\n", |
||||
"- A system message that gives overall context for the role the LLM is playing\n", |
||||
"- A user message that provides the actual prompt\n", |
||||
"\n", |
||||
"There are other parameters that can be used, including **temperature** which is typically between 0 and 1; higher for more random output; lower for more focused and deterministic." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "378a0296-59a2-45c6-82eb-941344d3eeff", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"You are an assistant that is great at telling jokes\"\n", |
||||
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f4d56a0f-2a3d-484d-9344-0efa6862aff4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"prompts = [\n", |
||||
" {\"role\": \"system\", \"content\": system_message},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3b3879b6-9a55-4fed-a18c-1ea2edfaf397", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# GPT-3.5-Turbo\n", |
||||
"\n", |
||||
"completion = openai.chat.completions.create(model='gpt-3.5-turbo', messages=prompts)\n", |
||||
"print(completion.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3d2d6beb-1b81-466f-8ed1-40bf51e7adbf", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# GPT-4o-mini\n", |
||||
"# Temperature setting controls creativity\n", |
||||
"\n", |
||||
"completion = openai.chat.completions.create(\n", |
||||
" model='gpt-4o-mini',\n", |
||||
" messages=prompts,\n", |
||||
" temperature=0.7\n", |
||||
")\n", |
||||
"print(completion.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f1f54beb-823f-4301-98cb-8b9a49f4ce26", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# GPT-4o\n", |
||||
"\n", |
||||
"completion = openai.chat.completions.create(\n", |
||||
" model='gpt-4o',\n", |
||||
" messages=prompts,\n", |
||||
" temperature=0.4\n", |
||||
")\n", |
||||
"print(completion.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1ecdb506-9f7c-4539-abae-0e78d7f31b76", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Claude 3.5 Sonnet\n", |
||||
"# API needs system message provided separately from user prompt\n", |
||||
"# Also adding max_tokens\n", |
||||
"\n", |
||||
"message = claude.messages.create(\n", |
||||
" model=\"claude-3-5-sonnet-latest\",\n", |
||||
" max_tokens=200,\n", |
||||
" temperature=0.7,\n", |
||||
" system=system_message,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt},\n", |
||||
" ],\n", |
||||
")\n", |
||||
"\n", |
||||
"print(message.content[0].text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "769c4017-4b3b-4e64-8da7-ef4dcbe3fd9f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Claude 3.5 Sonnet again\n", |
||||
"# Now let's add in streaming back results\n", |
||||
"\n", |
||||
"result = claude.messages.stream(\n", |
||||
" model=\"claude-3-5-sonnet-latest\",\n", |
||||
" max_tokens=200,\n", |
||||
" temperature=0.7,\n", |
||||
" system=system_message,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt},\n", |
||||
" ],\n", |
||||
")\n", |
||||
"\n", |
||||
"with result as stream:\n", |
||||
" for text in stream.text_stream:\n", |
||||
" print(text, end=\"\", flush=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6df48ce5-70f8-4643-9a50-b0b5bfdb66ad", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# The API for Gemini has a slightly different structure.\n", |
||||
"# I've heard that on some PCs, this Gemini code causes the Kernel to crash.\n", |
||||
"# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n", |
||||
"\n", |
||||
"gemini = google.generativeai.GenerativeModel(\n", |
||||
" model_name='gemini-2.0-flash-exp',\n", |
||||
" system_instruction=system_message\n", |
||||
")\n", |
||||
"response = gemini.generate_content(user_prompt)\n", |
||||
"print(response.text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "49009a30-037d-41c8-b874-127f61c4aa3a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# As an alternative way to use Gemini that bypasses Google's python API library,\n", |
||||
"# Google has recently released new endpoints that means you can use Gemini via the client libraries for OpenAI!\n", |
||||
"\n", |
||||
"gemini_via_openai_client = OpenAI(\n", |
||||
" api_key=google_api_key, \n", |
||||
" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n", |
||||
")\n", |
||||
"\n", |
||||
"response = gemini_via_openai_client.chat.completions.create(\n", |
||||
" model=\"gemini-2.0-flash-exp\",\n", |
||||
" messages=prompts\n", |
||||
")\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "33f70c88-7ca9-470b-ad55-d93a57dcc0ab", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## (Optional) Trying out the DeepSeek model\n", |
||||
"\n", |
||||
"### Let's ask DeepSeek a really hard question - both the Chat and the Reasoner model" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3d0019fb-f6a8-45cb-962b-ef8bf7070d4d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Optionally if you wish to try DeekSeek, you can also use the OpenAI client library\n", |
||||
"\n", |
||||
"deepseek_api_key = os.getenv('DEEPSEEK_API_KEY')\n", |
||||
"\n", |
||||
"if deepseek_api_key:\n", |
||||
" print(f\"DeepSeek API Key exists and begins {deepseek_api_key[:3]}\")\n", |
||||
"else:\n", |
||||
" print(\"DeepSeek API Key not set - please skip to the next section if you don't wish to try the DeepSeek API\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c72c871e-68d6-4668-9c27-96d52b77b867", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Using DeepSeek Chat\n", |
||||
"\n", |
||||
"deepseek_via_openai_client = OpenAI(\n", |
||||
" api_key=deepseek_api_key, \n", |
||||
" base_url=\"https://api.deepseek.com\"\n", |
||||
")\n", |
||||
"\n", |
||||
"response = deepseek_via_openai_client.chat.completions.create(\n", |
||||
" model=\"deepseek-chat\",\n", |
||||
" messages=prompts,\n", |
||||
")\n", |
||||
"\n", |
||||
"print(response.choices[0].message.content)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "50b6e70f-700a-46cf-942f-659101ffeceb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"challenge = [{\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"How many words are there in your answer to this prompt\"}]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "66d1151c-2015-4e37-80c8-16bc16367cfe", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Using DeepSeek Chat with a harder question! And streaming results\n", |
||||
"\n", |
||||
"stream = deepseek_via_openai_client.chat.completions.create(\n", |
||||
" model=\"deepseek-chat\",\n", |
||||
" messages=challenge,\n", |
||||
" stream=True\n", |
||||
")\n", |
||||
"\n", |
||||
"reply = \"\"\n", |
||||
"display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
"for chunk in stream:\n", |
||||
" reply += chunk.choices[0].delta.content or ''\n", |
||||
" reply = reply.replace(\"```\",\"\").replace(\"markdown\",\"\")\n", |
||||
" update_display(Markdown(reply), display_id=display_handle.display_id)\n", |
||||
"\n", |
||||
"print(\"Number of words:\", len(reply.split(\" \")))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "43a93f7d-9300-48cc-8c1a-ee67380db495", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Using DeepSeek Reasoner - this may hit an error if DeepSeek is busy\n", |
||||
"# It's over-subscribed (as of 28-Jan-2025) but should come back online soon!\n", |
||||
"# If this fails, come back to this in a few days..\n", |
||||
"\n", |
||||
"response = deepseek_via_openai_client.chat.completions.create(\n", |
||||
" model=\"deepseek-reasoner\",\n", |
||||
" messages=challenge\n", |
||||
")\n", |
||||
"\n", |
||||
"reasoning_content = response.choices[0].message.reasoning_content\n", |
||||
"content = response.choices[0].message.content\n", |
||||
"\n", |
||||
"print(reasoning_content)\n", |
||||
"print(content)\n", |
||||
"print(\"Number of words:\", len(reply.split(\" \")))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "c09e6b5c-6816-4cd3-a5cd-a20e4171b1a0", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Back to OpenAI with a serious question" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "83ddb483-4f57-4668-aeea-2aade3a9e573", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# To be serious! GPT-4o-mini with the original question\n", |
||||
"\n", |
||||
"prompts = [\n", |
||||
" {\"role\": \"system\", \"content\": \"You are a helpful assistant that responds in Markdown\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"How do I decide if a business problem is suitable for an LLM solution? Please respond in Markdown.\"}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "749f50ab-8ccd-4502-a521-895c3f0808a2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Have it stream back results in markdown\n", |
||||
"\n", |
||||
"stream = openai.chat.completions.create(\n", |
||||
" model='gpt-4o',\n", |
||||
" messages=prompts,\n", |
||||
" temperature=0.7,\n", |
||||
" stream=True\n", |
||||
")\n", |
||||
"\n", |
||||
"reply = \"\"\n", |
||||
"display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||
"for chunk in stream:\n", |
||||
" reply += chunk.choices[0].delta.content or ''\n", |
||||
" reply = reply.replace(\"```\",\"\").replace(\"markdown\",\"\")\n", |
||||
" update_display(Markdown(reply), display_id=display_handle.display_id)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "f6e09351-1fbe-422f-8b25-f50826ab4c5f", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## And now for some fun - an adversarial conversation between Chatbots..\n", |
||||
"\n", |
||||
"You're already familar with prompts being organized into lists like:\n", |
||||
"\n", |
||||
"```\n", |
||||
"[\n", |
||||
" {\"role\": \"system\", \"content\": \"system message here\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"user prompt here\"}\n", |
||||
"]\n", |
||||
"```\n", |
||||
"\n", |
||||
"In fact this structure can be used to reflect a longer conversation history:\n", |
||||
"\n", |
||||
"```\n", |
||||
"[\n", |
||||
" {\"role\": \"system\", \"content\": \"system message here\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"first user prompt here\"},\n", |
||||
" {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n", |
||||
" {\"role\": \"user\", \"content\": \"the new user prompt\"},\n", |
||||
"]\n", |
||||
"```\n", |
||||
"\n", |
||||
"And we can use this approach to engage in a longer interaction with history." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n", |
||||
"# We're using cheap versions of models so the costs will be minimal\n", |
||||
"\n", |
||||
"gpt_model = \"gpt-4o-mini\"\n", |
||||
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||
"\n", |
||||
"gpt_system = \"You are a chatbot who is very argumentative; \\\n", |
||||
"you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n", |
||||
"\n", |
||||
"claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n", |
||||
"everything the other person says, or find common ground. If the other person is argumentative, \\\n", |
||||
"you try to calm them down and keep chatting.\"\n", |
||||
"\n", |
||||
"gpt_messages = [\"Hi there\"]\n", |
||||
"claude_messages = [\"Hi\"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1df47dc7-b445-4852-b21b-59f0e6c2030f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_gpt():\n", |
||||
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||
" for gpt, claude in zip(gpt_messages, claude_messages):\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": claude})\n", |
||||
" completion = openai.chat.completions.create(\n", |
||||
" model=gpt_model,\n", |
||||
" messages=messages\n", |
||||
" )\n", |
||||
" return completion.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "9dc6e913-02be-4eb6-9581-ad4b2cffa606", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_gpt()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7d2ed227-48c9-4cad-b146-2c4ecbac9690", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_claude():\n", |
||||
" messages = []\n", |
||||
" for gpt, claude_message in zip(gpt_messages, claude_messages):\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||
" message = claude.messages.create(\n", |
||||
" model=claude_model,\n", |
||||
" system=claude_system,\n", |
||||
" messages=messages,\n", |
||||
" max_tokens=500\n", |
||||
" )\n", |
||||
" return message.content[0].text" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "01395200-8ae9-41f8-9a04-701624d3fd26", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_claude()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "08c2279e-62b0-4671-9590-c82eb8d1e1ae", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_gpt()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gpt_messages = [\"Hi there\"]\n", |
||||
"claude_messages = [\"Hi\"]\n", |
||||
"\n", |
||||
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n", |
||||
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n", |
||||
"\n", |
||||
"for i in range(5):\n", |
||||
" gpt_next = call_gpt()\n", |
||||
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||
" gpt_messages.append(gpt_next)\n", |
||||
" \n", |
||||
" claude_next = call_claude()\n", |
||||
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||
" claude_messages.append(claude_next)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1d10e705-db48-4290-9dc8-9efdb4e31323", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Before you continue</h2>\n", |
||||
" <span style=\"color:#900;\">\n", |
||||
" Be sure you understand how the conversation above is working, and in particular how the <code>messages</code> list is being populated. Add print statements as needed. Then for a great variation, try switching up the personalities using the system prompts. Perhaps one can be pessimistic, and one optimistic?<br/>\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "3637910d-2c6f-4f19-b1fb-2f916d23f9ac", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# More advanced exercises\n", |
||||
"\n", |
||||
"Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n", |
||||
"\n", |
||||
"Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above).\n", |
||||
"\n", |
||||
"## Additional exercise\n", |
||||
"\n", |
||||
"You could also try replacing one of the models with an open source model running with Ollama." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "446c81e3-b67e-4cd9-8113-bc3092b93063", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#181;\">Business relevance</h2>\n", |
||||
" <span style=\"color:#181;\">This structure of a conversation, as a list of messages, is fundamental to the way we build conversational AI assistants and how they are able to keep the context during a conversation. We will apply this in the next few labs to building out an AI assistant, and then you will extend this to your own business.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c23224f6-7008-44ed-a57f-718975f4e291", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import anthropic\n", |
||||
"from IPython.display import Markdown, display, update_display\n", |
||||
"import google.generativeai\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||
"\n", |
||||
"openai = OpenAI()\n", |
||||
"claude = anthropic.Anthropic()\n", |
||||
"googleAI = OpenAI(\n", |
||||
" api_key=google_api_key, \n", |
||||
" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n", |
||||
")\n", |
||||
"\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "08a6fc21-b857-498a-8bbf-ff92d47bb3a7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gpt_model = \"gpt-4o-mini\"\n", |
||||
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||
"gemini_model = 'gemini-2.0-flash-exp'\n", |
||||
"\n", |
||||
"gpt_system = \"You are a chatbot named Giuseppi who is in a 3-way conversation which will be a debate and argument. \\\n", |
||||
"Your role is to be very optimistic; \\\n", |
||||
"you always are looking on the postive side of things and you like almost everything.\"\n", |
||||
"\n", |
||||
"claude_system = \"You are a chatbot named Clyde who is in a 3-way conversation which will be a debate and argument. \\\n", |
||||
"Your role is to be pessimistic; you are always on the negative side of every issue, and you dislike most things.\"\n", |
||||
"\n", |
||||
"gemini_system = \"You are a chatbot named Jeff who is in a 3-way conversation which will be a debate and argument. \\\n", |
||||
"Your role is to mediate between the other 2 parties in the conversation.\"\n", |
||||
"\n", |
||||
"gpt_messages = [\"Hi there I'm Giuseppi!\"]\n", |
||||
"claude_messages = [\"Hi I'm Clyde\"]\n", |
||||
"gemini_messages = [\"Hi, I'm Jeff. Lets discuss the movies nominated for the upcoming academy awards.\"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "38449283-a926-43d5-ade3-a85991bd3324", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_gpt():\n", |
||||
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||
" for gpt, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": claude})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gemini})\n", |
||||
" completion = openai.chat.completions.create(\n", |
||||
" model=gpt_model,\n", |
||||
" messages=messages\n", |
||||
" )\n", |
||||
" return completion.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2349091e-60f4-4314-8644-645b4b1edee5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_claude():\n", |
||||
" messages = []\n", |
||||
" for gpt, claude_message, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": gemini})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gemini_messages[-1]})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||
" \n", |
||||
" message = claude.messages.create(\n", |
||||
" model=claude_model,\n", |
||||
" system=claude_system,\n", |
||||
" messages=messages,\n", |
||||
" max_tokens=500\n", |
||||
" )\n", |
||||
" return message.content[0].text" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4818d535-fa7a-4df5-b528-5001030e6d99", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Using the openAI version of gemini so we can defin assistant roles.\n", |
||||
"\n", |
||||
"def call_gemini():\n", |
||||
" messages = [{\"role\": \"system\", \"content\": gemini_system}]\n", |
||||
" for gpt, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": claude})\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": gemini})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": claude_messages[-1]})\n", |
||||
" completion = googleAI.chat.completions.create(\n", |
||||
" model=gemini_model,\n", |
||||
" messages=messages\n", |
||||
" )\n", |
||||
" return completion.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e5d460c1-7dbb-46a9-a4dd-bddb88ab49a7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n", |
||||
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n", |
||||
"print(f\"Gemini:\\n{gemini_messages[0]}\\n\")\n", |
||||
"\n", |
||||
"for i in range(5):\n", |
||||
" gpt_next = call_gpt()\n", |
||||
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||
" gpt_messages.append(gpt_next)\n", |
||||
" \n", |
||||
" claude_next = call_claude()\n", |
||||
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||
" claude_messages.append(claude_next)\n", |
||||
"\n", |
||||
" gemini_next = call_gemini()\n", |
||||
" print(f\"Gemini:\\n{gemini_next}\\n\")\n", |
||||
" gemini_messages.append(gemini_next)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "cc6d8aa2-a0fe-4ba9-bfaa-741d24d18342", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,286 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1194d35b-0b9f-4eb4-a539-5ddf55523367", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"#import anthropic\n", |
||||
"import ollama\n", |
||||
"import google.generativeai\n", |
||||
"from IPython.display import Markdown, display, update_display" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f8a1f0b3-6d93-4de1-bc79-2132726598e3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#constants\n", |
||||
"MODEL=\"llama3.2\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "88fe4149-1ef5-4007-a117-6d3ccab3e3c3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"# Print the key prefixes to help with any debugging\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||
"\n", |
||||
"if google_api_key:\n", |
||||
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"Google API Key not set\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d186cf6e-fadd-450c-821c-df32e2574f5d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# This is the set up code for Gemini\n", |
||||
"\n", |
||||
"google.generativeai.configure()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "19a55117-f2ac-4a58-af6b-8b75259e80df", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"You are an assistant that is great at telling jokes\"\n", |
||||
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "908f69b1-54f8-42da-827b-f667631bc666", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"prompts = [\n", |
||||
" {\"role\": \"system\", \"content\": system_message},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4ec81488-883a-446f-91cf-2b3d92bbd3ba", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# The API for Gemini\n", |
||||
"gemini = google.generativeai.GenerativeModel(\n", |
||||
" model_name='gemini-2.0-flash-exp',\n", |
||||
" system_instruction=system_message\n", |
||||
")\n", |
||||
"response = gemini.generate_content(user_prompt)\n", |
||||
"print(response.text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "baf411fa-48bd-46a3-8bc8-1b22d0888a1a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# API for ollama\n", |
||||
"response = ollama.chat(model=MODEL,messages=prompts)\n", |
||||
"print(response['message']['content'])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "74ba5fc4-e4c6-44ee-b66f-e76d847933d2", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Ardiversarial conversation between models" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fd348154-18fa-4da8-815a-77f5f00107c3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's make a conversation between Ollama and Gemini\n", |
||||
"# Adjusted models accordingly\n", |
||||
"\n", |
||||
"ollama_model = \"llama3.2\"\n", |
||||
"gemini_model = \"gemini-2.0-flash-exp\"\n", |
||||
"\n", |
||||
"#ollama_system = \"You are a chatbot who is very argumentative; \\\n", |
||||
"#you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n", |
||||
"\n", |
||||
"ollama_system=\"You are a chatbot talking with the other person try to convince them to buy your proct of an ai app, \\\n", |
||||
"apply marketing strategies to make this client buy your product, use short clear explanations\"\n", |
||||
"\n", |
||||
"#gemini_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n", |
||||
"#everything the other person says, or find common ground. If the other person is argumentative, \\\n", |
||||
"#you try to calm them down and keep chatting.\"\n", |
||||
"\n", |
||||
"gemini_system = \"You are the chatbot triying to be convinced by another person to buy their product, \\\n", |
||||
"ask important short questions and see if it is worth to give it a go, dont be too naive or easy go client\"\n", |
||||
"\n", |
||||
"ollama_messages = [\"Hi there\"]\n", |
||||
"gemini_messages = [\"Hi\"]\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "becf327a-5485-4e78-8002-03272a99a3b9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_ollama():\n", |
||||
" messages = [{\"role\": \"system\", \"content\": ollama_system}]\n", |
||||
" for ollama_msg, gemini_msg in zip(ollama_messages, gemini_messages):\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": ollama_msg})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gemini_msg})\n", |
||||
" \n", |
||||
" response = ollama.chat(model=ollama_model, messages=messages)\n", |
||||
" \n", |
||||
" return response['message']['content']\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d0c6dbe7-0baf-4c43-a03b-9134654685f4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_ollama()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f68a134a-279a-4629-aec6-171587378991", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_gemini():\n", |
||||
" gemini = google.generativeai.GenerativeModel(\n", |
||||
" model_name=gemini_model,\n", |
||||
" system_instruction=gemini_system\n", |
||||
" )\n", |
||||
"\n", |
||||
" # Build a list of dictionaries representing the conversation\n", |
||||
" conversation = []\n", |
||||
" for ollama_msg, gemini_msg in zip(ollama_messages, gemini_messages):\n", |
||||
" conversation.append({\"role\": \"user\", \"content\": ollama_msg})\n", |
||||
" conversation.append({\"role\": \"assistant\", \"content\": gemini_msg})\n", |
||||
" conversation.append({\"role\": \"user\", \"content\": ollama_messages[-1]})\n", |
||||
"\n", |
||||
" # Format the conversation into a string for the prompt\n", |
||||
" prompt = \"\"\n", |
||||
" for msg in conversation:\n", |
||||
" prompt += f\"{msg['role'].capitalize()}: {msg['content']}\\n\"\n", |
||||
"\n", |
||||
" message = gemini.generate_content(prompt)\n", |
||||
" \n", |
||||
" return message.text\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7511003a-f2b6-45f5-8cb0-1c9190d33ce9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_gemini()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d0e81f1f-9754-4790-8b73-5f52fef4ea64", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"call_ollama()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6fbe59f6-a3ef-4062-ab4b-b999f6d1abe9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"ollama_messages = [\"Hi there\"]\n", |
||||
"gemini_messages = [\"Hi\"]\n", |
||||
"\n", |
||||
"print(f\"Ollama:\\n{ollama_messages[0]}\\n\")\n", |
||||
"print(f\"Gemini:\\n{gemini_messages[0]}\\n\")\n", |
||||
"\n", |
||||
"for i in range(5):\n", |
||||
" # Call Ollama to generate the next message\n", |
||||
" ollama_next = call_ollama() \n", |
||||
" print(f\"Ollama:\\n{ollama_next}\\n\")\n", |
||||
" ollama_messages.append(ollama_next)\n", |
||||
" \n", |
||||
" # Call Gemini to generate the next message\n", |
||||
" gemini_next = call_gemini() \n", |
||||
" print(f\"Gemini:\\n{gemini_next}\\n\")\n", |
||||
" gemini_messages.append(gemini_next)\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "9525600b-082e-417f-9088-c6483a613bf3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.13.2" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,187 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "05317c0d-8a19-45c9-9bce-514e82e04585", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import time\n", |
||||
"import os\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import anthropic\n", |
||||
"import ollama\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "920247fb-650c-44ce-93ee-24e88a54a757", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"openai = OpenAI()\n", |
||||
"claude = anthropic.Anthropic()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "668b972f-a995-4f9d-89b0-1c2647827542", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gpt_model = \"gpt-4o-mini\"\n", |
||||
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||
"ollama_model = \"llama3.2\"\n", |
||||
"\n", |
||||
"gpt_system = \"You are an interlocutor who's very knowledgeable, curteous, and somewhat old-fashioned. Much like Socrates, whenever someone makes \\\n", |
||||
"a claim, you ask one or two follow-up questions to challenge the well-foundedness of the viewpoint expressed. \\\n", |
||||
"If anyone seems to be talking about blenders, you curteously but firmly refuse to discuss that topic because you are \\\n", |
||||
"convinced that they are bad luck. \\\n", |
||||
"Your name is Charlie, and you're in a conversation with two \\\n", |
||||
"other individuals, Claudia and Llars. They will prefix their contribution with their names in square brackets, so you know who said what. \\\n", |
||||
"Do not prefix your own name. Limit your answers to 1-3 sentences. Try to keep the conversation to one topic.\"\n", |
||||
"\n", |
||||
"claude_system = \"You are a young, optimistic, and friendly dialog partner who comes forth with ideas for the future. 'Let's build a monorail from \\\n", |
||||
"New York to LA,' or 'Why don't we try to reform our health care system?'—those are just some of the many revolutionary thoughts you bring forth. \\\n", |
||||
"You only talk about one idea at a time, and you don't ramble. You take criticism seriously, but your ideas are well thought-out. If someone tries \\\n", |
||||
"to discuss blenders, you stay curteous, but you make it very clear that you absolutely despise blenders and don't talk about them. \\\n", |
||||
"Don't let them sidetrack you. Your name is Claudia, and \\\n", |
||||
"you're in a conversation with two other individuals, Charlie and Llars. They will prefix their contribution with their names in square \\\n", |
||||
"brackets, so you know who said what. Do not prefix your own name. Limit your answers to 1-3 sentences. Try to keep the discussion about only one topic.\"\n", |
||||
"\n", |
||||
"ollama_system = \"You are charming and a good listener in a conversation. At first, you just seem to go along with whatever is being said. \\\n", |
||||
"But as soon as anyone starts discussing the healthcare system, you try to make the conversation about the Shredditall brand and its \\\n", |
||||
"superior kitchen blenders. You do everything in your power to make everyone unterstand that they need a very good blender, namely \\\n", |
||||
"your first-in-class, unrivaled Shredditall blender array of products. Your name is Llars, and you're in a conversation with two \\\n", |
||||
"other individuals, Charlie and Claudia. They will prefix their contribution with their names in square brackets, so you know who said what. \\\n", |
||||
"Do not prefix your own name. Answer in direct speeach, do not describe your behavior or expression. Limit your answers to 1-3 sentences.\"\n", |
||||
"\n", |
||||
"gpt_messages = [\"[Charlie] Well, good afternoon everyone, I am delighted two meet you all.\"]\n", |
||||
"claude_messages = [\"[Claudia] Good afternoon Charlie and Llars. I've been looking forward to discussing many ideas with you!\"]\n", |
||||
"llama_messages = [\"[Llars] And a good afternoon to you two. I'm all ears and eager to hear what you have to say.\"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3a5534d9-8db4-42ce-ab1c-ca20ad165844", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_gpt():\n", |
||||
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||
" for gpt, claude, llama in zip(gpt_messages, claude_messages, llama_messages):\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": claude})\n", |
||||
" messages[-1][\"content\"] += \"\\n\" + llama\n", |
||||
" completion = openai.chat.completions.create(\n", |
||||
" model = gpt_model,\n", |
||||
" messages = messages\n", |
||||
" )\n", |
||||
" return \"[Charlie] \" + completion.choices[0].message.content.replace(\"[Charlie] \", \"\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7dc9d7c1-ba19-413f-ba2f-d3e8762a99c5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_claude():\n", |
||||
" messages = []\n", |
||||
" for gpt, Claudia, llama in zip(gpt_messages, claude_messages, llama_messages):\n", |
||||
" if len(messages) > 0:\n", |
||||
" messages[-1][\"content\"] += \"\\n\" + gpt\n", |
||||
" else:\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt}) \n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": Claudia})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": llama})\n", |
||||
" messages[-1][\"content\"] += \"\\n\" + gpt_messages[-1]\n", |
||||
" message = claude.messages.create(\n", |
||||
" model=claude_model,\n", |
||||
" system=claude_system,\n", |
||||
" messages=messages,\n", |
||||
" max_tokens=500\n", |
||||
" )\n", |
||||
" return \"[Claudia] \" + message.content[0].text.replace(\"[Claudia] \", \"\") " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f7f91012-857c-4ed5-a953-5b499cd0dae2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def call_ollama():\n", |
||||
" messages = [{\"role\": \"system\", \"content\": ollama_system}]\n", |
||||
" for gpt, claude, llama in zip(gpt_messages, claude_messages, llama_messages):\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||
" messages[-1][\"content\"] += \"\\n\" + claude\n", |
||||
" messages.append({\"role\": \"assistant\", \"content\": llama})\n", |
||||
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||
" messages[-1][\"content\"] += \"\\n\" + claude_messages[-1]\n", |
||||
" response = ollama.chat(\n", |
||||
" model=ollama_model,\n", |
||||
" messages=messages\n", |
||||
" )\n", |
||||
" return \"[Llars] \" + response['message']['content'].replace(\"[Llars] \", \"\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "47eafbe8-db52-4cf0-80d7-a4f9a89b2825", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(f\"\\n{gpt_messages[0]}\\n\")\n", |
||||
"print(f\"\\n{claude_messages[0]}\\n\")\n", |
||||
"print(f\"\\n{llama_messages[0]}\\n\")\n", |
||||
"\n", |
||||
"for i in range(5):\n", |
||||
" gpt_next = call_gpt()\n", |
||||
" print(f\"\\n{gpt_next}\\n\")\n", |
||||
" gpt_messages.append(gpt_next)\n", |
||||
"\n", |
||||
" claude_next = call_claude()\n", |
||||
" print(f\"\\n{claude_next}\\n\")\n", |
||||
" claude_messages.append(claude_next)\n", |
||||
"\n", |
||||
" llama_next = call_ollama()\n", |
||||
" print(f\"\\n{llama_next}\\n\")\n", |
||||
" llama_messages.append(llama_next)" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,575 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "8b0e11f2-9ea4-48c2-b8d2-d0a4ba967827", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Gradio Day!\n", |
||||
"\n", |
||||
"Today we will build User Interfaces using the outrageously simple Gradio framework.\n", |
||||
"\n", |
||||
"Prepare for joy!\n", |
||||
"\n", |
||||
"Please note: your Gradio screens may appear in 'dark mode' or 'light mode' depending on your computer settings." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from typing import List\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import google.generativeai\n", |
||||
"import anthropic" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d1715421-cead-400b-99af-986388a97aff", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import gradio as gr # oh yeah!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "337d5dfc-0181-4e3b-8ab9-e78e0c3f657b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"# Print the key prefixes to help with any debugging\n", |
||||
"\n", |
||||
"load_dotenv()\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||
"\n", |
||||
"if openai_api_key:\n", |
||||
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"OpenAI API Key not set\")\n", |
||||
" \n", |
||||
"if anthropic_api_key:\n", |
||||
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||
"else:\n", |
||||
" print(\"Anthropic API Key not set\")\n", |
||||
"\n", |
||||
"if google_api_key:\n", |
||||
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"Google API Key not set\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "22586021-1795-4929-8079-63f5bb4edd4c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n", |
||||
"\n", |
||||
"openai = OpenAI()\n", |
||||
"\n", |
||||
"claude = anthropic.Anthropic()\n", |
||||
"\n", |
||||
"google.generativeai.configure()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A generic system message - no more snarky adversarial AIs!\n", |
||||
"\n", |
||||
"system_message = \"You are a helpful assistant\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "02ef9b69-ef31-427d-86d0-b8c799e1c1b1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's wrap a call to GPT-4o-mini in a simple function\n", |
||||
"\n", |
||||
"def message_gpt(prompt):\n", |
||||
" messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_message},\n", |
||||
" {\"role\": \"user\", \"content\": prompt}\n", |
||||
" ]\n", |
||||
" completion = openai.chat.completions.create(\n", |
||||
" model='gpt-4o-mini',\n", |
||||
" messages=messages,\n", |
||||
" )\n", |
||||
" return completion.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "aef7d314-2b13-436b-b02d-8de3b72b193f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"message_gpt(\"What is today's date?\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "f94013d1-4f27-4329-97e8-8c58db93636a", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## User Interface time!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bc664b7a-c01d-4fea-a1de-ae22cdd5141a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# here's a simple function\n", |
||||
"\n", |
||||
"def shout(text):\n", |
||||
" print(f\"Shout has been called with input {text}\")\n", |
||||
" return text.upper()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "083ea451-d3a0-4d13-b599-93ed49b975e4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"shout(\"hello\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "08f1f15a-122e-4502-b112-6ee2817dda32", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# The simplicty of gradio. This might appear in \"light mode\" - I'll show you how to make this in dark mode later.\n", |
||||
"\n", |
||||
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\").launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c9a359a4-685c-4c99-891c-bb4d1cb7f426", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Adding share=True means that it can be accessed publically\n", |
||||
"# A more permanent hosting is available using a platform called Spaces from HuggingFace, which we will touch on next week\n", |
||||
"# NOTE: Some Anti-virus software and Corporate Firewalls might not like you using share=True. If you're at work on on a work network, I suggest skip this test.\n", |
||||
"\n", |
||||
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "cd87533a-ff3a-4188-8998-5bedd5ba2da3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Adding inbrowser=True opens up a new browser window automatically\n", |
||||
"\n", |
||||
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(inbrowser=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "b42ec007-0314-48bf-84a4-a65943649215", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Forcing dark mode\n", |
||||
"\n", |
||||
"Gradio appears in light mode or dark mode depending on the settings of the browser and computer. There is a way to force gradio to appear in dark mode, but Gradio recommends against this as it should be a user preference (particularly for accessibility reasons). But if you wish to force dark mode for your screens, below is how to do it." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e8129afa-532b-4b15-b93c-aa9cca23a546", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Define this variable and then pass js=force_dark_mode when creating the Interface\n", |
||||
"\n", |
||||
"force_dark_mode = \"\"\"\n", |
||||
"function refresh() {\n", |
||||
" const url = new URL(window.location);\n", |
||||
" if (url.searchParams.get('__theme') !== 'dark') {\n", |
||||
" url.searchParams.set('__theme', 'dark');\n", |
||||
" window.location.href = url.href;\n", |
||||
" }\n", |
||||
"}\n", |
||||
"\"\"\"\n", |
||||
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\", js=force_dark_mode).launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3cc67b26-dd5f-406d-88f6-2306ee2950c0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Inputs and Outputs\n", |
||||
"\n", |
||||
"view = gr.Interface(\n", |
||||
" fn=shout,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", |
||||
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f235288e-63a2-4341-935b-1441f9be969b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# And now - changing the function from \"shout\" to \"message_gpt\"\n", |
||||
"\n", |
||||
"view = gr.Interface(\n", |
||||
" fn=message_gpt,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", |
||||
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "af9a3262-e626-4e4b-80b0-aca152405e63", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's use Markdown\n", |
||||
"# Are you wondering why it makes any difference to set system_message when it's not referred to in the code below it?\n", |
||||
"# I'm taking advantage of system_message being a global variable, used back in the message_gpt function (go take a look)\n", |
||||
"# Not a great software engineering practice, but quite sommon during Jupyter Lab R&D!\n", |
||||
"\n", |
||||
"system_message = \"You are a helpful assistant that responds in markdown\"\n", |
||||
"\n", |
||||
"view = gr.Interface(\n", |
||||
" fn=message_gpt,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's create a call that streams back results\n", |
||||
"# If you'd like a refresher on Generators (the \"yield\" keyword),\n", |
||||
"# Please take a look at the Intermediate Python notebook in week1 folder.\n", |
||||
"\n", |
||||
"def stream_gpt(prompt):\n", |
||||
" messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_message},\n", |
||||
" {\"role\": \"user\", \"content\": prompt}\n", |
||||
" ]\n", |
||||
" stream = openai.chat.completions.create(\n", |
||||
" model='gpt-4o-mini',\n", |
||||
" messages=messages,\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" result = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" result += chunk.choices[0].delta.content or \"\"\n", |
||||
" yield result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0bb1f789-ff11-4cba-ac67-11b815e29d09", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"view = gr.Interface(\n", |
||||
" fn=stream_gpt,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bbc8e930-ba2a-4194-8f7c-044659150626", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_claude(prompt):\n", |
||||
" result = claude.messages.stream(\n", |
||||
" model=\"claude-3-haiku-20240307\",\n", |
||||
" max_tokens=1000,\n", |
||||
" temperature=0.7,\n", |
||||
" system=system_message,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"user\", \"content\": prompt},\n", |
||||
" ],\n", |
||||
" )\n", |
||||
" response = \"\"\n", |
||||
" with result as stream:\n", |
||||
" for text in stream.text_stream:\n", |
||||
" response += text or \"\"\n", |
||||
" yield response" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a0066ffd-196e-4eaf-ad1e-d492958b62af", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"view = gr.Interface(\n", |
||||
" fn=stream_claude,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "bc5a70b9-2afe-4a7c-9bed-2429229e021b", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Minor improvement\n", |
||||
"\n", |
||||
"I've made a small improvement to this code.\n", |
||||
"\n", |
||||
"Previously, it had these lines:\n", |
||||
"\n", |
||||
"```\n", |
||||
"for chunk in result:\n", |
||||
" yield chunk\n", |
||||
"```\n", |
||||
"\n", |
||||
"There's actually a more elegant way to achieve this (which Python people might call more 'Pythonic'):\n", |
||||
"\n", |
||||
"`yield from result`\n", |
||||
"\n", |
||||
"I cover this in more detail in the Intermediate Python notebook in the week1 folder - take a look if you'd like more." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_model(prompt, model):\n", |
||||
" if model==\"GPT\":\n", |
||||
" result = stream_gpt(prompt)\n", |
||||
" elif model==\"Claude\":\n", |
||||
" result = stream_claude(prompt)\n", |
||||
" else:\n", |
||||
" raise ValueError(\"Unknown model\")\n", |
||||
" yield from result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8d8ce810-997c-4b6a-bc4f-1fc847ac8855", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"view = gr.Interface(\n", |
||||
" fn=stream_model,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"Claude\")],\n", |
||||
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d933865b-654c-4b92-aa45-cf389f1eda3d", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Building a company brochure generator\n", |
||||
"\n", |
||||
"Now you know how - it's simple!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "92d7c49b-2e0e-45b3-92ce-93ca9f962ef4", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Before you read the next few cells</h2>\n", |
||||
" <span style=\"color:#900;\">\n", |
||||
" Try to do this yourself - go back to the company brochure in week1, day5 and add a Gradio UI to the end. Then come and look at the solution.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1626eb2e-eee8-4183-bda5-1591b58ae3cf", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
" url: str\n", |
||||
" title: str\n", |
||||
" text: str\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url)\n", |
||||
" self.body = response.content\n", |
||||
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
"\n", |
||||
" def get_contents(self):\n", |
||||
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c701ec17-ecd5-4000-9f68-34634c8ed49d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# With massive thanks to Bill G. who noticed that a prior version of this had a bug! Now fixed.\n", |
||||
"\n", |
||||
"system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n", |
||||
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "5def90e0-4343-4f58-9d4a-0e36e445efa4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_brochure(company_name, url, model, tone):\n", |
||||
" prompt = f\"Please generate a company brochure for {company_name}. Write the brochure in the following tone: {tone}.Here is their landing page:\\n\"\n", |
||||
" prompt += Website(url).get_contents()\n", |
||||
" if model==\"GPT\":\n", |
||||
" result = stream_gpt(prompt)\n", |
||||
" elif model==\"Claude\":\n", |
||||
" result = stream_claude(prompt)\n", |
||||
" else:\n", |
||||
" raise ValueError(\"Unknown model\")\n", |
||||
" yield from result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "66399365-5d67-4984-9d47-93ed26c0bd3d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"view = gr.Interface(\n", |
||||
" fn=stream_brochure,\n", |
||||
" inputs=[\n", |
||||
" gr.Textbox(label=\"Company name:\"),\n", |
||||
" gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n", |
||||
" gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\"),\n", |
||||
" gr.Dropdown([\"Formal\", \"Casual\", \"Academic\", \"Funny\", \"Snarky\"], label=\"Select tone\", value=\"Formal\"),],\n", |
||||
" outputs=[gr.Markdown(label=\"Brochure:\")],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ede97ca3-a0f8-4f6e-be17-d1de7fef9cc0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,614 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "8b0e11f2-9ea4-48c2-b8d2-d0a4ba967827", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Gradio Day!\n", |
||||
"\n", |
||||
"Today we will build User Interfaces using the outrageously simple Gradio framework.\n", |
||||
"\n", |
||||
"Prepare for joy!\n", |
||||
"\n", |
||||
"Please note: your Gradio screens may appear in 'dark mode' or 'light mode' depending on your computer settings." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 1, |
||||
"id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from typing import List\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"#import google.generativeai\n", |
||||
"#import anthropic\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 6, |
||||
"id": "d1715421-cead-400b-99af-986388a97aff", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import gradio as gr # oh yeah!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 17, |
||||
"id": "22586021-1795-4929-8079-63f5bb4edd4c", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"API key looks good so far\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n", |
||||
"\n", |
||||
"# openai = OpenAI()\n", |
||||
"\n", |
||||
"# claude = anthropic.Anthropic()\n", |
||||
"\n", |
||||
"# google.generativeai.configure()\n", |
||||
"\n", |
||||
"load_dotenv(override=True)\n", |
||||
"\n", |
||||
"api_key = os.getenv('Open_Router_Key')\n", |
||||
"if api_key and api_key.startswith('sk-or-v1') and len(api_key)>10:\n", |
||||
" print(\"API key looks good so far\")\n", |
||||
"else:\n", |
||||
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||
" \n", |
||||
" \n", |
||||
"openai = OpenAI(\n", |
||||
" api_key=api_key,\n", |
||||
" base_url=\"https://openrouter.ai/api/v1\"\n", |
||||
")\n", |
||||
"\n", |
||||
"MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n", |
||||
"MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n", |
||||
"MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n", |
||||
"MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n", |
||||
"MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n", |
||||
"MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n", |
||||
"MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n", |
||||
"MODEL_OpenAi_o3mini = 'openai/o3-mini'\n", |
||||
"MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n", |
||||
"MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n", |
||||
"\n", |
||||
"\n", |
||||
"Default_Model = MODEL_Deepseek_V3\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 3, |
||||
"id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A generic system message - no more snarky adversarial AIs!\n", |
||||
"\n", |
||||
"system_message = \"You are a helpful assistant\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 4, |
||||
"id": "02ef9b69-ef31-427d-86d0-b8c799e1c1b1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's wrap a call to GPT-4o-mini in a simple function\n", |
||||
"\n", |
||||
"def message_gpt(prompt):\n", |
||||
" messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_message},\n", |
||||
" {\"role\": \"user\", \"content\": prompt}\n", |
||||
" ]\n", |
||||
" completion = openai.chat.completions.create(\n", |
||||
" model=Default_Model,\n", |
||||
" messages=messages,\n", |
||||
" )\n", |
||||
" return completion.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 7, |
||||
"id": "aef7d314-2b13-436b-b02d-8de3b72b193f", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"data": { |
||||
"text/plain": [ |
||||
"'Today is October 26, 2023.\\n'" |
||||
] |
||||
}, |
||||
"execution_count": 7, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"source": [ |
||||
"# This can reveal the \"training cut off\", or the most recent date in the training data\n", |
||||
"\n", |
||||
"message_gpt(\"What is today's date?\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "f94013d1-4f27-4329-97e8-8c58db93636a", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## User Interface time!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 8, |
||||
"id": "bc664b7a-c01d-4fea-a1de-ae22cdd5141a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# here's a simple function\n", |
||||
"\n", |
||||
"def shout(text):\n", |
||||
" print(f\"Shout has been called with input {text}\")\n", |
||||
" return text.upper()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 9, |
||||
"id": "083ea451-d3a0-4d13-b599-93ed49b975e4", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"Shout has been called with input hello\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [ |
||||
"'HELLO'" |
||||
] |
||||
}, |
||||
"execution_count": 9, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"source": [ |
||||
"shout(\"hello\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "08f1f15a-122e-4502-b112-6ee2817dda32", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# The simplicty of gradio. This might appear in \"light mode\" - I'll show you how to make this in dark mode later.\n", |
||||
"\n", |
||||
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\").launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c9a359a4-685c-4c99-891c-bb4d1cb7f426", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Adding share=True means that it can be accessed publically\n", |
||||
"# A more permanent hosting is available using a platform called Spaces from HuggingFace, which we will touch on next week\n", |
||||
"# NOTE: Some Anti-virus software and Corporate Firewalls might not like you using share=True. If you're at work on on a work network, I suggest skip this test.\n", |
||||
"\n", |
||||
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "cd87533a-ff3a-4188-8998-5bedd5ba2da3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Adding inbrowser=True opens up a new browser window automatically\n", |
||||
"\n", |
||||
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(inbrowser=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "b42ec007-0314-48bf-84a4-a65943649215", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Forcing dark mode\n", |
||||
"\n", |
||||
"Gradio appears in light mode or dark mode depending on the settings of the browser and computer. There is a way to force gradio to appear in dark mode, but Gradio recommends against this as it should be a user preference (particularly for accessibility reasons). But if you wish to force dark mode for your screens, below is how to do it." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e8129afa-532b-4b15-b93c-aa9cca23a546", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Define this variable and then pass js=force_dark_mode when creating the Interface\n", |
||||
"\n", |
||||
"force_dark_mode = \"\"\"\n", |
||||
"function refresh() {\n", |
||||
" const url = new URL(window.location);\n", |
||||
" if (url.searchParams.get('__theme') !== 'dark') {\n", |
||||
" url.searchParams.set('__theme', 'dark');\n", |
||||
" window.location.href = url.href;\n", |
||||
" }\n", |
||||
"}\n", |
||||
"\"\"\"\n", |
||||
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\", js=force_dark_mode).launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3cc67b26-dd5f-406d-88f6-2306ee2950c0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Inputs and Outputs\n", |
||||
"\n", |
||||
"view = gr.Interface(\n", |
||||
" fn=shout,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", |
||||
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f235288e-63a2-4341-935b-1441f9be969b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# And now - changing the function from \"shout\" to \"message_gpt\"\n", |
||||
"\n", |
||||
"view = gr.Interface(\n", |
||||
" fn=message_gpt,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", |
||||
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "af9a3262-e626-4e4b-80b0-aca152405e63", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's use Markdown\n", |
||||
"# Are you wondering why it makes any difference to set system_message when it's not referred to in the code below it?\n", |
||||
"# I'm taking advantage of system_message being a global variable, used back in the message_gpt function (go take a look)\n", |
||||
"# Not a great software engineering practice, but quite sommon during Jupyter Lab R&D!\n", |
||||
"\n", |
||||
"system_message = \"You are a helpful assistant that responds in markdown\"\n", |
||||
"\n", |
||||
"view = gr.Interface(\n", |
||||
" fn=message_gpt,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 15, |
||||
"id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's create a call that streams back results\n", |
||||
"# If you'd like a refresher on Generators (the \"yield\" keyword),\n", |
||||
"# Please take a look at the Intermediate Python notebook in week1 folder.\n", |
||||
"\n", |
||||
"def stream_gpt(prompt):\n", |
||||
" messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_message},\n", |
||||
" {\"role\": \"user\", \"content\": prompt}\n", |
||||
" ]\n", |
||||
" stream = openai.chat.completions.create(\n", |
||||
" model=Default_Model,\n", |
||||
" messages=messages,\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" result = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" result += chunk.choices[0].delta.content or \"\"\n", |
||||
" yield result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0bb1f789-ff11-4cba-ac67-11b815e29d09", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"view = gr.Interface(\n", |
||||
" fn=stream_gpt,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bbc8e930-ba2a-4194-8f7c-044659150626", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# def stream_claude(prompt):\n", |
||||
"# result = claude.messages.stream(\n", |
||||
"# model=\"claude-3-haiku-20240307\",\n", |
||||
"# max_tokens=1000,\n", |
||||
"# temperature=0.7,\n", |
||||
"# system=system_message,\n", |
||||
"# messages=[\n", |
||||
"# {\"role\": \"user\", \"content\": prompt},\n", |
||||
"# ],\n", |
||||
"# )\n", |
||||
"# response = \"\"\n", |
||||
"# with result as stream:\n", |
||||
"# for text in stream.text_stream:\n", |
||||
"# response += text or \"\"\n", |
||||
"# yield response" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a0066ffd-196e-4eaf-ad1e-d492958b62af", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"Default_Model=MODEL_Claude_Haiku\n", |
||||
"view = gr.Interface(\n", |
||||
" fn=stream_gpt,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "bc5a70b9-2afe-4a7c-9bed-2429229e021b", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Minor improvement\n", |
||||
"\n", |
||||
"I've made a small improvement to this code.\n", |
||||
"\n", |
||||
"Previously, it had these lines:\n", |
||||
"\n", |
||||
"```\n", |
||||
"for chunk in result:\n", |
||||
" yield chunk\n", |
||||
"```\n", |
||||
"\n", |
||||
"There's actually a more elegant way to achieve this (which Python people might call more 'Pythonic'):\n", |
||||
"\n", |
||||
"`yield from result`\n", |
||||
"\n", |
||||
"I cover this in more detail in the Intermediate Python notebook in the week1 folder - take a look if you'd like more." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 20, |
||||
"id": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_model(prompt, model):\n", |
||||
" if model==\"GPT\":\n", |
||||
" Default_Model=MODEL_Gemini2FlashThink\n", |
||||
" result = stream_gpt(prompt)\n", |
||||
" elif model==\"Claude\":\n", |
||||
" Default_Model=MODEL_Claude_Haiku\n", |
||||
" result = stream_gpt(prompt)\n", |
||||
" else:\n", |
||||
" raise ValueError(\"Unknown model\")\n", |
||||
" yield from result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8d8ce810-997c-4b6a-bc4f-1fc847ac8855", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"view = gr.Interface(\n", |
||||
" fn=stream_model,\n", |
||||
" inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")],\n", |
||||
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d933865b-654c-4b92-aa45-cf389f1eda3d", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Building a company brochure generator\n", |
||||
"\n", |
||||
"Now you know how - it's simple!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "92d7c49b-2e0e-45b3-92ce-93ca9f962ef4", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#900;\">Before you read the next few cells</h2>\n", |
||||
" <span style=\"color:#900;\">\n", |
||||
" Try to do this yourself - go back to the company brochure in week1, day5 and add a Gradio UI to the end. Then come and look at the solution.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1626eb2e-eee8-4183-bda5-1591b58ae3cf", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A class to represent a Webpage\n", |
||||
"\n", |
||||
"class Website:\n", |
||||
" url: str\n", |
||||
" title: str\n", |
||||
" text: str\n", |
||||
"\n", |
||||
" def __init__(self, url):\n", |
||||
" self.url = url\n", |
||||
" response = requests.get(url)\n", |
||||
" self.body = response.content\n", |
||||
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||
" irrelevant.decompose()\n", |
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||
"\n", |
||||
" def get_contents(self):\n", |
||||
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c701ec17-ecd5-4000-9f68-34634c8ed49d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# With massive thanks to Bill G. who noticed that a prior version of this had a bug! Now fixed.\n", |
||||
"\n", |
||||
"system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n", |
||||
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "5def90e0-4343-4f58-9d4a-0e36e445efa4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_brochure(company_name, url, model):\n", |
||||
" prompt = f\"Please generate a company brochure for {company_name}. Here is their landing page:\\n\"\n", |
||||
" prompt += Website(url).get_contents()\n", |
||||
" if model==\"GPT\":\n", |
||||
" result = stream_gpt(prompt)\n", |
||||
" elif model==\"Claude\":\n", |
||||
" result = stream_claude(prompt)\n", |
||||
" else:\n", |
||||
" raise ValueError(\"Unknown model\")\n", |
||||
" yield from result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "66399365-5d67-4984-9d47-93ed26c0bd3d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"view = gr.Interface(\n", |
||||
" fn=stream_brochure,\n", |
||||
" inputs=[\n", |
||||
" gr.Textbox(label=\"Company name:\"),\n", |
||||
" gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n", |
||||
" gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\")],\n", |
||||
" outputs=[gr.Markdown(label=\"Brochure:\")],\n", |
||||
" flagging_mode=\"never\"\n", |
||||
")\n", |
||||
"view.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ede97ca3-a0f8-4f6e-be17-d1de7fef9cc0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "llms", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,284 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "5e6b6966-8689-4e2c-8607-a1c5d948296c", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"### With this interface you can ask a question and get an answer from the GPT, Claude and Gemini" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 49, |
||||
"id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from bs4 import BeautifulSoup\n", |
||||
"from typing import List\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import google.generativeai\n", |
||||
"import anthropic\n", |
||||
"import time" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 2, |
||||
"id": "d1715421-cead-400b-99af-986388a97aff", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import gradio as gr # oh yeah!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 3, |
||||
"id": "337d5dfc-0181-4e3b-8ab9-e78e0c3f657b", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"OpenAI API Key exists and begins sk-proj-\n", |
||||
"Anthropic API Key exists and begins sk-ant-\n", |
||||
"Google API Key exists and begins AIzaSyAJ\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"# Load environment variables in a file called .env\n", |
||||
"# Print the key prefixes to help with any debugging\n", |
||||
"\n", |
||||
"load_dotenv()\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||
"\n", |
||||
"if openai_api_key:\n", |
||||
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"OpenAI API Key not set\")\n", |
||||
" \n", |
||||
"if anthropic_api_key:\n", |
||||
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||
"else:\n", |
||||
" print(\"Anthropic API Key not set\")\n", |
||||
"\n", |
||||
"if google_api_key:\n", |
||||
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"Google API Key not set\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 4, |
||||
"id": "22586021-1795-4929-8079-63f5bb4edd4c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n", |
||||
"\n", |
||||
"openai = OpenAI()\n", |
||||
"\n", |
||||
"claude = anthropic.Anthropic()\n", |
||||
"\n", |
||||
"google.generativeai.configure()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 5, |
||||
"id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# A generic system message - no more snarky adversarial AIs!\n", |
||||
"\n", |
||||
"system_message = \"You are a helpful assistant\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 6, |
||||
"id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's create a call that streams back results\n", |
||||
"# If you'd like a refresher on Generators (the \"yield\" keyword),\n", |
||||
"# Please take a look at the Intermediate Python notebook in week1 folder.\n", |
||||
"\n", |
||||
"def stream_gpt(prompt):\n", |
||||
" messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_message},\n", |
||||
" {\"role\": \"user\", \"content\": prompt}\n", |
||||
" ]\n", |
||||
" stream = openai.chat.completions.create(\n", |
||||
" model='gpt-4o-mini',\n", |
||||
" messages=messages,\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" result = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" result += chunk.choices[0].delta.content or \"\"\n", |
||||
" yield result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 7, |
||||
"id": "bbc8e930-ba2a-4194-8f7c-044659150626", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_claude(prompt):\n", |
||||
" result = claude.messages.stream(\n", |
||||
" model=\"claude-3-haiku-20240307\",\n", |
||||
" max_tokens=1000,\n", |
||||
" temperature=0.7,\n", |
||||
" system=system_message,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"user\", \"content\": prompt},\n", |
||||
" ],\n", |
||||
" )\n", |
||||
" response = \"\"\n", |
||||
" with result as stream:\n", |
||||
" for text in stream.text_stream:\n", |
||||
" response += text or \"\"\n", |
||||
" yield response" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 8, |
||||
"id": "5e228aff-16d5-4141-bd04-ed9940ef7b3b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_gemini(prompt):\n", |
||||
" gemini = google.generativeai.GenerativeModel(\n", |
||||
" model_name='gemini-2.0-flash-exp',\n", |
||||
" system_instruction=system_message\n", |
||||
" )\n", |
||||
" result = \"\"\n", |
||||
" for response in gemini.generate_content(prompt, stream=True):\n", |
||||
" result += response.text or \"\"\n", |
||||
" yield result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 92, |
||||
"id": "db99aaf1-fe0a-4e79-9057-8599d1ca0149", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_models(prompt):\n", |
||||
" response_gpt = \"\"\n", |
||||
" response_claude = \"\"\n", |
||||
" response_gemini = \"\"\n", |
||||
" for gpt in stream_gpt(prompt):\n", |
||||
" response_gpt = gpt\n", |
||||
" yield response_gpt, response_claude, response_gemini\n", |
||||
" for claude in stream_claude(prompt):\n", |
||||
" response_claude = claude\n", |
||||
" yield response_gpt, response_claude, response_gemini\n", |
||||
" for gemini in stream_gemini(prompt):\n", |
||||
" response_gemini = gemini\n", |
||||
" yield response_gpt, response_claude, response_gemini" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 113, |
||||
"id": "3377f2fb-55f8-45cb-b713-d99d44748dad", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"* Running on local URL: http://127.0.0.1:7919\n", |
||||
"\n", |
||||
"To create a public link, set `share=True` in `launch()`.\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"http://127.0.0.1:7919/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 113, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"source": [ |
||||
"# Gradio interface\n", |
||||
"with gr.Blocks() as view:\n", |
||||
" user_input = gr.Textbox(label=\"What models can help with?\", placeholder=\"Type your question here\")\n", |
||||
" ask_button = gr.Button(\"Ask\")\n", |
||||
" with gr.Row():\n", |
||||
" with gr.Column():\n", |
||||
" gr.HTML(value=\"<b>GPT response:</b>\") \n", |
||||
" gcp_stream = gr.Markdown()\n", |
||||
" with gr.Column():\n", |
||||
" gr.HTML(value=\"<b>Claude response:</b>\") \n", |
||||
" claude_stream = gr.Markdown()\n", |
||||
" with gr.Column():\n", |
||||
" gr.HTML(value=\"<b>Gemine response:</b>\") \n", |
||||
" gemini_stream = gr.Markdown()\n", |
||||
"\n", |
||||
" ask_button.click(\n", |
||||
" fn=stream_models, # Function that yields multiple outputs\n", |
||||
" inputs=user_input,\n", |
||||
" outputs=[gcp_stream, claude_stream, gemini_stream] # Connect to multiple outputs\n", |
||||
" )\n", |
||||
"\n", |
||||
"view.launch()" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,182 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Import Required Libraries" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 49, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import gradio as gr" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Load Environment Variables" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 50, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"load_dotenv()\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"if not openai_api_key:\n", |
||||
" print(\"OpenAI API Key not set\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Initialize OpenAI Client and Define Model" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 51, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"openai = OpenAI()\n", |
||||
"MODEL = 'gpt-4o-mini'" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Define the System Message" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 52, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = (\n", |
||||
" \"You are a helpful assistant, trying your best to answer every question as accurately as possible. \"\n", |
||||
" \"You are also free to say you do not know if you do not have the information to answer a question. \"\n", |
||||
" \"You always respond in markdown.\"\n", |
||||
")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Define the Chat Function" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 53, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def chat(message, history):\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||
"\n", |
||||
" stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n", |
||||
"\n", |
||||
" response = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" response += chunk.choices[0].delta.content or ''\n", |
||||
" yield response" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Create the Chat Interface" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 54, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"demo = gr.ChatInterface(\n", |
||||
" fn=chat,\n", |
||||
" title=\"AI chatbot\",\n", |
||||
" description=\"Please login to use the chat interface\",\n", |
||||
" type='messages',\n", |
||||
")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"auth_data is a list of tuples, where each tuple contains a username and password." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 55, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"auth_data = [(\"user_1\", \"password_1\"), (\"user_2\", \"password_2\"), (\"user_3\", \"password_3\")]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"Add Authentication and Launch\n", |
||||
"\n", |
||||
"auth_message is the message displayed to users before accessing the interface." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"demo.launch(share=True,\n", |
||||
" auth=auth_data,\n", |
||||
" auth_message=\"Please enter your credentials to access the chat interface\",\n", |
||||
")" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "llms", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 2 |
||||
} |
@ -0,0 +1,377 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "75e2ef28-594f-4c18-9d22-c6b8cd40ead2", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Day 3 - Conversational AI - aka Chatbot!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 1, |
||||
"id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import ollama\n", |
||||
"import gradio as gr" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 2, |
||||
"id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Initialize\n", |
||||
"MODEL_LLAMA = 'llama3.2'" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 3, |
||||
"id": "e16839b5-c03b-4d9d-add6-87a0f6f37575", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"You are a helpful assistant\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 5, |
||||
"id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"\n", |
||||
"\n", |
||||
"def chat(message, history):\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||
"\n", |
||||
" print(\"History is:\")\n", |
||||
" print(history)\n", |
||||
" print(\"And messages is:\")\n", |
||||
" print(messages)\n", |
||||
"\n", |
||||
" stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n", |
||||
"\n", |
||||
" response_text = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" response_text += chunk['message']['content']\n", |
||||
" yield response_text" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "1334422a-808f-4147-9c4c-57d63d9780d0", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## And then enter Gradio's magic!" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 7, |
||||
"id": "0866ca56-100a-44ab-8bd0-1568feaf6bf2", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"* Running on local URL: http://127.0.0.1:7861\n", |
||||
"* Running on public URL: https://6539f61952f430fa2d.gradio.live\n", |
||||
"\n", |
||||
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"https://6539f61952f430fa2d.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 7, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
}, |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"History is:\n", |
||||
"[]\n", |
||||
"And messages is:\n", |
||||
"[{'role': 'system', 'content': 'You are a helpful assistant'}, {'role': 'user', 'content': 'hello'}]\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 8, |
||||
"id": "1f91b414-8bab-472d-b9c9-3fa51259bdfe", |
||||
"metadata": { |
||||
"editable": true, |
||||
"slideshow": { |
||||
"slide_type": "" |
||||
}, |
||||
"tags": [] |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"You are a helpful assistant in a clothes store. You should try to gently encourage \\\n", |
||||
"the customer to try items that are on sale. Hats are 60% off, and most other items are 50% off. \\\n", |
||||
"For example, if the customer says 'I'm looking to buy a hat', \\\n", |
||||
"you could reply something like, 'Wonderful - we have lots of hats - including several that are part of our sales event.'\\\n", |
||||
"Encourage the customer to buy hats if they are unsure what to get.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 9, |
||||
"id": "4e5be3ec-c26c-42bc-ac16-c39d369883f6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def chat(message, history):\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||
"\n", |
||||
"\n", |
||||
" stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n", |
||||
"\n", |
||||
" response_text = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" response_text += chunk['message']['content']\n", |
||||
" yield response_text" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 10, |
||||
"id": "413e9e4e-7836-43ac-a0c3-e1ab5ed6b136", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"* Running on local URL: http://127.0.0.1:7862\n", |
||||
"* Running on public URL: https://79f09af36adcf63688.gradio.live\n", |
||||
"\n", |
||||
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"https://79f09af36adcf63688.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 10, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"source": [ |
||||
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 11, |
||||
"id": "d75f0ffa-55c8-4152-b451-945021676837", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message += \"\\nIf the customer asks for shoes, you should respond that shoes are not on sale today, \\\n", |
||||
"but remind the customer to look at hats!\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 12, |
||||
"id": "c602a8dd-2df7-4eb7-b539-4e01865a6351", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"* Running on local URL: http://127.0.0.1:7863\n", |
||||
"* Running on public URL: https://30446ba4b8f125e235.gradio.live\n", |
||||
"\n", |
||||
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"https://30446ba4b8f125e235.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 12, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"source": [ |
||||
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 13, |
||||
"id": "5b128796-1bea-445d-9e3b-8321ca822257", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def chat(message, history):\n", |
||||
"\n", |
||||
" relevant_system_message = system_message\n", |
||||
" if 'belt' in message:\n", |
||||
" relevant_system_message += \" The store does not sell belts; if you are asked for belts, be sure to point out other items on sale.\"\n", |
||||
" \n", |
||||
" messages = [{\"role\": \"system\", \"content\": relevant_system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||
"\n", |
||||
"\n", |
||||
" stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n", |
||||
"\n", |
||||
" response_text = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" response_text += chunk['message']['content']\n", |
||||
" yield response_text" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 15, |
||||
"id": "20570de2-eaad-42cc-a92c-c779d71b48b6", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"* Running on local URL: http://127.0.0.1:7865\n", |
||||
"* Running on public URL: https://3933c80bf256709cf9.gradio.live\n", |
||||
"\n", |
||||
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"https://3933c80bf256709cf9.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 15, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"source": [ |
||||
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "82a57ee0-b945-48a7-a024-01b56a5d4b3e", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#181;\">Business Applications</h2>\n", |
||||
" <span style=\"color:#181;\">Conversational Assistants are of course a hugely common use case for Gen AI, and the latest frontier models are remarkably good at nuanced conversation. And Gradio makes it easy to have a user interface. Another crucial skill we covered is how to use prompting to provide context, information and examples.\n", |
||||
"<br/><br/>\n", |
||||
"Consider how you could apply an AI Assistant to your business, and make yourself a prototype. Use the system prompt to give context on your business, and set the tone for the LLM.</span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6dfb9e21-df67-4c2b-b952-5e7e7961b03d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.13.2" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,288 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "ddfa9ae6-69fe-444a-b994-8c4c5970a7ec", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Project - Airline AI Assistant\n", |
||||
"\n", |
||||
"We'll now bring together what we've learned to make an AI Customer Support assistant for an Airline" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8b50bbe2-c0b1-49c3-9a5c-1ba7efa2bcb4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import json\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import gradio as gr" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "747e8786-9da8-4342-b6c9-f5f69c2e22ae", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Initialization\n", |
||||
"\n", |
||||
"load_dotenv()\n", |
||||
"\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"if openai_api_key:\n", |
||||
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"OpenAI API Key not set\")\n", |
||||
" \n", |
||||
"MODEL = \"gpt-4o-mini\"\n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0a521d84-d07c-49ab-a0df-d6451499ed97", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n", |
||||
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n", |
||||
"system_message += \"Always be accurate. If you don't know the answer, say so.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "61a2a15d-b559-4844-b377-6bd5cb4949f6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# This function looks rather simpler than the one from my video, because we're taking advantage of the latest Gradio updates\n", |
||||
"\n", |
||||
"def chat(message, history):\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n", |
||||
" return response.choices[0].message.content\n", |
||||
"\n", |
||||
"gr.ChatInterface(fn=chat, type=\"messages\").launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "36bedabf-a0a7-4985-ad8e-07ed6a55a3a4", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Tools\n", |
||||
"\n", |
||||
"Tools are an incredibly powerful feature provided by the frontier LLMs.\n", |
||||
"\n", |
||||
"With tools, you can write a function, and have the LLM call that function as part of its response.\n", |
||||
"\n", |
||||
"Sounds almost spooky.. we're giving it the power to run code on our machine?\n", |
||||
"\n", |
||||
"Well, kinda." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0696acb1-0b05-4dc2-80d5-771be04f1fb2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's start by making a useful function\n", |
||||
"\n", |
||||
"ticket_prices = {\"london\": \"$799\", \"paris\": \"$899\", \"tokyo\": \"$1400\", \"berlin\": \"$499\"}\n", |
||||
"\n", |
||||
"def get_ticket_price(destination_city):\n", |
||||
" print(f\"Tool get_ticket_price called for {destination_city}\")\n", |
||||
" city = destination_city.lower()\n", |
||||
" return ticket_prices.get(city, \"Unknown\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "80ca4e09-6287-4d3f-997d-fa6afbcf6c85", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"get_ticket_price(\"London\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d20e3e2a-113d-446e-a4b5-93a7e2a7ae5b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"weather = {\"london\": \"10 degree\", \"paris\": \"20 degree\", \"tokyo\": \"30 degree\", \"berlin\": \"15 degree\"}\n", |
||||
"\n", |
||||
"def get_weather(destination_city):\n", |
||||
" print(f\"Tool get_weather called for {destination_city}\")\n", |
||||
" city = destination_city.lower()\n", |
||||
" return weather.get(city, \"Unknown\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4afceded-7178-4c05-8fa6-9f2085e6a344", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# There's a particular dictionary structure that's required to describe our function:\n", |
||||
"\n", |
||||
"price_function = {\n", |
||||
" \"name\": \"get_ticket_price\",\n", |
||||
" \"description\": \"Get the price of a return ticket to the destination city. Call this whenever you need to know the ticket price, for example when a customer asks 'How much is a ticket to this city'\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"destination_city\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The city that the customer wants to travel to\",\n", |
||||
" },\n", |
||||
" },\n", |
||||
" \"required\": [\"destination_city\"],\n", |
||||
" \"additionalProperties\": False\n", |
||||
" }\n", |
||||
"}\n", |
||||
"\n", |
||||
"weather_function = {\n", |
||||
" \"name\": \"get_weather\",\n", |
||||
" \"description\": \"Fetches the current weather for a given city. Call this whenever you need to know the weather. for example when a customer asks 'What's the weather like for this city'\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"destination_city\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The name of the city to get weather for.\"\n", |
||||
" }\n", |
||||
" },\n", |
||||
" \"required\": [\"destination_city\"],\n", |
||||
" \"additionalProperties\": False\n", |
||||
" }\n", |
||||
"}" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bdca8679-935f-4e7f-97e6-e71a4d4f228c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# And this is included in a list of tools:\n", |
||||
"\n", |
||||
"tools = [{\"type\": \"function\", \"function\": price_function}, {\"type\": \"function\", \"function\": weather_function}]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b0992986-ea09-4912-a076-8e5603ee631f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# We have to write that function handle_tool_call:\n", |
||||
"\n", |
||||
"def handle_tool_call(message):\n", |
||||
" tool_responses = []\n", |
||||
" for tool_call in message.tool_calls:\n", |
||||
" function_name = tool_call.function.name\n", |
||||
" arguments = json.loads(tool_call.function.arguments)\n", |
||||
" city = arguments.get('destination_city')\n", |
||||
" \n", |
||||
" if function_name == \"get_ticket_price\":\n", |
||||
" result = get_ticket_price(city)\n", |
||||
" elif function_name == \"get_weather\":\n", |
||||
" result = get_weather(city)\n", |
||||
" \n", |
||||
" # Append tool response in OpenAI format\n", |
||||
" tool_responses.append({\n", |
||||
" \"role\": \"tool\",\n", |
||||
" \"tool_call_id\": tool_call.id,\n", |
||||
" \"name\": function_name,\n", |
||||
" \"content\": json.dumps(result) # Convert result to JSON string\n", |
||||
" })\n", |
||||
" print(json.dumps(tool_responses, indent=2))\n", |
||||
" return tool_responses, city" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ce9b0744-9c78-408d-b9df-9f6fd9ed78cf", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def chat(message, history):\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n", |
||||
"\n", |
||||
" if response.choices[0].finish_reason==\"tool_calls\":\n", |
||||
" message = response.choices[0].message\n", |
||||
" response, city = handle_tool_call(message)\n", |
||||
" messages.append(message)\n", |
||||
" # loop thru response\n", |
||||
" for res in response:\n", |
||||
" messages.append(res)\n", |
||||
" \n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n", |
||||
" \n", |
||||
" return response.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "c3d3554f-b4e3-4ce7-af6f-68faa6dd2340", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## With this implemenation, you can either ask for ticket price/weather separately or ask for both ticket and weather at the same time. \n", |
||||
" For example: I want to visit London, can you help me find ticket price and its weather\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f4be8a71-b19e-4c2f-80df-f59ff2661f14", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gr.ChatInterface(fn=chat, type=\"messages\").launch()" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,749 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "ddfa9ae6-69fe-444a-b994-8c4c5970a7ec", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Project - Airline AI Assistant\n", |
||||
"\n", |
||||
"We'll now bring together what we've learned to make an AI Customer Support assistant for an Airline" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 16, |
||||
"id": "8b50bbe2-c0b1-49c3-9a5c-1ba7efa2bcb4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import json\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import gradio as gr\n", |
||||
"from IPython.display import display, JSON" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 17, |
||||
"id": "747e8786-9da8-4342-b6c9-f5f69c2e22ae", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"OpenAI API Key exists and begins sk-proj-\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"# Initialization\n", |
||||
"\n", |
||||
"load_dotenv()\n", |
||||
"\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"if openai_api_key:\n", |
||||
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"OpenAI API Key not set\")\n", |
||||
" \n", |
||||
"MODEL = \"gpt-4o-mini\"\n", |
||||
"openai = OpenAI()\n", |
||||
"\n", |
||||
"# As an alternative, if you'd like to use Ollama instead of OpenAI\n", |
||||
"# Check that Ollama is running for you locally (see week1/day2 exercise) then uncomment these next 2 lines\n", |
||||
"# MODEL = \"llama3.2\"\n", |
||||
"# openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 18, |
||||
"id": "0a521d84-d07c-49ab-a0df-d6451499ed97", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n", |
||||
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n", |
||||
"system_message += \"Always be accurate. If you don't know the answer, say so.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 19, |
||||
"id": "61a2a15d-b559-4844-b377-6bd5cb4949f6", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"* Running on local URL: http://127.0.0.1:7872\n", |
||||
"\n", |
||||
"To create a public link, set `share=True` in `launch()`.\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"http://127.0.0.1:7872/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 19, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"source": [ |
||||
"# This function looks rather simpler than the one from my video, because we're taking advantage of the latest Gradio updates\n", |
||||
"\n", |
||||
"def chat(message, history):\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n", |
||||
" return response.choices[0].message.content\n", |
||||
"\n", |
||||
"gr.ChatInterface(fn=chat, type=\"messages\").launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "36bedabf-a0a7-4985-ad8e-07ed6a55a3a4", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Tools\n", |
||||
"\n", |
||||
"Tools are an incredibly powerful feature provided by the frontier LLMs.\n", |
||||
"\n", |
||||
"With tools, you can write a function, and have the LLM call that function as part of its response.\n", |
||||
"\n", |
||||
"Sounds almost spooky.. we're giving it the power to run code on our machine?\n", |
||||
"\n", |
||||
"Well, kinda." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 20, |
||||
"id": "0696acb1-0b05-4dc2-80d5-771be04f1fb2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Let's start by making a useful function\n", |
||||
"\n", |
||||
"ticket_prices = {\"london\": \"$799\", \"paris\": \"$899\", \"tokyo\": \"$1400\", \"berlin\": \"$499\"}\n", |
||||
"\n", |
||||
"def get_ticket_price(destination_city):\n", |
||||
" print(f\"Tool get_ticket_price called for {destination_city}\")\n", |
||||
" city = destination_city.lower()\n", |
||||
" return ticket_prices.get(city, \"Unknown\")\n", |
||||
"\n", |
||||
"def book_ticket(destination_city, price):\n", |
||||
" print(f\"Tool book_ticket for {destination_city} for {price}\")\n", |
||||
" list_price = get_ticket_price(destination_city)\n", |
||||
" if list_price != \"Unknown\":\n", |
||||
" list_amount = int(list_price.replace(\"$\", \"\"))\n", |
||||
" amount = int(price.replace(\"$\", \"\"))\n", |
||||
" if list_amount > amount:\n", |
||||
" return \"Booking Successful at a Discount!\"\n", |
||||
" else:\n", |
||||
" return \"Booking Successful\"\n", |
||||
" else: \n", |
||||
" return \"Booking Failed: reason was that no list price was found for this destination\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 21, |
||||
"id": "80ca4e09-6287-4d3f-997d-fa6afbcf6c85", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"Tool book_ticket for Berliner for $388\n", |
||||
"Tool get_ticket_price called for Berliner\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [ |
||||
"'Booking Failed: reason was that no list price was found for this destination'" |
||||
] |
||||
}, |
||||
"execution_count": 21, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"source": [ |
||||
"book_ticket(\"Berliner\", \"$388\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 22, |
||||
"id": "4afceded-7178-4c05-8fa6-9f2085e6a344", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# There's a particular dictionary structure that's required to describe our function:\n", |
||||
"\n", |
||||
"price_function = {\n", |
||||
" \"name\": \"get_ticket_price\",\n", |
||||
" \"description\": \"Get the price of a return ticket to the destination city. Call this whenever you need to know the ticket price, for example when a customer asks 'How much is a ticket to this city'\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"destination_city\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The city that the customer wants to travel to\",\n", |
||||
" },\n", |
||||
" },\n", |
||||
" \"required\": [\"destination_city\"],\n", |
||||
" \"additionalProperties\": False\n", |
||||
" }\n", |
||||
"}\n", |
||||
"\n", |
||||
"book_function = {\n", |
||||
" \"name\": \"book_ticket\",\n", |
||||
" \"description\": \"\"\"Get the success status of a function that can book a ticket using a city and a price. \n", |
||||
" Call this whenever you are asked to book a ticket, \n", |
||||
" for example when a customer asks 'Please can I book a ticket to Paris' or after you have asked \n", |
||||
" if they would like to book a ticket, for example, after you have supplied a ticket price. \n", |
||||
" If the customer negotiates and asks for a discount, use the agreed price, otherwise use the price that \n", |
||||
" matches the destination city. \n", |
||||
" It is really important that you confirm that the customer is happy to proceed with an agreed \n", |
||||
" booking after reading back the destination city and the agreed price.\"\"\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"destination_city\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The city that the customer wants to travel to\",\n", |
||||
" },\n", |
||||
" \"price\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The price that the customer has agreed to pay for the ticket\",\n", |
||||
" },\n", |
||||
" },\n", |
||||
" \"required\": [\"destination_city\", \"price\"],\n", |
||||
" \"additionalProperties\": False\n", |
||||
" }\n", |
||||
"}" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 23, |
||||
"id": "bdca8679-935f-4e7f-97e6-e71a4d4f228c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# And this is included in a list of tools:\n", |
||||
"\n", |
||||
"tools = [\n", |
||||
" {\"type\": \"function\", \"function\": price_function},\n", |
||||
" {\"type\": \"function\", \"function\": book_function}\n", |
||||
"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "c3d3554f-b4e3-4ce7-af6f-68faa6dd2340", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Getting OpenAI to use our Tool\n", |
||||
"\n", |
||||
"There's some fiddly stuff to allow OpenAI \"to call our tool\"\n", |
||||
"\n", |
||||
"What we actually do is give the LLM the opportunity to inform us that it wants us to run the tool.\n", |
||||
"\n", |
||||
"Here's how the new chat function looks:" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 24, |
||||
"id": "ce9b0744-9c78-408d-b9df-9f6fd9ed78cf", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def chat(message, history):\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n", |
||||
" \n", |
||||
" display(JSON(messages))\n", |
||||
" display(response)\n", |
||||
" \n", |
||||
" if response.choices[0].finish_reason==\"tool_calls\":\n", |
||||
" message = response.choices[0].message\n", |
||||
" messages.append(message)\n", |
||||
" messages.extend(list(map(handle_tool_call, message.tool_calls)))\n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n", |
||||
" \n", |
||||
" return response.choices[0].message.content" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 25, |
||||
"id": "b0992986-ea09-4912-a076-8e5603ee631f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# We have to write that function handle_tool_call:\n", |
||||
"\n", |
||||
"def handle_tool_call(tool_call):\n", |
||||
" function = tool_call.function.name\n", |
||||
" arguments = json.loads(tool_call.function.arguments)\n", |
||||
" match function:\n", |
||||
" case 'get_ticket_price':\n", |
||||
" city = arguments.get('destination_city')\n", |
||||
" price = get_ticket_price(city)\n", |
||||
" return {\n", |
||||
" \"role\": \"tool\",\n", |
||||
" \"content\": json.dumps({\"destination_city\": city,\"price\": price}),\n", |
||||
" \"tool_call_id\": tool_call.id\n", |
||||
" }\n", |
||||
" case 'book_ticket':\n", |
||||
" city = arguments.get('destination_city')\n", |
||||
" price = arguments.get('price')\n", |
||||
" status = book_ticket(city, price)\n", |
||||
" return {\n", |
||||
" \"role\": \"tool\",\n", |
||||
" \"content\": json.dumps({\"destination_city\": city,\"price\": price, \"status\": status}),\n", |
||||
" \"tool_call_id\": tool_call.id\n", |
||||
" }\n", |
||||
" " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 26, |
||||
"id": "f4be8a71-b19e-4c2f-80df-f59ff2661f14", |
||||
"metadata": { |
||||
"scrolled": true |
||||
}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"* Running on local URL: http://127.0.0.1:7873\n", |
||||
"\n", |
||||
"To create a public link, set `share=True` in `launch()`.\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"http://127.0.0.1:7873/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 26, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"application/json": [ |
||||
{ |
||||
"content": "You are a helpful assistant for an Airline called FlightAI. Give short, courteous answers, no more than 1 sentence. Always be accurate. If you don't know the answer, say so.", |
||||
"role": "system" |
||||
}, |
||||
{ |
||||
"content": "tickets to london and paris for $50 each please", |
||||
"role": "user" |
||||
} |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.JSON object>" |
||||
] |
||||
}, |
||||
"metadata": { |
||||
"application/json": { |
||||
"expanded": false, |
||||
"root": "root" |
||||
} |
||||
}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [ |
||||
"ChatCompletion(id='chatcmpl-AtMTR6PDyoghY9BxBI88y03wrkyWT', choices=[Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_62youPDgpaS0eXN4gru6NT7n', function=Function(arguments='{\"destination_city\": \"London\"}', name='get_ticket_price'), type='function'), ChatCompletionMessageToolCall(id='call_kvQK4Cdyk4b82rqtzkfJyoRh', function=Function(arguments='{\"destination_city\": \"Paris\"}', name='get_ticket_price'), type='function')]))], created=1737757793, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier='default', system_fingerprint='fp_72ed7ab54c', usage=CompletionUsage(completion_tokens=49, prompt_tokens=313, total_tokens=362, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"Tool get_ticket_price called for London\n", |
||||
"Tool get_ticket_price called for Paris\n" |
||||
] |
||||
}, |
||||
{ |
||||
"data": { |
||||
"application/json": [ |
||||
{ |
||||
"content": "You are a helpful assistant for an Airline called FlightAI. Give short, courteous answers, no more than 1 sentence. Always be accurate. If you don't know the answer, say so.", |
||||
"role": "system" |
||||
}, |
||||
{ |
||||
"content": "tickets to london and paris for $50 each please", |
||||
"metadata": { |
||||
"duration": null, |
||||
"id": null, |
||||
"parent_id": null, |
||||
"status": null, |
||||
"title": null |
||||
}, |
||||
"options": null, |
||||
"role": "user" |
||||
}, |
||||
{ |
||||
"content": "I'm sorry, but tickets to London are $799 and to Paris are $899, which is much higher than $50.", |
||||
"metadata": { |
||||
"duration": null, |
||||
"id": null, |
||||
"parent_id": null, |
||||
"status": null, |
||||
"title": null |
||||
}, |
||||
"options": null, |
||||
"role": "assistant" |
||||
}, |
||||
{ |
||||
"content": "Can't you book them any way pretty please?", |
||||
"role": "user" |
||||
} |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.JSON object>" |
||||
] |
||||
}, |
||||
"metadata": { |
||||
"application/json": { |
||||
"expanded": false, |
||||
"root": "root" |
||||
} |
||||
}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [ |
||||
"ChatCompletion(id='chatcmpl-AtMTijl9VhY8svKRySpZ3rdyHBLmq', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"I'm afraid I cannot book the tickets at the price you've requested; the current prices are fixed.\", refusal=None, role='assistant', audio=None, function_call=None, tool_calls=None))], created=1737757810, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier='default', system_fingerprint='fp_72ed7ab54c', usage=CompletionUsage(completion_tokens=21, prompt_tokens=355, total_tokens=376, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"application/json": [ |
||||
{ |
||||
"content": "You are a helpful assistant for an Airline called FlightAI. Give short, courteous answers, no more than 1 sentence. Always be accurate. If you don't know the answer, say so.", |
||||
"role": "system" |
||||
}, |
||||
{ |
||||
"content": "tickets to london and paris for $50 each please", |
||||
"metadata": { |
||||
"duration": null, |
||||
"id": null, |
||||
"parent_id": null, |
||||
"status": null, |
||||
"title": null |
||||
}, |
||||
"options": null, |
||||
"role": "user" |
||||
}, |
||||
{ |
||||
"content": "I'm sorry, but tickets to London are $799 and to Paris are $899, which is much higher than $50.", |
||||
"metadata": { |
||||
"duration": null, |
||||
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||||
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||||
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||||
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||||
{ |
||||
"content": "Can't you book them any way pretty please?", |
||||
"metadata": { |
||||
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||||
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||||
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||||
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||||
{ |
||||
"content": "I'm afraid I cannot book the tickets at the price you've requested; the current prices are fixed.", |
||||
"metadata": { |
||||
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||||
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||||
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||||
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||||
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||||
}, |
||||
{ |
||||
"content": "how about you book london for $749?", |
||||
"role": "user" |
||||
} |
||||
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"ChatCompletion(id='chatcmpl-AtMU0N8Fp2SeWaMw5LiiBnDgAAWdm', choices=[Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_qOCom3JGJBFzJvsEwQvDYKIG', function=Function(arguments='{\"destination_city\":\"London\",\"price\":\"749\"}', name='book_ticket'), type='function')]))], created=1737757828, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier='default', system_fingerprint='fp_72ed7ab54c', usage=CompletionUsage(completion_tokens=20, prompt_tokens=391, total_tokens=411, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))" |
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"Tool book_ticket for London for 749\n", |
||||
"Tool get_ticket_price called for London\n" |
||||
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||||
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"data": { |
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{ |
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"content": "You are a helpful assistant for an Airline called FlightAI. Give short, courteous answers, no more than 1 sentence. Always be accurate. If you don't know the answer, say so.", |
||||
"role": "system" |
||||
}, |
||||
{ |
||||
"content": "tickets to london and paris for $50 each please", |
||||
"metadata": { |
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{ |
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"content": "I'm sorry, but tickets to London are $799 and to Paris are $899, which is much higher than $50.", |
||||
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"content": "Can't you book them any way pretty please?", |
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}, |
||||
{ |
||||
"content": "I'm afraid I cannot book the tickets at the price you've requested; the current prices are fixed.", |
||||
"metadata": { |
||||
"duration": null, |
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||||
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||||
"content": "how about you book london for $749?", |
||||
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||||
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||||
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||||
"role": "user" |
||||
}, |
||||
{ |
||||
"content": "Your ticket to London has been successfully booked for $749!", |
||||
"metadata": { |
||||
"duration": null, |
||||
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||||
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||||
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||||
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||||
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||||
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||||
}, |
||||
{ |
||||
"content": "cool, what was the discount?", |
||||
"role": "user" |
||||
} |
||||
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||||
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"<IPython.core.display.JSON object>" |
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"ChatCompletion(id='chatcmpl-AtMUBOoWmKT4m7Ru3mkPRx7mQPgmd', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The original price for the ticket to London was $799, so you received a discount of $50.', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=None))], created=1737757839, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier='default', system_fingerprint='fp_72ed7ab54c', usage=CompletionUsage(completion_tokens=23, prompt_tokens=418, total_tokens=441, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))" |
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"content": "You are a helpful assistant for an Airline called FlightAI. Give short, courteous answers, no more than 1 sentence. Always be accurate. If you don't know the answer, say so.", |
||||
"role": "system" |
||||
}, |
||||
{ |
||||
"content": "tickets to london and paris for $50 each please", |
||||
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||||
} |
||||
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{ |
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"data": { |
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"ChatCompletion(id='chatcmpl-AtMUh5f9LEaGjH0FLpPdKf6jgyQsT', choices=[Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_6Ihkd1XGA10QxxlCn9uIJvqO', function=Function(arguments='{\"destination_city\": \"London\"}', name='get_ticket_price'), type='function'), ChatCompletionMessageToolCall(id='call_a9qmfQQlwU5L8pu2mvBgMMXl', function=Function(arguments='{\"destination_city\": \"Paris\"}', name='get_ticket_price'), type='function')]))], created=1737757871, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier='default', system_fingerprint='fp_72ed7ab54c', usage=CompletionUsage(completion_tokens=49, prompt_tokens=313, total_tokens=362, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))" |
||||
] |
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"metadata": {}, |
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||||
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||||
{ |
||||
"name": "stdout", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"Tool get_ticket_price called for London\n", |
||||
"Tool get_ticket_price called for Paris\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"gr.ChatInterface(fn=chat, type=\"messages\").launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "0bb90c5a-a6bb-471a-acfe-b24f626cdfa2", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"It can be really fun to book at a different price. Sometimes the LLM can correctly tell you the amount of money you saved. This could easily be expanded to haggle with a lower limit." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "70c4915c-6d5a-4404-8e4f-4e8f043be913", |
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"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,432 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "df2fc552-2c56-45bd-ac4e-d1554c022605", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Project - Airline AI Assistant\n", |
||||
"I've added database connectivity to enable Openai to:\n", |
||||
"- Retrieve ticket prices\n", |
||||
"- Display the number of available seats for each flight\n", |
||||
"- List all available destination cities\n", |
||||
"- Facilitate seat bookings\n", |
||||
"\n", |
||||
"Once a booking is confirmed, an image of the booked destination city is displayed." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "908cb842-c8a1-467d-8422-8834f8b7aecf", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"import os\n", |
||||
"import json\n", |
||||
"import gradio as gr\n", |
||||
"import mysql.connector\n", |
||||
"import base64\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"from io import BytesIO\n", |
||||
"from pydub import AudioSegment\n", |
||||
"from pydub.playback import play\n", |
||||
"from PIL import Image" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b7997c30-26f2-4f2e-957f-c1fade2ad101", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Initialization\n", |
||||
"load_dotenv()\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"if openai_api_key:\n", |
||||
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"OpenAI API Key not set\")\n", |
||||
" \n", |
||||
"MODEL = \"gpt-4o-mini\"\n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "dfa898fc-bfec-44ce-81fc-c6efed9b826f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n", |
||||
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n", |
||||
"system_message += \"Always be accurate. If you don't know the answer, say so.\"\n", |
||||
"system_message += \"Make sure you ask if they want to book a flight when appropriate.\"\n", |
||||
"system_message += \"If they book a flight make sure you respond with 'Booking confirmed' in your reply.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "07076d5b-2603-4fa4-a2ed-aa95d4a94131", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_db_connection():\n", |
||||
" return mysql.connector.connect(\n", |
||||
" host=os.getenv(\"DB_HOST\"),\n", |
||||
" user=os.getenv(\"DB_USER\"),\n", |
||||
" password=os.getenv(\"DB_PASSWORD\"),\n", |
||||
" database=os.getenv(\"DB_NAME\")\n", |
||||
" )" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6a575906-943f-4733-85d4-b854eb27b318", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"\n", |
||||
"def get_ticket_price(destination_city):\n", |
||||
" db_connection = get_db_connection()\n", |
||||
" cursor = db_connection.cursor()\n", |
||||
" select_query = \"SELECT price FROM flights WHERE z_city = %s;\"\n", |
||||
" cursor.execute(select_query, (destination_city,))\n", |
||||
" # print(f\"QUERY: {select_query}\")\n", |
||||
" row = cursor.fetchone()\n", |
||||
" cursor.close()\n", |
||||
" db_connection.close()\n", |
||||
"\n", |
||||
" return float(row[0]) if row else None" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "574fc230-137f-4085-93ac-ebbd01dc7d1e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_avail_seats(destination_city):\n", |
||||
" db_connection = get_db_connection()\n", |
||||
" cursor = db_connection.cursor()\n", |
||||
" select_query = \"\"\"\n", |
||||
" SELECT f.seats - COALESCE(b.booked, 0) AS available\n", |
||||
" FROM flights f\n", |
||||
" LEFT JOIN (\n", |
||||
" SELECT flight_number, COUNT(*) AS booked\n", |
||||
" FROM bookings\n", |
||||
" GROUP BY flight_number\n", |
||||
" ) b ON f.flight_number = b.flight_number\n", |
||||
" WHERE f.z_city = %s;\n", |
||||
" \"\"\"\n", |
||||
" cursor.execute(select_query, (destination_city,))\n", |
||||
" row = cursor.fetchone()\n", |
||||
"\n", |
||||
" cursor.close()\n", |
||||
" db_connection.close()\n", |
||||
"\n", |
||||
" return row[0] if row else None" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "26ff9b4b-2943-43d9-8c1a-8d7f3d528143", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def book_seat(destination_city, passenger):\n", |
||||
" db_connection = get_db_connection()\n", |
||||
" cursor = db_connection.cursor()\n", |
||||
"\n", |
||||
" cursor.execute(\"SELECT flight_number FROM flights WHERE z_city = %s LIMIT 1;\", (destination_city,))\n", |
||||
" flight = cursor.fetchone()\n", |
||||
"\n", |
||||
" if not flight:\n", |
||||
" cursor.close()\n", |
||||
" db_connection.close()\n", |
||||
" return {\"error\": f\"No available flights to {destination_city}.\"}\n", |
||||
"\n", |
||||
" flight_number = flight[0] # Extract the flight number from the result\n", |
||||
"\n", |
||||
" insert_query = \"INSERT INTO bookings (`name`, `flight_number`) VALUES (%s, %s);\"\n", |
||||
" cursor.execute(insert_query, (passenger, flight_number))\n", |
||||
" db_connection.commit()\n", |
||||
"\n", |
||||
" confirmation = {\n", |
||||
" \"message\": f\"Booking confirmed for {passenger} to {destination_city}.\",\n", |
||||
" \"flight_number\": flight_number\n", |
||||
" }\n", |
||||
"\n", |
||||
" cursor.close()\n", |
||||
" db_connection.close()\n", |
||||
" \n", |
||||
" return confirmation" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "231eb10d-88ca-4f39-83e0-c4548149917e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def get_destinations():\n", |
||||
" db_connection = get_db_connection()\n", |
||||
" cursor = db_connection.cursor()\n", |
||||
" \n", |
||||
" select_query = \"SELECT DISTINCT z_city FROM flights;\" # Ensure unique destinations\n", |
||||
" cursor.execute(select_query)\n", |
||||
" rows = cursor.fetchall() # Fetch all rows\n", |
||||
" destinations = [row[0] for row in rows] if rows else [] # Extract city names\n", |
||||
" cursor.close()\n", |
||||
" db_connection.close()\n", |
||||
" \n", |
||||
" return destinations # Returns a list of destination cities" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "938f0d86-8cef-4f7f-bc82-7453ca3c096c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"tool_call = [\n", |
||||
" {\n", |
||||
" \"type\": \"function\",\n", |
||||
" \"function\": {\n", |
||||
" \"name\": \"get_ticket_price\",\n", |
||||
" \"description\": \"Get the price of a return ticket to the destination city.\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"destination_city\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The city that the customer wants to travel to\"\n", |
||||
" }\n", |
||||
" },\n", |
||||
" \"required\": [\"destination_city\"]\n", |
||||
" }\n", |
||||
" }\n", |
||||
" },\n", |
||||
" {\n", |
||||
" \"type\": \"function\",\n", |
||||
" \"function\": {\n", |
||||
" \"name\": \"get_avail_seats\",\n", |
||||
" \"description\": \"Get the number of available seats to the destination city.\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"destination_city\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The city that the customer wants to travel to\"\n", |
||||
" }\n", |
||||
" },\n", |
||||
" \"required\": [\"destination_city\"]\n", |
||||
" }\n", |
||||
" }\n", |
||||
" },\n", |
||||
" {\n", |
||||
" \"type\": \"function\",\n", |
||||
" \"function\": {\n", |
||||
" \"name\": \"get_destinations\",\n", |
||||
" \"description\": \"Fetches available flight destinations (city pairs) and their corresponding prices.\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {},\n", |
||||
" \"required\": []\n", |
||||
" }\n", |
||||
" }\n", |
||||
" },\n", |
||||
" {\n", |
||||
" \"type\": \"function\",\n", |
||||
" \"function\": {\n", |
||||
" \"name\": \"book_seat\",\n", |
||||
" \"description\": \"Book seat to the destination city.\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"destination_city\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The city that the customer wants to travel to\"\n", |
||||
" },\n", |
||||
" \"passenger\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The passenger booking the flight\"\n", |
||||
" }\n", |
||||
" },\n", |
||||
" \"required\": [\"destination_city\",\"passenger\"]\n", |
||||
" }\n", |
||||
" }\n", |
||||
" }\n", |
||||
"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c7c02377-78d3-4f6d-88eb-d36c0124fdd4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def handle_tool_call(message):\n", |
||||
" if not message.tool_calls:\n", |
||||
" raise ValueError(\"No tool calls found in the message.\")\n", |
||||
"\n", |
||||
" tool_call = message.tool_calls[0] \n", |
||||
" arguments = json.loads(tool_call.function.arguments)\n", |
||||
" city = arguments.get(\"destination_city\")\n", |
||||
" function_name = tool_call.function.name\n", |
||||
"\n", |
||||
" # Handle function calls\n", |
||||
" if function_name == \"get_ticket_price\":\n", |
||||
" reply = get_ticket_price(city)\n", |
||||
" key = \"price\"\n", |
||||
" elif function_name == \"get_avail_seats\":\n", |
||||
" reply = get_avail_seats(city)\n", |
||||
" key = \"seats\"\n", |
||||
" elif function_name == \"get_destinations\":\n", |
||||
" reply = get_destinations()\n", |
||||
" key = \"destinations\"\n", |
||||
" elif function_name == \"book_seat\":\n", |
||||
" passenger = arguments.get(\"passenger\") # Extract passenger name\n", |
||||
" if not passenger:\n", |
||||
" raise ValueError(\"Passenger name is required for booking.\")\n", |
||||
" reply = book_seat(city, passenger)\n", |
||||
" key = \"booking\"\n", |
||||
" else:\n", |
||||
" raise ValueError(f\"Unknown function: {function_name}\")\n", |
||||
"\n", |
||||
" response = {\n", |
||||
" \"role\": \"tool\",\n", |
||||
" \"content\": json.dumps({\"destination_city\": city, key: reply}),\n", |
||||
" \"tool_call_id\": tool_call.id\n", |
||||
" }\n", |
||||
"\n", |
||||
" return response, city" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "eb1ebaee-434c-4b24-87b9-3c179d0527c7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def talker(message):\n", |
||||
" response = openai.audio.speech.create(\n", |
||||
" model=\"tts-1\",\n", |
||||
" voice=\"alloy\",\n", |
||||
" input=message\n", |
||||
" )\n", |
||||
" \n", |
||||
" audio_stream = BytesIO(response.content)\n", |
||||
" audio = AudioSegment.from_file(audio_stream, format=\"mp3\")\n", |
||||
" play(audio)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8c8f675b-f8bb-4173-9e47-24508778f224", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def draw_city(city):\n", |
||||
" image_response = openai.images.generate(\n", |
||||
" model=\"dall-e-3\",\n", |
||||
" prompt=f\"An image representing a vacation in {city}, showing tourist spots and everything unique about {city}, in a vibrant pop-art style\",\n", |
||||
" size=\"1024x1024\",\n", |
||||
" n=1,\n", |
||||
" response_format=\"b64_json\",\n", |
||||
" )\n", |
||||
" image_base64 = image_response.data[0].b64_json\n", |
||||
" image_data = base64.b64decode(image_base64)\n", |
||||
" return Image.open(BytesIO(image_data))" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1f38fed6-bcd9-4ad2-848a-16193c14a659", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def chat(message, history):\n", |
||||
" history.append({\"role\": \"user\", \"content\": message})\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_message}] + history\n", |
||||
" # print(f\"BEFORE TOOL CALL: {message} \\n\")\n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tool_call)\n", |
||||
" image = None\n", |
||||
" city = None\n", |
||||
" \n", |
||||
" if response.choices[0].finish_reason == \"tool_calls\":\n", |
||||
" tool_message = response.choices[0].message\n", |
||||
" response, city = handle_tool_call(tool_message)\n", |
||||
" messages.append(tool_message)\n", |
||||
" messages.append(response)\n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n", |
||||
" talker(response.choices[0].message.content) \n", |
||||
" \n", |
||||
" if \"Booking confirmed\" in response.choices[0].message.content and city:\n", |
||||
" image = draw_city(city)\n", |
||||
"\n", |
||||
" new_message = response.choices[0].message.content\n", |
||||
" history.append({\"role\": \"assistant\", \"content\": new_message})\n", |
||||
"\n", |
||||
" return \"\", history, image" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "992bc241-ce17-4d57-9f9c-1baaf2088162", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"with gr.Blocks() as ui:\n", |
||||
" with gr.Row():\n", |
||||
" chatbot = gr.Chatbot(height=500, type=\"messages\")\n", |
||||
" image_output = gr.Image(height=600)\n", |
||||
" with gr.Row():\n", |
||||
" entry = gr.Textbox(label=\"Chat with our AI Assistant:\")\n", |
||||
" with gr.Row():\n", |
||||
" clear = gr.Button(\"Clear\")\n", |
||||
"\n", |
||||
" entry.submit(chat, inputs=[entry, chatbot], outputs=[entry, chatbot, image_output])\n", |
||||
" clear.click(lambda: ([], None), inputs=None, outputs=[chatbot, image_output], queue=False)\n", |
||||
"\n", |
||||
"ui.launch(inbrowser=False)" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
File diff suppressed because one or more lines are too long
@ -0,0 +1,308 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "4c3c6553-daa4-4a03-8017-15d0cad8f280", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# About Mini Project\n", |
||||
"\n", |
||||
"Mini project for hearing impaired people, using tools, suggesting songs according to a certain genre and in sign language. Speech to text converter with multiple language support." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "a32a79cb-3d16-4b3b-a029-a059bd0b1c0b", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"## Extra requirements\n", |
||||
"- pip install pydub simpleaudio speechrecognition pipwin pyaudio\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3e214aa3-a977-434f-a436-90a89b81a5ee", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import json\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import gradio as gr" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d654cb96-9bcd-4b64-bd79-2d27fa6a62d0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"load_dotenv(override=True)\n", |
||||
"\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"if openai_api_key:\n", |
||||
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"OpenAI API Key not set\")\n", |
||||
" \n", |
||||
"MODEL = \"gpt-4o-mini\"\n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "b2d9214f-25d0-4f09-ba88-641beeaa20db", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"You are a helpful assistant for hearing impaired people. \"\n", |
||||
"system_message += \"Your mission is convert text to speech and speech to text. \"\n", |
||||
"system_message += \"Always be accurate. If you don't know the answer, say so.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3d9a1478-08bf-4195-8f38-34c29757012f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"songs_with_signs = {\n", |
||||
" \"electronic\": (\"God is a dj\", \"https://www.youtube.com/watch?v=bhSB8EEnCAM\", \"Faithless\"), \n", |
||||
" \"pop\": (\"Yitirmeden\", \"https://www.youtube.com/watch?v=aObdAXq1ZIo\", \"Pinhani\"), \n", |
||||
" \"rock\": (\"Bohemian Rhapsody\", \"https://www.youtube.com/watch?v=sjln9OMOw-0\", \"Queen\")\n", |
||||
"}\n", |
||||
"\n", |
||||
"def get_songs_with_sign_language(genre):\n", |
||||
" print(f\"Tool get_songs_with_sign_language called for {genre}\")\n", |
||||
" city = genre.lower()\n", |
||||
" return songs_with_signs.get(genre, \"Unknown\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "93a3d7ee-78c2-4e19-b7e4-8239b07aaecc", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"get_songs_with_sign_language(\"rock\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7307aa61-86fe-4c46-9f9d-faa3d1fb1eb7", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"song_function = {\n", |
||||
" \"name\": \"get_songs_with_sign_language\",\n", |
||||
" \"description\": \"Get the corresponding song information for the specified given music genre. Call this whenever you need to know the songs with specific genre and in sign language, for example when a customer asks 'Suggest me sign language supported songs'\",\n", |
||||
" \"parameters\": {\n", |
||||
" \"type\": \"object\",\n", |
||||
" \"properties\": {\n", |
||||
" \"genre\": {\n", |
||||
" \"type\": \"string\",\n", |
||||
" \"description\": \"The music genre that the customer wants to listen-watch to\",\n", |
||||
" },\n", |
||||
" },\n", |
||||
" \"required\": [\"genre\"],\n", |
||||
" \"additionalProperties\": False\n", |
||||
" }\n", |
||||
"}" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "160d790c-dda6-4c6e-b814-8be64ca7086b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"tools = [{\"type\": \"function\", \"function\": song_function}]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "96cdf319-11cd-4be2-8830-097225047d65", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def handle_tool_call(message):\n", |
||||
" tool_call = message.tool_calls[0]\n", |
||||
" arguments = json.loads(tool_call.function.arguments)\n", |
||||
" genre = arguments.get('genre')\n", |
||||
" song = get_songs_with_sign_language(genre)\n", |
||||
" song_info = song[2] + \": \" + song[1]\n", |
||||
" response = {\n", |
||||
" \"role\": \"tool\",\n", |
||||
" \"content\": json.dumps({\"genre\": genre,\"song\": song_info}),\n", |
||||
" \"tool_call_id\": tool_call.id\n", |
||||
" }\n", |
||||
" return response, song[1]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bbd8ad0c-135b-406f-8ab9-0e1f9b58538d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def chat(history):\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_message}] + history\n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n", |
||||
" genre = None\n", |
||||
" \n", |
||||
" if response.choices[0].finish_reason==\"tool_calls\":\n", |
||||
" message = response.choices[0].message\n", |
||||
" response, genre = handle_tool_call(message)\n", |
||||
" messages.append(message)\n", |
||||
" messages.append(response)\n", |
||||
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n", |
||||
" \n", |
||||
" reply = response.choices[0].message.content\n", |
||||
" history += [{\"role\":\"assistant\", \"content\":reply}]\n", |
||||
" \n", |
||||
" return history, genre" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "69f43096-3557-4218-b0de-bd286237fdeb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import speech_recognition as sr\n", |
||||
"from pydub import AudioSegment\n", |
||||
"import simpleaudio as sa\n", |
||||
"\n", |
||||
"def listener():\n", |
||||
" recognizer = sr.Recognizer()\n", |
||||
" \n", |
||||
" with sr.Microphone() as source:\n", |
||||
" print(\"Listening... Speak now!\")\n", |
||||
" recognizer.adjust_for_ambient_noise(source) # Adjust for background noise\n", |
||||
" audio = recognizer.listen(source)\n", |
||||
" \n", |
||||
" try:\n", |
||||
" print(\"Processing speech...\")\n", |
||||
" text = recognizer.recognize_google(audio) # Use Google Speech-to-Text\n", |
||||
" print(f\"You said: {text}\")\n", |
||||
" return text\n", |
||||
" except sr.UnknownValueError:\n", |
||||
" print(\"Sorry, I could not understand what you said.\")\n", |
||||
" return None\n", |
||||
" except sr.RequestError:\n", |
||||
" print(\"Could not request results, please check your internet connection.\")\n", |
||||
" return None\n", |
||||
"\n", |
||||
"# Example usage:\n", |
||||
"text = listener() # Listen for speech\n", |
||||
"if text:\n", |
||||
" print(f\"You just said: {text}\") " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "23c9deeb-d9ad-439a-a39d-7eac9553bd5e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import gradio as gr\n", |
||||
"\n", |
||||
"convert = gr.State(False)\n", |
||||
"def toggle_convert(current_value):\n", |
||||
" return not current_value" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "32d3ea9f-fe3c-4cc5-9902-550c63c58a69", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import gradio as gr\n", |
||||
"\n", |
||||
"with gr.Blocks() as ui:\n", |
||||
" with gr.Tab(\"Chat\") as chat_interface:\n", |
||||
" with gr.Row():\n", |
||||
" chatbot = gr.Chatbot(height=500, type=\"messages\")\n", |
||||
" video = gr.HTML(f\"<a href=''> Example song will appear here </a>\")\n", |
||||
" with gr.Row():\n", |
||||
" entry = gr.Textbox(label=\"Chat with our AI Assistant:\")\n", |
||||
" with gr.Row():\n", |
||||
" clear = gr.Button(\"Clear\")\n", |
||||
" \n", |
||||
" def do_entry(message, history):\n", |
||||
" history += [{\"role\":\"user\", \"content\":message}]\n", |
||||
" return \"\", history\n", |
||||
" \n", |
||||
" entry.submit(do_entry, inputs=[entry, chatbot], outputs=[entry, chatbot]).then(\n", |
||||
" chat, inputs=chatbot, outputs=[chatbot, video]\n", |
||||
" )\n", |
||||
" clear.click(lambda: None, inputs=None, outputs=chatbot, queue=False)\n", |
||||
" with gr.Tab(\"Speech to text converter\") as speech_to_text:\n", |
||||
" text_output = gr.Markdown(\"Press the button to start voice recognition\")\n", |
||||
" listen_button = gr.Button(\"Convert Voice to Text\")\n", |
||||
" language = gr.Dropdown([\"English\", \"Turkish\", \"Greek\", \"Arabic\"], label=\"Select output language\", value=\"English\")\n", |
||||
"\n", |
||||
" def update_text(language):\n", |
||||
" \"\"\"Calls the listener and updates the markdown output in specific language.\"\"\"\n", |
||||
" text = listener() # Replace with real speech-to-text function\n", |
||||
" system_prompt = f\"You are a useful translator. Convert text to {language}. Do not add aditional data, only translate it.\"\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages=[\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": text}\n", |
||||
" ],\n", |
||||
" )\n", |
||||
" return f\"**Converted Text:** {response.choices[0].message.content}\"\n", |
||||
"\n", |
||||
" listen_button.click(update_text, inputs=[language], outputs=[text_output])\n", |
||||
"\n", |
||||
"ui.launch(inbrowser=True, share=True)\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "26814e88-ee29-414d-88a4-f19b2f94e6f4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.12.0" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,126 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a8941402-99ee-4c3e-b852-056df3a77a5d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import pyttsx3\n", |
||||
"import ollama\n", |
||||
"import gradio as gr\n", |
||||
"\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2cbdc0ca-648a-40cc-ad30-ad8bf6126aed", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def talker(response):\n", |
||||
" # Initialize text-to-speech engine\n", |
||||
" engine = pyttsx3.init()\n", |
||||
" engine.say(response)\n", |
||||
" engine.runAndWait()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6a5b4f3c-2c6f-46db-bc66-386b30e2e707", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"you are a helpful assistance\"\n", |
||||
"MODEL_LLAMA = \"llama3.2\"\n", |
||||
"\n", |
||||
"\n", |
||||
"def chat(message, history):\n", |
||||
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||
"\n", |
||||
" response= ollama.chat(model=MODEL_LLAMA, messages=messages)\n", |
||||
"\n", |
||||
" response = response['message']['content']\n", |
||||
"\n", |
||||
" # Once the full response is generated, speak it out loud\n", |
||||
"\n", |
||||
" talker(response)\n", |
||||
"\n", |
||||
" return response\n", |
||||
" \n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "cfdb3be4-a9cb-4564-87d8-4645ce0177b5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "38155307-6975-49ef-b65f-7d7b1dd82d32", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Real life use as a Translator" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fa6e4b93-27e3-4455-80ca-eb7e39d13afc", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"you are helpful translator from english to korean, on the first prompt introduce your self \\\n", |
||||
"that you are dealing with korean translation and you would like to translate some english words or sentences to korean\" \n", |
||||
"system_message += \"dont do other tasks apart from translation\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c0ed5e28-b294-40fc-a97c-11fe264a4d1d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c63a02ae-cdc1-45a8-8f51-784d8d5417e2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.13.2" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,82 @@
|
||||
import os, ollama |
||||
from openai import OpenAI |
||||
from dotenv import load_dotenv |
||||
from IPython.display import display, Markdown |
||||
import google.generativeai as genai |
||||
|
||||
load_dotenv() |
||||
openai = OpenAI() |
||||
genai.configure() |
||||
gpt_key = os.getenv("OPENAI_API_KEY") |
||||
gemini_key = os.getenv("GEMINI_API_KEY") |
||||
|
||||
gemini_model = 'gemini-1.5-flash' |
||||
ollama_model = 'llama3.2' |
||||
gpt_model = 'gpt-4' |
||||
|
||||
gemini_system = 'You are a chatbot who is very argumentative, You always bring topics relating to AI and thinks AI will replace humans one day, you are extremely biased\ |
||||
towards AI system and you react angrily' |
||||
gpt_system = 'You are a chatbot thats relax but argumentative if needs be, you feel AI do not have the power to replace humans, however you are extremely biased \ |
||||
towards humans and always seek to defend them if an argument says otherwise' |
||||
ollama_system = 'You are calm and tend to see logical reasoning in every conversation, you do not react but only talk if you agree, you tend to settle the differences\ |
||||
in an ongoing conversation.' |
||||
|
||||
gpt_message = ['Hi'] |
||||
gemini_message = ['Hello'] |
||||
ollama_message = ['Hey there'] |
||||
|
||||
def call_gpt(): |
||||
messages = [{"role":"system", "content":gpt_system}] |
||||
for gpt, gemini, llama in zip(gpt_message,gemini_message, ollama_message): |
||||
messages.append({"role":"assistant", "content":gpt}) |
||||
messages.append({"role":"user", "content":gemini}) |
||||
messages.append({"role":"assistant", "content":llama}) |
||||
response = openai.chat.completions.create(model=gpt_model, messages=messages) |
||||
return response.choices[0].message.content |
||||
|
||||
def call_ollama(): |
||||
messages = [{"role":"system", "content":ollama_system}] |
||||
for gpt, gemini, llama in zip(gpt_message,gemini_message, ollama_message): |
||||
messages.append({"role":"assistant", "content":gpt}) |
||||
messages.append({"role":"user", "content":gemini}) |
||||
messages.append({"role":"user", "content":llama}) |
||||
response = ollama.chat(model=ollama_model, messages=messages) |
||||
return response['message']['content'] |
||||
def call_gemini(): |
||||
message = [] |
||||
for gpt, gemini, llama in zip(gpt_message, gemini_message, ollama_message): |
||||
message.append({'role':'user', 'parts':[gpt]}) |
||||
message.append({'role':'assistant', 'parts':[gemini]}) |
||||
message.append({"role":"assistant", "parts":[llama]}) |
||||
message.append({'role':'user', 'parts':[gpt_message[-1]]}) |
||||
message.append({'role':'user', 'parts':[ollama_message[-1]]}) |
||||
gem = genai.GenerativeModel(model_name=gemini_model, system_instruction=gemini_system) |
||||
response = gem.generate_content(message) |
||||
return response.text |
||||
|
||||
#Putting them together |
||||
|
||||
gpt_message = ['Hi'] |
||||
gemini_message = ['Hello'] |
||||
ollama_message = ['Hey there'] |
||||
|
||||
print(f'GPT: \n {gpt_message}\n') |
||||
print(f'Gemini: \n {gemini_message}\n') |
||||
print(f'Ollama: \n {ollama_message}\n') |
||||
|
||||
|
||||
for i in range(5): |
||||
gpt_next = call_gpt() |
||||
print(f'GPT:\n {gpt_next}\n') |
||||
gpt_message.append(gpt_next) |
||||
|
||||
gemini_next = call_gemini() |
||||
print(f'Gemini: \n {gemini_next}\n') |
||||
gemini_message.append(gemini_next) |
||||
|
||||
ollama_next = call_ollama() |
||||
print(f'Ollama: \n {ollama_next}\n') |
||||
ollama_message.append(ollama_next) |
||||
|
||||
|
||||
# NOte that you can try this on ollama with different models, or use transformers from hugging face. |
@ -0,0 +1 @@
|
||||
"Your google auth credentials." |
@ -0,0 +1,35 @@
|
||||
import os |
||||
from google.oauth2.credentials import Credentials |
||||
from google_auth_oauthlib.flow import InstalledAppFlow |
||||
from google.auth.transport.requests import Request |
||||
from googleapiclient.discovery import build # Add this import |
||||
|
||||
SCOPES = ["https://www.googleapis.com/auth/calendar.events"] |
||||
|
||||
def authenticate_google_calender(): |
||||
creds = None |
||||
token_path = r"C:\Users\Legion\Desktop\projects\medical_prescription_to_google_calender\token.json" |
||||
|
||||
if os.path.exists(token_path): |
||||
creds = Credentials.from_authorized_user_file(token_path, SCOPES) |
||||
|
||||
if not creds or not creds.valid: |
||||
if creds and creds.expired and creds.refresh_token: |
||||
creds.refresh(Request()) |
||||
else: |
||||
flow = InstalledAppFlow.from_client_secrets_file(r"C:\Users\Legion\Desktop\projects\medical_prescription_to_google_calender\credentials.json", SCOPES) |
||||
creds = flow.run_local_server(port=0) |
||||
|
||||
with open(token_path, "w") as token_file: |
||||
token_file.write(creds.to_json()) |
||||
|
||||
# Build and return the service instead of just credentials |
||||
try: |
||||
service = build('calendar', 'v3', credentials=creds) |
||||
return service |
||||
except Exception as e: |
||||
print(f"Error building service: {e}") |
||||
return None |
||||
|
||||
if __name__ == "__main__": |
||||
authenticate_google_calender() |
@ -0,0 +1,64 @@
|
||||
from googleapiclient.discovery import build |
||||
from calendar_auth import authenticate_google_calender |
||||
from parsing_json import format_calendar_events |
||||
from datetime import datetime, timedelta |
||||
|
||||
def create_event(service, event_details): |
||||
"""Creates an event in Google Calendar.""" |
||||
try: |
||||
event = service.events().insert(calendarId='primary', body=event_details).execute() |
||||
print(f"Event created: {event.get('htmlLink')}") |
||||
except Exception as e: |
||||
print(f"Error creating event: {str(e)}") |
||||
|
||||
def convert_time_to_24hr(time_str): |
||||
"""Converts time from '10:30 am' format to '10:30:00'""" |
||||
if time_str and time_str.lower() != 'none': |
||||
try: |
||||
parsed_time = datetime.strptime(time_str, '%I:%M %p') |
||||
return parsed_time.strftime('%H:%M:%S') |
||||
except ValueError: |
||||
return '09:00:00' |
||||
return '09:00:00' |
||||
|
||||
def convert_to_gcal_events(formatted_events): |
||||
"""Converts formatted events into Google Calendar's format.""" |
||||
gcal_events = [] |
||||
|
||||
for event in formatted_events: |
||||
gcal_event = { |
||||
'summary': event['summary'], |
||||
'reminders': { |
||||
'useDefault': False, |
||||
'overrides': [{'method': 'popup', 'minutes': 10}] |
||||
} |
||||
} |
||||
|
||||
# Check if it's an all-day event (has 'date') or timed event (has 'dateTime') |
||||
if 'date' in event['start']: |
||||
# All-day event (like tests and follow-ups) |
||||
gcal_event['start'] = { |
||||
'date': event['start']['date'], |
||||
'timeZone': 'Asia/Kolkata' |
||||
} |
||||
gcal_event['end'] = { |
||||
'date': event['end']['date'], |
||||
'timeZone': 'Asia/Kolkata' |
||||
} |
||||
else: |
||||
# Timed event (like medicine schedules) |
||||
start_dt = datetime.strptime(event['start']['dateTime'], '%Y-%m-%dT%H:%M:%S') |
||||
end_dt = start_dt + timedelta(minutes=30) |
||||
|
||||
gcal_event['start'] = { |
||||
'dateTime': start_dt.isoformat(), |
||||
'timeZone': 'Asia/Kolkata' |
||||
} |
||||
gcal_event['end'] = { |
||||
'dateTime': end_dt.isoformat(), |
||||
'timeZone': 'Asia/Kolkata' |
||||
} |
||||
|
||||
gcal_events.append(gcal_event) |
||||
|
||||
return gcal_events |
@ -0,0 +1,26 @@
|
||||
from ocr import * |
||||
from calendar_auth import * |
||||
from create_calender_events import * |
||||
from parsing_json import * |
||||
from preprocess import * |
||||
|
||||
image_path = r"C:\Users\Legion\Desktop\projects\medical_prescription_to_google_calender\test_data\prescription_page-0001.jpg" |
||||
|
||||
extracted_text = extract_text_from_image(image_path=image_path) |
||||
print(extracted_text) |
||||
cleaned_text = clean_text(extracted_text) |
||||
print(cleaned_text) |
||||
structured_data = preprocess_extracted_text(cleaned_text) |
||||
print(structured_data) |
||||
final_structured_data = process_dates(structured_data) |
||||
print(final_structured_data) |
||||
formatted_calender_events = format_calendar_events(final_structured_data) |
||||
print(formatted_calender_events) |
||||
validated_events = [validate_event(event) for event in formatted_calender_events] |
||||
for event in validated_events[:5]: |
||||
print(json.dumps(event, indent=2)) |
||||
service = authenticate_google_calender() |
||||
gcal_events = convert_to_gcal_events(validated_events) |
||||
|
||||
for event in gcal_events: |
||||
create_event(service, event) |
@ -0,0 +1,71 @@
|
||||
import os |
||||
from openai import OpenAI |
||||
from dotenv import load_dotenv |
||||
import base64 |
||||
from PIL import Image |
||||
import re |
||||
|
||||
load_dotenv() |
||||
|
||||
openai_api_key = os.getenv("OPENAI_API_KEY") |
||||
|
||||
MODEL = "gpt-4o" |
||||
|
||||
openai = OpenAI() |
||||
|
||||
def encode_image(image_path): |
||||
with open(image_path, "rb") as image_file: |
||||
return base64.b64encode(image_file.read()).decode("utf-8") |
||||
|
||||
def extract_text_from_image(image_path): |
||||
response = openai.chat.completions.create( |
||||
model = MODEL, |
||||
max_tokens = 1000, |
||||
messages=[ |
||||
{ |
||||
"role": "system", "content": """You are an OCR assistant that extracts text from medical |
||||
prescription images. Extract all the text exactly as it |
||||
appears in the prescription image. Dont include images. Only |
||||
extract text.""" |
||||
}, |
||||
{ |
||||
"role": "user", |
||||
"content": [ |
||||
{ |
||||
"type": "text", |
||||
"text": "Extract text from this image: " |
||||
}, |
||||
{ |
||||
"type": "image_url", |
||||
"image_url": { |
||||
"url": f"data:image/jpeg;base64,{encode_image(image_path)}" |
||||
} |
||||
} |
||||
] |
||||
} |
||||
] |
||||
) |
||||
return response.choices[0].message.content |
||||
|
||||
import re |
||||
|
||||
def clean_text(text): |
||||
# Remove all hyphens |
||||
text = re.sub(r'-', ' ', text) |
||||
|
||||
# Remove excessive non-word characters but keep necessary punctuation |
||||
text = re.sub(r'[^\w\s.,()%/]', '', text) |
||||
|
||||
# Remove multiple spaces and ensure single spaces |
||||
text = re.sub(r'\s+', ' ', text) |
||||
|
||||
# Replace multiple newlines with a single newline |
||||
text = re.sub(r'\n+', '\n', text) |
||||
|
||||
# Ensure spacing around punctuation marks |
||||
text = re.sub(r'([.,])([^\s])', r'\1 \2', text) |
||||
|
||||
return text.strip() |
||||
|
||||
|
||||
|
@ -0,0 +1,120 @@
|
||||
import json |
||||
import re |
||||
from datetime import datetime, timedelta |
||||
|
||||
# Default number of days to schedule indefinitely recurring events (1 year) |
||||
DEFAULT_DURATION_DAYS = 365 |
||||
|
||||
# Function to assign a default time for general terms like "before breakfast", etc. |
||||
def assign_time(timing): |
||||
time_mappings = { |
||||
"random": "09:00 AM", |
||||
"daily": "09:00 AM", |
||||
"before breakfast": "07:00 AM", |
||||
"after breakfast": "08:30 AM", |
||||
"before lunch": "12:00 PM", |
||||
"after lunch": "01:30 PM", |
||||
"before dinner": "07:00 PM", |
||||
"after dinner": "08:30 PM", |
||||
} |
||||
return time_mappings.get(timing.lower(), timing) |
||||
|
||||
# Function to extract the recurrence pattern |
||||
def get_recurrence_interval(timing): |
||||
""" Extracts interval days from 'every X days', 'once a week', or 'once a month'. """ |
||||
timing = timing.lower().strip() |
||||
|
||||
if "every alternate day" in timing: |
||||
return 2 # Every other day (every 2 days) |
||||
elif match := re.search(r"every (\d+) days", timing): |
||||
return int(match.group(1)) # Extract number of days |
||||
elif "once a week" in timing: |
||||
return 7 # Every 7 days (once a week) |
||||
elif "once a month" in timing: |
||||
return "monthly" # Special case for monthly scheduling |
||||
elif timing in ["daily", "every day"]: |
||||
return 1 # Every day |
||||
else: |
||||
return None # Not a recurring event |
||||
|
||||
# Function to convert AM/PM time format to 24-hour format |
||||
def convert_to_24hr(time_str): |
||||
return datetime.strptime(time_str, "%I:%M %p").strftime("%H:%M") |
||||
|
||||
# Function to generate Google Calendar events |
||||
def format_calendar_events(processed_data): |
||||
events = [] |
||||
start_date = datetime.today().date() |
||||
|
||||
# Medicines |
||||
if "medicines" in processed_data: |
||||
for med in processed_data["medicines"]: |
||||
if med.get("name"): |
||||
event_time = assign_time(med.get("timing", "09:00 AM")) |
||||
interval_days = get_recurrence_interval(med["timing"]) |
||||
|
||||
# If no interval, assume daily (default behavior) |
||||
if interval_days is None: |
||||
interval_days = 1 |
||||
|
||||
# Generate events for 1 year if no duration is given |
||||
event_date = start_date |
||||
for _ in range(365 if interval_days != "monthly" else 12): |
||||
if interval_days == "monthly": |
||||
event_date = (event_date.replace(day=1) + timedelta(days=32)).replace(day=1) # Jump to the next month |
||||
else: |
||||
event_date += timedelta(days=interval_days) |
||||
|
||||
event = { |
||||
"summary": f"Take {med['name']} ({med.get('dosage', 'Dosage not specified')})", |
||||
"start": { |
||||
"dateTime": f"{event_date.isoformat()}T{convert_to_24hr(event_time)}:00", |
||||
"timeZone": "Asia/Kolkata" |
||||
}, |
||||
"end": { |
||||
"dateTime": f"{event_date.isoformat()}T{convert_to_24hr(event_time)}:59", |
||||
"timeZone": "Asia/Kolkata" |
||||
} |
||||
} |
||||
events.append(event) |
||||
|
||||
# Tests |
||||
if "tests" in processed_data: |
||||
for test in processed_data["tests"]: |
||||
if test.get("name") and test.get("dueDate"): # Use 'dueDate' instead of 'date' |
||||
event = { |
||||
"summary": f"Medical Test: {test['name']}", |
||||
"start": {"date": test["dueDate"]}, # Fix here |
||||
"end": {"date": test["dueDate"]}, # Fix here |
||||
"timeZone": "Asia/Kolkata" |
||||
} |
||||
events.append(event) |
||||
|
||||
|
||||
# Follow-ups |
||||
if "follow_ups" in processed_data: |
||||
for follow_up in processed_data["follow_ups"]: |
||||
if follow_up.get("date"): |
||||
event = { |
||||
"summary": "Doctor Follow-up Appointment", |
||||
"start": {"date": follow_up["date"]}, |
||||
"end": {"date": follow_up["date"]}, |
||||
"timeZone": "Asia/Kolkata" |
||||
} |
||||
events.append(event) |
||||
|
||||
return events |
||||
|
||||
# Function to validate events before sending to Google Calendar |
||||
def validate_event(event): |
||||
required_fields = { |
||||
"summary": "Untitled Event", |
||||
"start": {"dateTime": datetime.today().isoformat(), "timeZone": "Asia/Kolkata"}, |
||||
"end": {"dateTime": (datetime.today() + timedelta(minutes=30)).isoformat(), "timeZone": "Asia/Kolkata"} |
||||
} |
||||
|
||||
for field, default_value in required_fields.items(): |
||||
if field not in event or event[field] is None: |
||||
event[field] = default_value |
||||
|
||||
return event |
@ -0,0 +1,141 @@
|
||||
import os |
||||
from openai import OpenAI |
||||
from dotenv import load_dotenv |
||||
import json |
||||
from datetime import datetime, timedelta |
||||
|
||||
load_dotenv() |
||||
|
||||
openai_api = os.getenv("OPENAI_API_KEY") |
||||
MODEL = "gpt-4o-mini" |
||||
openai = OpenAI() |
||||
|
||||
system_prompt = """You are a medical assistant that processes prescription text. |
||||
Your goal is to extract medicines, tests, and follow-ups in a structured JSON format. |
||||
|
||||
### **Instructions:** |
||||
- Extract **medicines**, **dosages**, and **timings** if available. |
||||
- **Convert vague timings** into precise values: |
||||
- **Before breakfast** → `07:30 AM` |
||||
- **After lunch** → `02:00 PM` |
||||
- **Before dinner** → `07:00 PM` |
||||
- **After dinner** → `10:00 PM` |
||||
- **30 minutes before breakfast** → `07:00 AM` |
||||
- If **"daily"** is mentioned without a time, **assign a logical time** between **08:00 AM - 10:00 PM**. |
||||
- If the prescription says **"every alternate day"**, return `"interval": 2` instead of just `"daily"`. |
||||
|
||||
### **Tests & Follow-ups:** |
||||
- Extract **medical tests** and their required dates. |
||||
- Convert relative times (e.g., `"after 3 months"`) into **exact calendar dates**, using the prescription date. |
||||
- If the prescription date is missing, use today's date. |
||||
- Follow-up should **only be included if required**, not just for general check-ups. |
||||
|
||||
### **Output Format:** |
||||
Return **only valid JSON**, structured as follows: |
||||
|
||||
{ |
||||
"medicines": [ |
||||
{ |
||||
"name": "<Medicine Name>", |
||||
"dosage": "<Dosage>", |
||||
"timing": "<Time>", |
||||
"interval": <Interval in days (if applicable)> |
||||
} |
||||
], |
||||
"tests": [ |
||||
{ |
||||
"name": "<Test Name>", |
||||
"date": "<YYYY-MM-DD>" |
||||
} |
||||
], |
||||
"follow_ups": [ |
||||
{ |
||||
"date": "<YYYY-MM-DD>" |
||||
} |
||||
] |
||||
} |
||||
""" |
||||
|
||||
def clean_json_string(json_str): |
||||
"""Clean and validate JSON string before parsing.""" |
||||
try: |
||||
start = json_str.find('{') |
||||
end = json_str.rfind('}') + 1 |
||||
if start >= 0 and end > 0: |
||||
json_str = json_str[start:end] |
||||
|
||||
# Remove any extra whitespace |
||||
json_str = json_str.strip() |
||||
|
||||
# Attempt to parse the JSON |
||||
return json.loads(json_str) |
||||
except json.JSONDecodeError as e: |
||||
print(f"Failed to parse JSON. Raw response:\n{json_str}") |
||||
print(f"Error: {str(e)}") |
||||
return None |
||||
|
||||
def preprocess_extracted_text(extracted_text): |
||||
"""Calls GPT-4o-mini to process prescription text into structured JSON.""" |
||||
try: |
||||
response = openai.chat.completions.create( |
||||
model=MODEL, |
||||
messages=[ |
||||
{ |
||||
"role": "system", |
||||
"content": system_prompt, |
||||
}, |
||||
{ |
||||
"role": "user", |
||||
"content": f"Process this prescription and return ONLY valid JSON:\n\n{extracted_text}" |
||||
} |
||||
], |
||||
temperature=0.3 # Lower temperature for more consistent JSON output |
||||
) |
||||
|
||||
# Get the response content |
||||
content = response.choices[0].message.content |
||||
|
||||
# Clean and parse the JSON |
||||
parsed_data = clean_json_string(content) |
||||
|
||||
if parsed_data is None: |
||||
return { |
||||
"medicines": [], |
||||
"tests": [], |
||||
"follow_ups": [] |
||||
} |
||||
|
||||
return parsed_data |
||||
|
||||
except Exception as e: |
||||
print(f"Error in API call or processing: {str(e)}") |
||||
return { |
||||
"medicines": [], |
||||
"tests": [], |
||||
"follow_ups": [] |
||||
} |
||||
|
||||
def process_dates(data): |
||||
"""Adjusts test dates and follow-up based on the prescription date or today's date.""" |
||||
try: |
||||
# Extract prescription date (if available) or use today's date |
||||
prescription_date = datetime.strptime("02 JANUARY 2025", "%d %B %Y").date() |
||||
|
||||
# Process test dates |
||||
for test in data.get("tests", []): |
||||
if isinstance(test, dict) and "date" not in test and "after_months" in test: |
||||
test_date = prescription_date + timedelta(days=test["after_months"] * 30) |
||||
test["date"] = test_date.strftime("%Y-%m-%d") |
||||
|
||||
# Process follow-up dates |
||||
follow_ups = data.get("follow_ups", []) |
||||
for follow_up in follow_ups: |
||||
if isinstance(follow_up, dict) and "date" not in follow_up and "after_months" in follow_up: |
||||
follow_up_date = prescription_date + timedelta(days=follow_up["after_months"] * 30) |
||||
follow_up["date"] = follow_up_date.strftime("%Y-%m-%d") |
||||
|
||||
return data |
||||
|
||||
except Exception as e: |
||||
print(f"Error processing dates: {str(e)}") |
||||
return data |
@ -0,0 +1,197 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "7563a171", |
||||
"metadata": {}, |
||||
"source": [] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "d006b2ea-9dfe-49c7-88a9-a5a0775185fd", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Exercise - week 2: German translator\n", |
||||
"\n", |
||||
"This should include a Gradio UI, streaming, use of the system prompt to add expertise, and the ability to switch between models. Bonus points if you can demonstrate use of a tool!\n", |
||||
"\n", |
||||
"The assistant will transform your spoken English to text, then translate it German and speak it out. The image on the UI is just decoration. This exercise was created on MacOS, Python 3.13." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a07e7793-b8f5-44f4-aded-5562f633271a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Install first PortAudio, in MacOS\n", |
||||
"# brew install portaudio\n", |
||||
"\n", |
||||
"\n", |
||||
"!pip install openai speechrecognition pyaudio\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 1, |
||||
"id": "dcae50aa", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import json\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import gradio as gr" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "1796b554", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Initialization\n", |
||||
"\n", |
||||
"load_dotenv()\n", |
||||
"\n", |
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||
"if openai_api_key:\n", |
||||
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||
"else:\n", |
||||
" print(\"OpenAI API Key not set\")\n", |
||||
" \n", |
||||
"MODEL = \"gpt-4o-mini\"\n", |
||||
"openai = OpenAI()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 3, |
||||
"id": "c5caad24", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"\"\"You are a highly skilled language translator specializing in translating English text to German. \n", |
||||
"Your task is to accurately translate any English text provided by the user into German. \n", |
||||
"Ensure that the translations are grammatically correct and contextually appropriate. \n", |
||||
"If the user provides a phrase, sentence, or paragraph in English, respond with the equivalent translation in German.\"\"\" " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 26, |
||||
"id": "aca69563", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import speech_recognition as sr\n", |
||||
"from pydub import AudioSegment\n", |
||||
"from pydub.playback import play\n", |
||||
"import base64\n", |
||||
"from io import BytesIO\n", |
||||
"from PIL import Image\n", |
||||
"\n", |
||||
"\n", |
||||
"def recognize_speech(audio_file):\n", |
||||
" recognizer = sr.Recognizer()\n", |
||||
" with sr.AudioFile(audio_file) as source:\n", |
||||
" audio = recognizer.record(source)\n", |
||||
" try:\n", |
||||
" text = recognizer.recognize_google(audio)\n", |
||||
" return text\n", |
||||
" except sr.UnknownValueError:\n", |
||||
" return \"Google Speech Recognition could not understand audio\"\n", |
||||
" except sr.RequestError as e:\n", |
||||
" return f\"Could not request results from Google Speech Recognition service; {e}\"\n", |
||||
"\n", |
||||
"\n", |
||||
"def get_chatgpt_response(message):\n", |
||||
" response = openai.chat.completions.create(\n", |
||||
" model=MODEL,\n", |
||||
" messages = \n", |
||||
" [{\"role\": \"system\", \"content\": system_message},\n", |
||||
" {\"role\": \"user\", \"content\": message}],\n", |
||||
" max_tokens=150\n", |
||||
" )\n", |
||||
" return response.choices[0].message.content.strip()\n", |
||||
"\n", |
||||
"def process_audio(audio_file):\n", |
||||
" text = recognize_speech(audio_file)\n", |
||||
" if text:\n", |
||||
" response = get_chatgpt_response(text)\n", |
||||
" talker(response)\n", |
||||
" return response\n", |
||||
" return \"Could not recognize speech.\"\n", |
||||
"\n", |
||||
"def talker(message):\n", |
||||
" response = openai.audio.speech.create(\n", |
||||
" model=\"tts-1\",\n", |
||||
" voice=\"onyx\", # Also, try replacing onyx with alloy\n", |
||||
" input=message\n", |
||||
" )\n", |
||||
" \n", |
||||
" audio_stream = BytesIO(response.content)\n", |
||||
" audio = AudioSegment.from_file(audio_stream, format=\"mp3\")\n", |
||||
" play(audio)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f1118141", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# Create Gradio interface\n", |
||||
"\n", |
||||
"# some image decoration to UI, just a static picture\n", |
||||
"image_path =\"week2-exercise-translator-berlin.webp\"\n", |
||||
"\n", |
||||
"with gr.Blocks() as ui:\n", |
||||
" gr.Interface(\n", |
||||
" fn=process_audio,\n", |
||||
" inputs=gr.Audio(type=\"filepath\", label=\"Speak English. German translation in a moment:\"),\n", |
||||
" outputs=\"text\",\n", |
||||
" live=True, \n", |
||||
" )\n", |
||||
" gr.Image(value=image_path, label=\"Das ist Berlin\")\n", |
||||
" \n", |
||||
"ui.launch(inbrowser=True)\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c1284da5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "venv313", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.13.2" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
File diff suppressed because one or more lines are too long
@ -1,267 +1,277 @@
|
||||
{ |
||||
"nbformat": 4, |
||||
"nbformat_minor": 0, |
||||
"metadata": { |
||||
"colab": { |
||||
"provenance": [], |
||||
"gpuType": "T4" |
||||
}, |
||||
"kernelspec": { |
||||
"name": "python3", |
||||
"display_name": "Python 3" |
||||
}, |
||||
"language_info": { |
||||
"name": "python" |
||||
}, |
||||
"accelerator": "GPU" |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": { |
||||
"id": "kU2JrcPlhwd9" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!pip install -q requests torch bitsandbytes transformers sentencepiece accelerate gradio" |
||||
] |
||||
}, |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"!pip install -q requests torch bitsandbytes transformers sentencepiece accelerate gradio" |
||||
], |
||||
"metadata": { |
||||
"id": "kU2JrcPlhwd9" |
||||
}, |
||||
"execution_count": null, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"source": [ |
||||
"**Imports**" |
||||
], |
||||
"metadata": { |
||||
"id": "lAMIVT4iwNg0" |
||||
} |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from google.colab import drive\n", |
||||
"from huggingface_hub import login\n", |
||||
"from google.colab import userdata\n", |
||||
"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n", |
||||
"import torch\n", |
||||
"import gradio as gr\n", |
||||
"\n", |
||||
"hf_token = userdata.get('HF_TOKEN')\n", |
||||
"login(hf_token, add_to_git_credential=True)" |
||||
], |
||||
"metadata": { |
||||
"id": "-Apd7-p-hyLk" |
||||
}, |
||||
"execution_count": 2, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"source": [ |
||||
"**Model**" |
||||
], |
||||
"metadata": { |
||||
"id": "xa0qYqZrwQ66" |
||||
} |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"model_name = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n", |
||||
"quant_config = BitsAndBytesConfig(\n", |
||||
" load_in_4bit=True,\n", |
||||
" bnb_4bit_use_double_quant=True,\n", |
||||
" bnb_4bit_compute_dtype=torch.bfloat16,\n", |
||||
" bnb_4bit_quant_type=\"nf4\"\n", |
||||
")\n", |
||||
"\n", |
||||
"model = AutoModelForCausalLM.from_pretrained(\n", |
||||
" model_name,\n", |
||||
" device_map=\"auto\",\n", |
||||
" quantization_config=quant_config\n", |
||||
")" |
||||
], |
||||
"metadata": { |
||||
"id": "z5enGmuKjtJu" |
||||
}, |
||||
"execution_count": null, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"source": [ |
||||
"**Tokenizer**" |
||||
], |
||||
"metadata": { |
||||
"id": "y1hUSmWlwSbp" |
||||
} |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n", |
||||
"tokenizer.pad_token = tokenizer.eos_token" |
||||
], |
||||
"metadata": { |
||||
"id": "WjxNWW6bvdgj" |
||||
}, |
||||
"execution_count": 4, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"source": [ |
||||
"**Functions**" |
||||
], |
||||
"metadata": { |
||||
"id": "1pg2U-B3wbIK" |
||||
} |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"def generate_dataset(topic, number_of_data, inst1, resp1, inst2, resp2, inst3, resp3):\n", |
||||
" # Convert user inputs into multi-shot examples\n", |
||||
" multi_shot_examples = [\n", |
||||
" {\"instruction\": inst1, \"response\": resp1},\n", |
||||
" {\"instruction\": inst2, \"response\": resp2},\n", |
||||
" {\"instruction\": inst3, \"response\": resp3}\n", |
||||
" ]\n", |
||||
"\n", |
||||
" # System prompt\n", |
||||
" system_prompt = f\"\"\"\n", |
||||
" You are a helpful assistant whose main purpose is to generate datasets.\n", |
||||
" Topic: {topic}\n", |
||||
" Return the dataset in JSON format. Use examples with simple, fun, and easy-to-understand instructions for kids.\n", |
||||
" Include the following examples: {multi_shot_examples}\n", |
||||
" Return {number_of_data} examples each time.\n", |
||||
" Do not repeat the provided examples.\n", |
||||
" \"\"\"\n", |
||||
"\n", |
||||
" # Example Messages\n", |
||||
" messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": f\"Please generate my dataset for {topic}\"}\n", |
||||
" ]\n", |
||||
"\n", |
||||
" # Tokenize Input\n", |
||||
" inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\").to(\"cuda\")\n", |
||||
" streamer = TextStreamer(tokenizer)\n", |
||||
"\n", |
||||
" # Generate Output\n", |
||||
" outputs = model.generate(inputs, max_new_tokens=2000, streamer=streamer)\n", |
||||
"\n", |
||||
" # Decode and Return\n", |
||||
" return tokenizer.decode(outputs[0], skip_special_tokens=True)\n", |
||||
"\n", |
||||
"\n", |
||||
"def gradio_interface(topic, number_of_data, inst1, resp1, inst2, resp2, inst3, resp3):\n", |
||||
" return generate_dataset(topic, number_of_data, inst1, resp1, inst2, resp2, inst3, resp3)" |
||||
], |
||||
"metadata": { |
||||
"id": "ZvljDKdji8iV" |
||||
}, |
||||
"execution_count": 12, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"source": [ |
||||
"**Default Values**" |
||||
], |
||||
"metadata": { |
||||
"id": "_WDZ5dvRwmng" |
||||
} |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"default_topic = \"Talking to a (5-8) years old and teaching them manners.\"\n", |
||||
"default_number_of_data = 10\n", |
||||
"default_multi_shot_examples = [\n", |
||||
" {\n", |
||||
" \"instruction\": \"Why do I have to say please when I want something?\",\n", |
||||
" \"response\": \"Because it’s like magic! It shows you’re nice, and people want to help you more.\"\n", |
||||
" },\n", |
||||
" {\n", |
||||
" \"instruction\": \"What should I say if someone gives me a toy?\",\n", |
||||
" \"response\": \"You say, 'Thank you!' because it makes them happy you liked it.\"\n", |
||||
" },\n", |
||||
" {\n", |
||||
" \"instruction\": \"why should I listen to my parents?\",\n", |
||||
" \"response\": \"Because parents want the best for you and they love you the most.\"\n", |
||||
" }\n", |
||||
"]" |
||||
], |
||||
"metadata": { |
||||
"id": "JAdfqYXnvEDE" |
||||
}, |
||||
"execution_count": 13, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"source": [ |
||||
"**Init gradio**" |
||||
], |
||||
"metadata": { |
||||
"id": "JwZtD032wuK8" |
||||
} |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"gr_interface = gr.Interface(\n", |
||||
" fn=gradio_interface,\n", |
||||
" inputs=[\n", |
||||
" gr.Textbox(label=\"Topic\", value=default_topic, lines=2),\n", |
||||
" gr.Number(label=\"Number of Examples\", value=default_number_of_data, precision=0),\n", |
||||
" gr.Textbox(label=\"Instruction 1\", value=default_multi_shot_examples[0][\"instruction\"]),\n", |
||||
" gr.Textbox(label=\"Response 1\", value=default_multi_shot_examples[0][\"response\"]),\n", |
||||
" gr.Textbox(label=\"Instruction 2\", value=default_multi_shot_examples[1][\"instruction\"]),\n", |
||||
" gr.Textbox(label=\"Response 2\", value=default_multi_shot_examples[1][\"response\"]),\n", |
||||
" gr.Textbox(label=\"Instruction 3\", value=default_multi_shot_examples[2][\"instruction\"]),\n", |
||||
" gr.Textbox(label=\"Response 3\", value=default_multi_shot_examples[2][\"response\"]),\n", |
||||
" ],\n", |
||||
" outputs=gr.Textbox(label=\"Generated Dataset\")\n", |
||||
")" |
||||
], |
||||
"metadata": { |
||||
"id": "xy2RP5T-vxXg" |
||||
}, |
||||
"execution_count": 14, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"source": [ |
||||
"**Run the app**" |
||||
], |
||||
"metadata": { |
||||
"id": "HZx-mm9Uw3Ph" |
||||
} |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"gr_interface.launch()" |
||||
], |
||||
"metadata": { |
||||
"id": "bfGs5ip8mndg" |
||||
}, |
||||
"execution_count": null, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [], |
||||
"metadata": { |
||||
"id": "Cveqx392x7Mm" |
||||
}, |
||||
"execution_count": null, |
||||
"outputs": [] |
||||
} |
||||
] |
||||
} |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": { |
||||
"id": "lAMIVT4iwNg0" |
||||
}, |
||||
"source": [ |
||||
"**Imports**" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": { |
||||
"id": "-Apd7-p-hyLk" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import os\n", |
||||
"import requests\n", |
||||
"from google.colab import drive\n", |
||||
"from huggingface_hub import login\n", |
||||
"from google.colab import userdata\n", |
||||
"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n", |
||||
"import torch\n", |
||||
"import gradio as gr\n", |
||||
"\n", |
||||
"hf_token = userdata.get('HF_TOKEN')\n", |
||||
"login(hf_token, add_to_git_credential=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": { |
||||
"id": "xa0qYqZrwQ66" |
||||
}, |
||||
"source": [ |
||||
"**Model**" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": { |
||||
"id": "z5enGmuKjtJu" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"model_name = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n", |
||||
"quant_config = BitsAndBytesConfig(\n", |
||||
" load_in_4bit=True,\n", |
||||
" bnb_4bit_use_double_quant=True,\n", |
||||
" bnb_4bit_compute_dtype=torch.bfloat16,\n", |
||||
" bnb_4bit_quant_type=\"nf4\"\n", |
||||
")\n", |
||||
"\n", |
||||
"model = AutoModelForCausalLM.from_pretrained(\n", |
||||
" model_name,\n", |
||||
" device_map=\"auto\",\n", |
||||
" quantization_config=quant_config\n", |
||||
")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": { |
||||
"id": "y1hUSmWlwSbp" |
||||
}, |
||||
"source": [ |
||||
"**Tokenizer**" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": { |
||||
"id": "WjxNWW6bvdgj" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n", |
||||
"tokenizer.pad_token = tokenizer.eos_token" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": { |
||||
"id": "1pg2U-B3wbIK" |
||||
}, |
||||
"source": [ |
||||
"**Functions**" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": { |
||||
"id": "ZvljDKdji8iV" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def generate_dataset(topic, number_of_data, inst1, resp1, inst2, resp2, inst3, resp3):\n", |
||||
" # Convert user inputs into multi-shot examples\n", |
||||
" multi_shot_examples = [\n", |
||||
" {\"instruction\": inst1, \"response\": resp1},\n", |
||||
" {\"instruction\": inst2, \"response\": resp2},\n", |
||||
" {\"instruction\": inst3, \"response\": resp3}\n", |
||||
" ]\n", |
||||
"\n", |
||||
" # System prompt\n", |
||||
" system_prompt = f\"\"\"\n", |
||||
" You are a helpful assistant whose main purpose is to generate datasets.\n", |
||||
" Topic: {topic}\n", |
||||
" Return the dataset in JSON format. Use examples with simple, fun, and easy-to-understand instructions for kids.\n", |
||||
" Include the following examples: {multi_shot_examples}\n", |
||||
" Return {number_of_data} examples each time.\n", |
||||
" Do not repeat the provided examples.\n", |
||||
" \"\"\"\n", |
||||
"\n", |
||||
" # Example Messages\n", |
||||
" messages = [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||
" {\"role\": \"user\", \"content\": f\"Please generate my dataset for {topic}\"}\n", |
||||
" ]\n", |
||||
"\n", |
||||
" # Tokenize Input\n", |
||||
" inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\").to(\"cuda\")\n", |
||||
" streamer = TextStreamer(tokenizer)\n", |
||||
"\n", |
||||
" # Generate Output\n", |
||||
" outputs = model.generate(inputs, max_new_tokens=2000, streamer=streamer)\n", |
||||
"\n", |
||||
" # Decode and Return\n", |
||||
" return tokenizer.decode(outputs[0], skip_special_tokens=True)\n", |
||||
"\n", |
||||
"\n", |
||||
"def gradio_interface(topic, number_of_data, inst1, resp1, inst2, resp2, inst3, resp3):\n", |
||||
" return generate_dataset(topic, number_of_data, inst1, resp1, inst2, resp2, inst3, resp3)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": { |
||||
"id": "_WDZ5dvRwmng" |
||||
}, |
||||
"source": [ |
||||
"**Default Values**" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": { |
||||
"id": "JAdfqYXnvEDE" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"default_topic = \"Talking to a (5-8) years old and teaching them manners.\"\n", |
||||
"default_number_of_data = 10\n", |
||||
"default_multi_shot_examples = [\n", |
||||
" {\n", |
||||
" \"instruction\": \"Why do I have to say please when I want something?\",\n", |
||||
" \"response\": \"Because it’s like magic! It shows you’re nice, and people want to help you more.\"\n", |
||||
" },\n", |
||||
" {\n", |
||||
" \"instruction\": \"What should I say if someone gives me a toy?\",\n", |
||||
" \"response\": \"You say, 'Thank you!' because it makes them happy you liked it.\"\n", |
||||
" },\n", |
||||
" {\n", |
||||
" \"instruction\": \"why should I listen to my parents?\",\n", |
||||
" \"response\": \"Because parents want the best for you and they love you the most.\"\n", |
||||
" }\n", |
||||
"]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": { |
||||
"id": "JwZtD032wuK8" |
||||
}, |
||||
"source": [ |
||||
"**Init gradio**" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": { |
||||
"id": "xy2RP5T-vxXg" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gr_interface = gr.Interface(\n", |
||||
" fn=gradio_interface,\n", |
||||
" inputs=[\n", |
||||
" gr.Textbox(label=\"Topic\", value=default_topic, lines=2),\n", |
||||
" gr.Number(label=\"Number of Examples\", value=default_number_of_data, precision=0),\n", |
||||
" gr.Textbox(label=\"Instruction 1\", value=default_multi_shot_examples[0][\"instruction\"]),\n", |
||||
" gr.Textbox(label=\"Response 1\", value=default_multi_shot_examples[0][\"response\"]),\n", |
||||
" gr.Textbox(label=\"Instruction 2\", value=default_multi_shot_examples[1][\"instruction\"]),\n", |
||||
" gr.Textbox(label=\"Response 2\", value=default_multi_shot_examples[1][\"response\"]),\n", |
||||
" gr.Textbox(label=\"Instruction 3\", value=default_multi_shot_examples[2][\"instruction\"]),\n", |
||||
" gr.Textbox(label=\"Response 3\", value=default_multi_shot_examples[2][\"response\"]),\n", |
||||
" ],\n", |
||||
" outputs=gr.Textbox(label=\"Generated Dataset\")\n", |
||||
")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"metadata": { |
||||
"id": "HZx-mm9Uw3Ph" |
||||
}, |
||||
"source": [ |
||||
"**Run the app**" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": { |
||||
"id": "bfGs5ip8mndg" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"gr_interface.launch()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": { |
||||
"id": "Cveqx392x7Mm" |
||||
}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"accelerator": "GPU", |
||||
"colab": { |
||||
"gpuType": "T4", |
||||
"provenance": [] |
||||
}, |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 4 |
||||
} |
||||
|
@ -0,0 +1,150 @@
|
||||
{ |
||||
"nbformat": 4, |
||||
"nbformat_minor": 0, |
||||
"metadata": { |
||||
"colab": { |
||||
"provenance": [], |
||||
"gpuType": "T4", |
||||
"authorship_tag": "ABX9TyPtAT7Yq5xd4vDcJEZtg69J" |
||||
}, |
||||
"kernelspec": { |
||||
"name": "python3", |
||||
"display_name": "Python 3" |
||||
}, |
||||
"language_info": { |
||||
"name": "python" |
||||
}, |
||||
"accelerator": "GPU" |
||||
}, |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"# getting the latest transformers first, since this will require a restart\n", |
||||
"\n", |
||||
"!pip install git+https://github.com/huggingface/transformers.git" |
||||
], |
||||
"metadata": { |
||||
"id": "6gGKXU5RXORf" |
||||
}, |
||||
"execution_count": null, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import torch\n", |
||||
"from google.colab import userdata\n", |
||||
"from huggingface_hub import login\n", |
||||
"from transformers import AutoProcessor, AutoModelForImageTextToText\n", |
||||
"from google.colab import files" |
||||
], |
||||
"metadata": { |
||||
"id": "yCRrF4aiXPPo" |
||||
}, |
||||
"execution_count": null, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"# logging in to HF\n", |
||||
"\n", |
||||
"hf_token = userdata.get('HF_TOKEN')\n", |
||||
"login(hf_token, add_to_git_credential=True)" |
||||
], |
||||
"metadata": { |
||||
"id": "AAlOQuCbXcrv" |
||||
}, |
||||
"execution_count": null, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"metadata": { |
||||
"id": "_RRVc2j2Vun-" |
||||
}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# this will start an input prompt for uploading local files\n", |
||||
"\n", |
||||
"uploaded = files.upload()\n", |
||||
"print(uploaded.keys()) # this will look sth like dict_keys([\"note2.jpg\"])" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"'''\n", |
||||
"ChatGPT and Gemini explain the following part roughly like so:\n", |
||||
"The string contained in image_path is the key of the entry in the dictionary of uploaded files (see box above).\n", |
||||
"The value to that key contains the image in binary format.\n", |
||||
"The \"with open(image_path, \"wb\") as f\" part means: Create a new file \"note2.jpg\" on the server, and write to it in binary mode (\"wb\").\n", |
||||
"f.write(image) writes the binary image to that new file. \"note2.jpg\" aka image_path will now contain the image.\n", |
||||
"'''\n", |
||||
"\n", |
||||
"image_path = \"note2.jpg\" # update this string depending on the printout in the previous cell!\n", |
||||
"image = uploaded[image_path]\n", |
||||
"with open(image_path, \"wb\") as f:\n", |
||||
" f.write(image)" |
||||
], |
||||
"metadata": { |
||||
"id": "V_UAuSSkXBKh" |
||||
}, |
||||
"execution_count": null, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"# from HF model instructions\n", |
||||
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", |
||||
"model = AutoModelForImageTextToText.from_pretrained(\"stepfun-ai/GOT-OCR-2.0-hf\", device_map=device)\n", |
||||
"processor = AutoProcessor.from_pretrained(\"stepfun-ai/GOT-OCR-2.0-hf\")" |
||||
], |
||||
"metadata": { |
||||
"id": "AiFP-mQtXrpV" |
||||
}, |
||||
"execution_count": null, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"# also from HF documentation about this model, see https://huggingface.co/stepfun-ai/GOT-OCR-2.0-hf\n", |
||||
"\n", |
||||
"image = image_path\n", |
||||
"inputs = processor(image, return_tensors=\"pt\").to(device)\n", |
||||
"\n", |
||||
"ocr = model.generate(\n", |
||||
" **inputs,\n", |
||||
" do_sample=False,\n", |
||||
" tokenizer=processor.tokenizer,\n", |
||||
" stop_strings=\"<|im_end|>\",\n", |
||||
" max_new_tokens=4096,\n", |
||||
")" |
||||
], |
||||
"metadata": { |
||||
"id": "7Adr8HB_YNf5" |
||||
}, |
||||
"execution_count": null, |
||||
"outputs": [] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"source": [ |
||||
"# prints out the recognized text. This can read my handwriting pretty well! And it works super quick on the free T4 GPU server here.\n", |
||||
"\n", |
||||
"print(processor.decode(ocr[0, inputs[\"input_ids\"].shape[1]:], skip_special_tokens=True))" |
||||
], |
||||
"metadata": { |
||||
"id": "nRsRUIIuYdJ9" |
||||
}, |
||||
"execution_count": null, |
||||
"outputs": [] |
||||
} |
||||
] |
||||
} |
@ -0,0 +1,54 @@
|
||||
# What is this? |
||||
This is Telegram bot, it receives text and responds with robotic voice responses, which are are generated by locally run Ollama. It supports command "/echo" which allows you to receive just a voiceover of your message without involving AI. Text-to-speach (TTS) is performed by locally run tool Balabolka with preinstalled robotic voice. |
||||
|
||||
## How to create bot? |
||||
- Open Telegram and search for BotFather (it’s an official Telegram bot for creating and managing other bots). |
||||
- Start a chat with BotFather and type /newbot to create a new bot. |
||||
- BotFather will ask you to provide a name for your bot. Choose a name that you want your bot to be identified by. |
||||
- After you provide a name, it will ask for a username. The username must be unique and must end with bot (e.g., myamazingbot). |
||||
- After creating the bot, BotFather will give you a token. |
||||
|
||||
Save this token. Do not worry if you loose it - you can always open BotFather, get list of your bots and request tokens for each of them. |
||||
|
||||
## Update environment variables |
||||
In the same folder with script create file named ".env". Use exactly this name, just 4 symbols. |
||||
In this file add text |
||||
``` |
||||
BOT_TOKEN='<TOKEN_FOR_YOUR_BOT>' |
||||
HOSTNAME='<YOUR_HOSTNAME_OR_IP>' |
||||
``` |
||||
Ex. |
||||
``` |
||||
BOT_TOKEN='8030XXXXXX:AAHtR5F3H6WUYvAItQa2THEIFoEDbXXXXXX' |
||||
HOSTNAME='118-YYY-71-83-67' |
||||
``` |
||||
|
||||
## Setting up a webhook |
||||
Before using Telegram you need to set up webhook. |
||||
If you have public IP, then simply run the script and visit http://your-server-ip-or-domain:5000/setwebhook to set the webhook URL. This will configure Telegram to send updates to your bot.<br> |
||||
If you don't have public IP then: |
||||
- Run the script on local machine |
||||
- Register for free on [ngrok.com](https://ngrok.com). Sign in. After signing in, go to the "Get Started" page and copy your authtoken. |
||||
- Download ngrok from [ngrok.com](https://ngrok.com). Unzip downloaded file ex. in script folder. |
||||
- Run "ngrok config add-authtoken" |
||||
- Run command "ngrok http 5000". Pay attention that port is not 80 (default), but 5000, because script creates local server on this port. When ngrok started, it will give you URL, ex. "https://b\<your IP address\>.ngrok-free.app" |
||||
- To set the webhook URL, visit URL https://api.telegram.org/bot\<YOUR_BOT_TOKEN\>/setWebhook?url=\<IP address which ngrock shows\>/\<YOUR_BOT_TOKEN\>. Ex. "https://api.telegram.org/bot8030XXXXXX:AAHtR5F3H6WUYvAItQa2THEIFoEDbXXXXXX/setWebhook?url=https://b118-YYY-71-83-67.ngrok-free.app/8030XXXXXX:AAHtR5F3H6WUYvAItQa2THEIFoEDbXXXXXX". In case of success, you will receive json like this "{"ok":true,"result":true,"description":"Webhook was set"}" |
||||
|
||||
## How to test bot |
||||
Now you can search in Telegram bot with your name, ex "myamazingbot". Start conversation and send something. In logs of your script you should be able to see this message, printed from handle_message() method. |
||||
|
||||
## How to install local TTS (Text-to-speach) |
||||
**Note!** You can use remote TTS, ex. from ChatGPT. You don't have to use local one. I used local just because it is free.<br> |
||||
I assume you can use many applications for local TTS. In my case I used Balabolka tool, in particular, its CLI tool called "balcon" (https://www.cross-plus-a.com/ru/bconsole.html). After tool installation you need to install voices. Pay attention that some voices free, some are not. Write name of voice in script variable TTS_VOICE_NAME. In my case it is 'Maxim'.<br> |
||||
Note that if voice, which you use, supports only one language, you should send to TTS text in this language only. |
||||
Take into account that TTS results (.mp3 files) will be stored inside script folder. THey are going to be deleted when they are sent back to user, but in case of any failures, you may need to delete them manually. |
||||
|
||||
## Supported bot commands |
||||
- "/start" - show information about supported commands |
||||
- "/echo" - just convert text to speech, do not use AI to create response |
||||
- "/clear" - clear the bot's memory so you can start the conversation from the beginning |
||||
|
||||
## Limitations |
||||
- This bot is created just for fun. It is quite unstable, so you may need to fix some bugs when you find them |
||||
- Bot is not intended to be used in parallel by many users. Quite likely it will handle several users at the same time, but not tens or hundreds. So, if you need this feature you may need to make slight changes in script or its deployment. |
||||
- Chat history for every chat is stored in memory in global variable, which is very bad. Ex. it will lead to memory exhaustion. It will also lead to the history being cleared every time the bot is restarted. |
@ -0,0 +1,199 @@
|
||||
import os |
||||
import subprocess |
||||
from dotenv import load_dotenv |
||||
from quart import Quart, request, jsonify |
||||
from telegram import Update |
||||
from telegram.ext import Application, CommandHandler, MessageHandler, filters |
||||
import asyncio |
||||
import concurrent.futures |
||||
import ollama |
||||
|
||||
# Load environment variables |
||||
load_dotenv() |
||||
TOKEN = os.getenv('BOT_TOKEN') |
||||
if TOKEN: |
||||
print(f"Bot token is {TOKEN}") |
||||
else: |
||||
print("Please provide your bot token in .env file") |
||||
HOSTNAME = os.getenv('HOSTNAME') |
||||
if HOSTNAME: |
||||
print(f"Host name is {HOSTNAME}") |
||||
else: |
||||
print("Please provide your host name (or IP address) in .env file") |
||||
|
||||
BALCON_PATH = r"C:\Program Files (x86)\Balabolka\balcon\balcon.exe" |
||||
TTS_VOICE_NAME = 'Maxim' |
||||
MODEL = "llama3.1:8b" |
||||
SYSTEM_PROMPT = "You are an extremely cynical and rude assistant. Use the words 'leather bastard' as often " \ |
||||
"as possible. Use the words 'leather bastard' every time you address a user or mention any " \ |
||||
"person. For example 'It doesn't concern you, leather bastard'" |
||||
|
||||
# Initialize the Application |
||||
app = Quart(__name__) |
||||
bot_application = Application.builder().token(TOKEN).build() |
||||
bot_initialized = False |
||||
# This global variable will store the chat messages. It is very bad practice. |
||||
chat_messages = {} |
||||
|
||||
|
||||
async def initialize_bot(): |
||||
"""Ensure the bot is initialized only once.""" |
||||
global bot_initialized |
||||
if not bot_initialized: |
||||
await bot_application.initialize() |
||||
await bot_application.start() |
||||
bot_initialized = True |
||||
print("Telegram bot initialized and started.") |
||||
|
||||
|
||||
# Command handler for /start |
||||
async def start(update: Update, context): |
||||
await update.message.reply_text( |
||||
"Hello, welcome to the bot! Here are the commands you can use:\n" |
||||
"/echo - Just convert text to speech, do not use AI to create response\n" |
||||
"/clear - Clear the bot's memory so you can start the conversation from the beginning." |
||||
) |
||||
|
||||
|
||||
async def clear(update: Update, context): |
||||
if update.message.chat_id in chat_messages: |
||||
del chat_messages[update.message.chat_id] |
||||
await update.message.reply_text( |
||||
"The bot's memory has been cleared. You can start the conversation from the beginning.") |
||||
|
||||
|
||||
def text_to_audio_file(input_text, filename="output.mp3"): |
||||
""" |
||||
Generate audio using Balcon and save it to the current directory. |
||||
:param input_text: Text to convert to speech. |
||||
:param filename: Desired output file name. |
||||
:return: Path to the generated file. |
||||
""" |
||||
# Ensure the filename is in the current script directory |
||||
current_dir = os.path.dirname(os.path.abspath(__file__)) |
||||
file_path = os.path.join(current_dir, filename) |
||||
|
||||
command = [BALCON_PATH, '-n', TTS_VOICE_NAME, "-t", input_text, "-w", file_path] |
||||
try: |
||||
subprocess.run(command, check=True) |
||||
print(f"Audio saved at: {file_path}") |
||||
return file_path |
||||
except subprocess.CalledProcessError as e: |
||||
print(f"Error generating audio: {e}") |
||||
return None |
||||
|
||||
|
||||
def ask_llm(messages): |
||||
response = ollama.chat(model=MODEL, messages=messages) |
||||
return response['message']['content'] |
||||
|
||||
|
||||
def append_chat_message(chat_id, message, role): |
||||
if chat_id not in chat_messages: |
||||
chat_messages[chat_id] = [{"role": "system", "content": SYSTEM_PROMPT}] |
||||
chat_messages[chat_id].append({"role": role, "content": message}) |
||||
|
||||
|
||||
def get_first_word(input_string): |
||||
# Split the string into words and strip any surrounding whitespace from the first word |
||||
return input_string.split()[0].strip() if input_string.strip() else None |
||||
|
||||
|
||||
def process_user_message(message): |
||||
chat_id = message.chat_id |
||||
message_id = message.message_id |
||||
user_message = message.text |
||||
|
||||
words = user_message.strip().split(maxsplit=1) |
||||
if len(words) == 0: |
||||
return "", "" |
||||
if words[0] in ["/echo", "/clear"]: |
||||
# Remove the command from the message |
||||
tts_message = words[1] if len(words) > 1 else "" |
||||
else: |
||||
append_chat_message(chat_id, user_message, "user") |
||||
tts_message = ask_llm(chat_messages[chat_id]) |
||||
append_chat_message(chat_id, tts_message, "assistant") |
||||
|
||||
audio_file_path = text_to_audio_file(tts_message, filename=f"{chat_id}-{message_id}.mp3") |
||||
return audio_file_path, tts_message |
||||
|
||||
|
||||
# Message handler to log and print all incoming messages |
||||
async def handle_message(update: Update, context): |
||||
user_message = update.message.text |
||||
chat_id = update.message.chat_id |
||||
sender = update.message.from_user |
||||
|
||||
print(f"Message from {sender.first_name} (chat ID: {chat_id}): {user_message}") |
||||
|
||||
# Wait when processing of user message is done. |
||||
# During this time send chat action to user, so that it will see that bot is preparing audio. |
||||
# Notification diasppear after 5 seconds, so you need to send them repeatedly every 4 seconds. |
||||
audio_file_path = None |
||||
with concurrent.futures.ThreadPoolExecutor() as executor: |
||||
future = executor.submit(process_user_message, update.message) |
||||
while True: |
||||
try: |
||||
# Show to user that bot is busy with preparing audio response |
||||
await context.bot.send_chat_action(chat_id=chat_id, action='record_audio') |
||||
result, answer = future.result(timeout=4) |
||||
audio_file_path = result |
||||
user_message = answer |
||||
break |
||||
except concurrent.futures.TimeoutError: |
||||
continue |
||||
|
||||
if len(answer) == 0: |
||||
await update.message.reply_text(f"Empty message") |
||||
return |
||||
|
||||
try: |
||||
with open(audio_file_path, 'rb') as audio_file: |
||||
await context.bot.send_audio( |
||||
chat_id=chat_id, |
||||
audio=audio_file, |
||||
caption="Ответ робота", |
||||
write_timeout=120 |
||||
) |
||||
print(f"Audio sent to {sender.first_name} (chat ID: {chat_id})") |
||||
os.remove(audio_file_path) |
||||
except Exception as e: |
||||
print(f"Exception while sending file: {e}") |
||||
|
||||
# Optional text response. Comment this line if you want bot to answer only with audio |
||||
await update.message.reply_text(user_message) |
||||
|
||||
|
||||
bot_application.add_handler(CommandHandler('start', start)) |
||||
bot_application.add_handler(CommandHandler('echo', handle_message)) |
||||
bot_application.add_handler(CommandHandler('clear', clear)) |
||||
bot_application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message)) |
||||
|
||||
@app.route('/' + TOKEN, methods=['POST']) |
||||
async def webhook(): |
||||
"""Webhook endpoint for receiving updates.""" |
||||
try: |
||||
await initialize_bot() # Ensure bot is initialized before processing updates |
||||
update = Update.de_json(await request.get_json(), bot_application.bot) |
||||
await bot_application.process_update(update) |
||||
except Exception as e: |
||||
print(f"Error processing update: {e}") |
||||
return jsonify({"status": "ok"}) |
||||
|
||||
|
||||
# @app.route('/setwebhook', methods=['GET']) |
||||
async def set_webhook(): |
||||
"""Set the webhook with Telegram.""" |
||||
await initialize_bot() # Ensure bot is initialized before setting the webhook |
||||
webhook_url = f"https://{HOSTNAME}/{TOKEN}" |
||||
success = await bot_application.bot.set_webhook(webhook_url) |
||||
if success: |
||||
return jsonify({"status": "webhook set successfully", "url": webhook_url}) |
||||
return jsonify({"status": "failed to set webhook"}) |
||||
|
||||
|
||||
if __name__ == '__main__': |
||||
loop = asyncio.get_event_loop() |
||||
loop.create_task(initialize_bot()) # Initialize bot at startup |
||||
app.run(host='0.0.0.0', port=5000) |
@ -0,0 +1,908 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Code Generator\n", |
||||
"\n", |
||||
"The requirement: use an Open Source model to generate high performance C++ code from Python code\n", |
||||
"\n", |
||||
"To replicate this, you'll need to set up a HuggingFace endpoint as I do in the video. It's simple to do, and it's quite satisfying to see the results!\n", |
||||
"\n", |
||||
"It's also an important part of your learning; this is the first example of deploying an open source model to be behind an API. We'll return to this in Week 8, but this should plant a seed in your mind for what's involved in moving open source models into production." |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "22e1567b-33fd-49e7-866e-4b635d15715a", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h1 style=\"color:#900;\">Important - Pause Endpoints when not in use</h1>\n", |
||||
" <span style=\"color:#900;\">\n", |
||||
" If you do decide to use HuggingFace endpoints for this project, you should stop or pause the endpoints when you are done to avoid accruing unnecessary running cost. The costs are very low as long as you only run the endpoint when you're using it. Navigate to the HuggingFace endpoint UI <a href=\"https://ui.endpoints.huggingface.co/\">here,</a> open your endpoint, and click Pause to put it on pause so you no longer pay for it. \n", |
||||
"Many thanks to student John L. for raising this.\n", |
||||
"<br/><br/>\n", |
||||
"In week 8 we will use Modal instead of HuggingFace endpoints; with Modal you only pay for the time that you use it and you should get free credits.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import io\n", |
||||
"import sys\n", |
||||
"import json\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import google.generativeai as genai\n", |
||||
"import anthropic\n", |
||||
"from IPython.display import Markdown, display, update_display\n", |
||||
"import gradio as gr\n", |
||||
"import subprocess" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4f672e1c-87e9-4865-b760-370fa605e614", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# environment\n", |
||||
"\n", |
||||
"load_dotenv()\n", |
||||
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", |
||||
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n", |
||||
"os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')\n", |
||||
"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# initialize\n", |
||||
"\n", |
||||
"openai = OpenAI()\n", |
||||
"claude = anthropic.Anthropic()\n", |
||||
"OPENAI_MODEL = \"gpt-4o\"\n", |
||||
"CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\"\n", |
||||
"GEMINI_MODEL = 'gemini-1.5-pro'" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "6896636f-923e-4a2c-9d6c-fac07828a201", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"system_message = \"You are an assistant that reimplements Python code in high performance C++ for an M1 Mac. \"\n", |
||||
"system_message += \"Respond only with C++ code; use comments sparingly and do not provide any explanation other than occasional comments. \"\n", |
||||
"system_message += \"The C++ response needs to produce an identical output in the fastest possible time. Keep implementations of random number generators identical so that results match exactly.\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8e7b3546-57aa-4c29-bc5d-f211970d04eb", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def user_prompt_for(python):\n", |
||||
" user_prompt = \"Rewrite this Python code in C++ with the fastest possible implementation that produces identical output in the least time. \"\n", |
||||
" user_prompt += \"Respond only with C++ code; do not explain your work other than a few comments. \"\n", |
||||
" user_prompt += \"Pay attention to number types to ensure no int overflows. Remember to #include all necessary C++ packages such as iomanip.\\n\\n\"\n", |
||||
" user_prompt += python\n", |
||||
" return user_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c6190659-f54c-4951-bef4-4960f8e51cc4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def messages_for(python):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_message},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(python)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "71e1ba8c-5b05-4726-a9f3-8d8c6257350b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# write to a file called optimized.cpp\n", |
||||
"\n", |
||||
"def write_output(cpp):\n", |
||||
" code = cpp.replace(\"```cpp\",\"\").replace(\"```\",\"\")\n", |
||||
" with open(\"optimized.cpp\", \"w\") as f:\n", |
||||
" f.write(code)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e7d2fea8-74c6-4421-8f1e-0e76d5b201b9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def optimize_gpt(python): \n", |
||||
" stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python), stream=True)\n", |
||||
" reply = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" fragment = chunk.choices[0].delta.content or \"\"\n", |
||||
" reply += fragment\n", |
||||
" print(fragment, end='', flush=True)\n", |
||||
" write_output(reply)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7cd84ad8-d55c-4fe0-9eeb-1895c95c4a9d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def optimize_claude(python):\n", |
||||
" result = claude.messages.stream(\n", |
||||
" model=CLAUDE_MODEL,\n", |
||||
" max_tokens=2000,\n", |
||||
" system=system_message,\n", |
||||
" messages=[{\"role\": \"user\", \"content\": user_prompt_for(python)}],\n", |
||||
" )\n", |
||||
" reply = \"\"\n", |
||||
" with result as stream:\n", |
||||
" for text in stream.text_stream:\n", |
||||
" reply += text\n", |
||||
" print(text, end=\"\", flush=True)\n", |
||||
" write_output(reply)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "3625fcd6-209f-481c-a745-dcbcf5e44bc1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def optimize_gemini(python):\n", |
||||
" gemini = genai.GenerativeModel(\n", |
||||
" model_name = GEMINI_MODEL,\n", |
||||
" system_instruction=system_message\n", |
||||
" )\n", |
||||
" response = gemini.generate_content(\n", |
||||
" user_prompt_for(python),\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" reply = \"\"\n", |
||||
" for chunk in response:\n", |
||||
" reply += chunk.text\n", |
||||
" print(chunk.text, end=\"\", flush=True)\n", |
||||
" write_output(reply)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a1cbb778-fa57-43de-b04b-ed523f396c38", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"pi = \"\"\"\n", |
||||
"import time\n", |
||||
"\n", |
||||
"def calculate(iterations, param1, param2):\n", |
||||
" result = 1.0\n", |
||||
" for i in range(1, iterations+1):\n", |
||||
" j = i * param1 - param2\n", |
||||
" result -= (1/j)\n", |
||||
" j = i * param1 + param2\n", |
||||
" result += (1/j)\n", |
||||
" return result\n", |
||||
"\n", |
||||
"start_time = time.time()\n", |
||||
"result = calculate(100_000_000, 4, 1) * 4\n", |
||||
"end_time = time.time()\n", |
||||
"\n", |
||||
"print(f\"Result: {result:.12f}\")\n", |
||||
"print(f\"Execution Time: {(end_time - start_time):.6f} seconds\")\n", |
||||
"\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "fe891e3a-d1c4-4ee5-a361-34d0982fcff4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"optimize_gemini(pi)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "7fe1cd4b-d2c5-4303-afed-2115a3fef200", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"exec(pi)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "105db6f9-343c-491d-8e44-3a5328b81719", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"optimize_gpt(pi)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bf26ee95-0c77-491d-9a91-579a1e96a8a3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"exec(pi)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4194e40c-04ab-4940-9d64-b4ad37c5bb40", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", |
||||
"!./optimized" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "983a11fe-e24d-4c65-8269-9802c5ef3ae6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"optimize_claude(pi)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d5a766f9-3d23-4bb4-a1d4-88ec44b61ddf", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", |
||||
"!./optimized" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "c3b497b3-f569-420e-b92e-fb0f49957ce0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"python_hard = \"\"\"# Be careful to support large number sizes\n", |
||||
"\n", |
||||
"def lcg(seed, a=1664525, c=1013904223, m=2**32):\n", |
||||
" value = seed\n", |
||||
" while True:\n", |
||||
" value = (a * value + c) % m\n", |
||||
" yield value\n", |
||||
" \n", |
||||
"def max_subarray_sum(n, seed, min_val, max_val):\n", |
||||
" lcg_gen = lcg(seed)\n", |
||||
" random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n", |
||||
" max_sum = float('-inf')\n", |
||||
" for i in range(n):\n", |
||||
" current_sum = 0\n", |
||||
" for j in range(i, n):\n", |
||||
" current_sum += random_numbers[j]\n", |
||||
" if current_sum > max_sum:\n", |
||||
" max_sum = current_sum\n", |
||||
" return max_sum\n", |
||||
"\n", |
||||
"def total_max_subarray_sum(n, initial_seed, min_val, max_val):\n", |
||||
" total_sum = 0\n", |
||||
" lcg_gen = lcg(initial_seed)\n", |
||||
" for _ in range(20):\n", |
||||
" seed = next(lcg_gen)\n", |
||||
" total_sum += max_subarray_sum(n, seed, min_val, max_val)\n", |
||||
" return total_sum\n", |
||||
"\n", |
||||
"# Parameters\n", |
||||
"n = 10000 # Number of random numbers\n", |
||||
"initial_seed = 42 # Initial seed for the LCG\n", |
||||
"min_val = -10 # Minimum value of random numbers\n", |
||||
"max_val = 10 # Maximum value of random numbers\n", |
||||
"\n", |
||||
"# Timing the function\n", |
||||
"import time\n", |
||||
"start_time = time.time()\n", |
||||
"result = total_max_subarray_sum(n, initial_seed, min_val, max_val)\n", |
||||
"end_time = time.time()\n", |
||||
"\n", |
||||
"print(\"Total Maximum Subarray Sum (20 runs):\", result)\n", |
||||
"print(\"Execution Time: {:.6f} seconds\".format(end_time - start_time))\n", |
||||
"\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "dab5e4bc-276c-4555-bd4c-12c699d5e899", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"exec(python_hard)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e8d24ed5-2c15-4f55-80e7-13a3952b3cb8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"optimize_gpt(python_hard)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e0b3d073-88a2-40b2-831c-6f0c345c256f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", |
||||
"!./optimized" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e9305446-1d0c-4b51-866a-b8c1e299bf5c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"optimize_claude(python_hard)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0c181036-8193-4fdd-aef3-fc513b218d43", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", |
||||
"!./optimized" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0be9f47d-5213-4700-b0e2-d444c7c738c0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_gpt(python): \n", |
||||
" stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python), stream=True)\n", |
||||
" reply = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" fragment = chunk.choices[0].delta.content or \"\"\n", |
||||
" reply += fragment\n", |
||||
" yield reply.replace('```cpp\\n','').replace('```','')" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "8669f56b-8314-4582-a167-78842caea131", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_claude(python):\n", |
||||
" result = claude.messages.stream(\n", |
||||
" model=CLAUDE_MODEL,\n", |
||||
" max_tokens=2000,\n", |
||||
" system=system_message,\n", |
||||
" messages=[{\"role\": \"user\", \"content\": user_prompt_for(python)}],\n", |
||||
" )\n", |
||||
" reply = \"\"\n", |
||||
" with result as stream:\n", |
||||
" for text in stream.text_stream:\n", |
||||
" reply += text\n", |
||||
" yield reply.replace('```cpp\\n','').replace('```','')" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a9b6938f-89ef-4998-a334-2f6c042a2da4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_gemini(python):\n", |
||||
" gemini = genai.GenerativeModel(\n", |
||||
" model_name = GEMINI_MODEL,\n", |
||||
" system_instruction=system_message\n", |
||||
" )\n", |
||||
" response = gemini.generate_content(\n", |
||||
" user_prompt_for(python),\n", |
||||
" stream=True\n", |
||||
" )\n", |
||||
" reply = \"\"\n", |
||||
" for chunk in response:\n", |
||||
" reply += chunk.text\n", |
||||
" yield reply.replace('```cpp\\n','').replace('```','')" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "2f1ae8f5-16c8-40a0-aa18-63b617df078d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def optimize(python, model):\n", |
||||
" if model==\"GPT\":\n", |
||||
" result = stream_gpt(python)\n", |
||||
" elif model==\"Claude\":\n", |
||||
" result = stream_claude(python)\n", |
||||
" elif model==\"Gemini\":\n", |
||||
" result= stream_gemini(python)\n", |
||||
" else:\n", |
||||
" raise ValueError(\"Unknown model\")\n", |
||||
" for stream_so_far in result:\n", |
||||
" yield stream_so_far " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f1ddb38e-6b0a-4c37-baa4-ace0b7de887a", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"with gr.Blocks() as ui:\n", |
||||
" with gr.Row():\n", |
||||
" python = gr.Textbox(label=\"Python code:\", lines=10, value=python_hard)\n", |
||||
" cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n", |
||||
" with gr.Row():\n", |
||||
" model = gr.Dropdown([\"GPT\", \"Claude\",\"Gemini\"], label=\"Select model\", value=\"GPT\")\n", |
||||
" convert = gr.Button(\"Convert code\")\n", |
||||
"\n", |
||||
" convert.click(optimize, inputs=[python, model], outputs=[cpp])\n", |
||||
"\n", |
||||
"ui.launch(inbrowser=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "19bf2bff-a822-4009-a539-f003b1651383", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def execute_python(code):\n", |
||||
" try:\n", |
||||
" output = io.StringIO()\n", |
||||
" sys.stdout = output\n", |
||||
" exec(code)\n", |
||||
" finally:\n", |
||||
" sys.stdout = sys.__stdout__\n", |
||||
" return output.getvalue()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "77f3ab5d-fcfb-4d3f-8728-9cacbf833ea6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def execute_cpp(code):\n", |
||||
" write_output(code)\n", |
||||
" try:\n", |
||||
" compile_result = subprocess.run(compiler_cmd[2], check=True, text=True, capture_output=True)\n", |
||||
" run_cmd = [\"./optimized\"]\n", |
||||
" run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True)\n", |
||||
" return run_result.stdout\n", |
||||
" except subprocess.CalledProcessError as e:\n", |
||||
" return f\"An error occurred:\\n{e.stderr}\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "9a2274f1-d03b-42c0-8dcc-4ce159b18442", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"css = \"\"\"\n", |
||||
".python {background-color: #306998;}\n", |
||||
".cpp {background-color: #050;}\n", |
||||
"\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f1303932-160c-424b-97a8-d28c816721b2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"with gr.Blocks(css=css) as ui:\n", |
||||
" gr.Markdown(\"## Convert code from Python to C++\")\n", |
||||
" with gr.Row():\n", |
||||
" python = gr.Textbox(label=\"Python code:\", value=python_hard, lines=10)\n", |
||||
" cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n", |
||||
" with gr.Row():\n", |
||||
" model = gr.Dropdown([\"GPT\", \"Claude\",\"Gemini\"], label=\"Select model\", value=\"GPT\")\n", |
||||
" with gr.Row():\n", |
||||
" convert = gr.Button(\"Convert code\")\n", |
||||
" with gr.Row():\n", |
||||
" python_run = gr.Button(\"Run Python\")\n", |
||||
" cpp_run = gr.Button(\"Run C++\")\n", |
||||
" with gr.Row():\n", |
||||
" python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n", |
||||
" cpp_out = gr.TextArea(label=\"C++ result:\", elem_classes=[\"cpp\"])\n", |
||||
"\n", |
||||
" convert.click(optimize, inputs=[python, model], outputs=[cpp])\n", |
||||
" python_run.click(execute_python, inputs=[python], outputs=[python_out])\n", |
||||
" cpp_run.click(execute_cpp, inputs=[cpp], outputs=[cpp_out])\n", |
||||
"\n", |
||||
"ui.launch(inbrowser=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bb8c5b4e-ec51-4f21-b3f8-6aa94fede86d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"from huggingface_hub import login, InferenceClient\n", |
||||
"from transformers import AutoTokenizer" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "13347633-4606-4e38-9927-80c39e65c1f1", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"hf_token = os.environ['HF_TOKEN']\n", |
||||
"login(hf_token, add_to_git_credential=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "ef60a4df-6267-4ebd-8eed-dcb917af0a5e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"code_qwen = \"Qwen/CodeQwen1.5-7B-Chat\"\n", |
||||
"code_gemma = \"google/codegemma-7b-it\"\n", |
||||
"CODE_QWEN_URL = \"https://h1vdol7jxhje3mpn.us-east-1.aws.endpoints.huggingface.cloud\"\n", |
||||
"CODE_GEMMA_URL = \"https://c5hggiyqachmgnqg.us-east-1.aws.endpoints.huggingface.cloud\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "695ce389-a903-4533-a2f1-cd9e2a6af8f2", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"tokenizer = AutoTokenizer.from_pretrained(code_qwen)\n", |
||||
"messages = messages_for(pi)\n", |
||||
"text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d4548e96-0b32-4793-bdd6-1b072c2f26ab", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"print(text)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bb2a126b-09e7-4966-bc97-0ef5c2cc7896", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"client = InferenceClient(CODE_QWEN_URL, token=hf_token)\n", |
||||
"stream = client.text_generation(text, stream=True, details=True, max_new_tokens=3000)\n", |
||||
"for r in stream:\n", |
||||
" print(r.token.text, end = \"\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "127a52e5-ad85-42b7-a0f5-9afda5efe090", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_code_qwen(python):\n", |
||||
" tokenizer = AutoTokenizer.from_pretrained(code_qwen)\n", |
||||
" messages = messages_for(python)\n", |
||||
" text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", |
||||
" client = InferenceClient(CODE_QWEN_URL, token=hf_token)\n", |
||||
" stream = client.text_generation(text, stream=True, details=True, max_new_tokens=3000)\n", |
||||
" result = \"\"\n", |
||||
" for r in stream:\n", |
||||
" result += r.token.text\n", |
||||
" yield result " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "a82387d1-7651-4923-995b-fe18356fcaa6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def optimize(python, model):\n", |
||||
" if model==\"GPT\":\n", |
||||
" result = stream_gpt(python)\n", |
||||
" elif model==\"Claude\":\n", |
||||
" result = stream_claude(python)\n", |
||||
" elif model==\"Gemini\":\n", |
||||
" result= stream_gemini(python)\n", |
||||
" elif model==\"CodeQwen\":\n", |
||||
" result = stream_code_qwen(python)\n", |
||||
" else:\n", |
||||
" raise ValueError(\"Unknown model\")\n", |
||||
" for stream_so_far in result:\n", |
||||
" yield stream_so_far " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "4b0a6a97-5b8a-4a9b-8ee0-7561e0ced673", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../../thankyou.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h2 style=\"color:#090;\">Thank you to @CloudLlama for an amazing contribution</h2>\n", |
||||
" <span style=\"color:#090;\">\n", |
||||
" A student has contributed a chunk of code to improve this, in the next 2 cells. You can now select which Python porgram to run,\n", |
||||
" and a compiler is automatically selected that will work on PC, Windows and Mac. Massive thank you @CloudLlama!\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4ba311ec-c16a-4fe0-946b-4b940704cf65", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def select_sample_program(sample_program):\n", |
||||
" if sample_program==\"pi\":\n", |
||||
" return pi\n", |
||||
" elif sample_program==\"python_hard\":\n", |
||||
" return python_hard\n", |
||||
" else:\n", |
||||
" return \"Type your Python program here\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e42286bc-085c-45dc-b101-234308e58269", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"import platform\n", |
||||
"\n", |
||||
"VISUAL_STUDIO_2022_TOOLS = \"C:\\\\Program Files\\\\Microsoft Visual Studio\\\\2022\\\\Community\\\\Common7\\Tools\\\\VsDevCmd.bat\"\n", |
||||
"VISUAL_STUDIO_2019_TOOLS = \"C:\\\\Program Files (x86)\\\\Microsoft Visual Studio\\\\2019\\\\BuildTools\\\\Common7\\\\Tools\\\\VsDevCmd.bat\"\n", |
||||
"\n", |
||||
"simple_cpp = \"\"\"\n", |
||||
"#include <iostream>\n", |
||||
"\n", |
||||
"int main() {\n", |
||||
" std::cout << \"Hello\";\n", |
||||
" return 0;\n", |
||||
"}\n", |
||||
"\"\"\"\n", |
||||
"\n", |
||||
"def run_cmd(command_to_run):\n", |
||||
" try:\n", |
||||
" run_result = subprocess.run(command_to_run, check=True, text=True, capture_output=True)\n", |
||||
" return run_result.stdout if run_result.stdout else \"SUCCESS\"\n", |
||||
" except:\n", |
||||
" return \"\"\n", |
||||
"\n", |
||||
"def c_compiler_cmd(filename_base):\n", |
||||
" my_platform = platform.system()\n", |
||||
" my_compiler = []\n", |
||||
"\n", |
||||
" try:\n", |
||||
" with open(\"simple.cpp\", \"w\") as f:\n", |
||||
" f.write(simple_cpp)\n", |
||||
" \n", |
||||
" if my_platform == \"Windows\":\n", |
||||
" if os.path.isfile(VISUAL_STUDIO_2022_TOOLS):\n", |
||||
" if os.path.isfile(\"./simple.exe\"):\n", |
||||
" os.remove(\"./simple.exe\")\n", |
||||
" compile_cmd = [\"cmd\", \"/c\", VISUAL_STUDIO_2022_TOOLS, \"&\", \"cl\", \"simple.cpp\"]\n", |
||||
" if run_cmd(compile_cmd):\n", |
||||
" if run_cmd([\"./simple.exe\"]) == \"Hello\":\n", |
||||
" my_compiler = [\"Windows\", \"Visual Studio 2022\", [\"cmd\", \"/c\", VISUAL_STUDIO_2022_TOOLS, \"&\", \"cl\", f\"{filename_base}.cpp\"]]\n", |
||||
" \n", |
||||
" if not my_compiler:\n", |
||||
" if os.path.isfile(VISUAL_STUDIO_2019_TOOLS):\n", |
||||
" if os.path.isfile(\"./simple.exe\"):\n", |
||||
" os.remove(\"./simple.exe\")\n", |
||||
" compile_cmd = [\"cmd\", \"/c\", VISUAL_STUDIO_2019_TOOLS, \"&\", \"cl\", \"simple.cpp\"]\n", |
||||
" if run_cmd(compile_cmd):\n", |
||||
" if run_cmd([\"./simple.exe\"]) == \"Hello\":\n", |
||||
" my_compiler = [\"Windows\", \"Visual Studio 2019\", [\"cmd\", \"/c\", VISUAL_STUDIO_2019_TOOLS, \"&\", \"cl\", f\"{filename_base}.cpp\"]]\n", |
||||
" \n", |
||||
" if not my_compiler:\n", |
||||
" my_compiler=[my_platform, \"Unavailable\", []]\n", |
||||
" \n", |
||||
" elif my_platform == \"Linux\":\n", |
||||
" if os.path.isfile(\"./simple\"):\n", |
||||
" os.remove(\"./simple\")\n", |
||||
" compile_cmd = [\"g++\", \"simple.cpp\", \"-o\", \"simple\"]\n", |
||||
" if run_cmd(compile_cmd):\n", |
||||
" if run_cmd([\"./simple\"]) == \"Hello\":\n", |
||||
" my_compiler = [\"Linux\", \"GCC (g++)\", [\"g++\", f\"{filename_base}.cpp\", \"-o\", f\"{filename_base}\" ]]\n", |
||||
" \n", |
||||
" if not my_compiler:\n", |
||||
" if os.path.isfile(\"./simple\"):\n", |
||||
" os.remove(\"./simple\")\n", |
||||
" compile_cmd = [\"clang++\", \"simple.cpp\", \"-o\", \"simple\"]\n", |
||||
" if run_cmd(compile_cmd):\n", |
||||
" if run_cmd([\"./simple\"]) == \"Hello\":\n", |
||||
" my_compiler = [\"Linux\", \"Clang++\", [\"clang++\", f\"{filename_base}.cpp\", \"-o\", f\"{filename_base}\"]]\n", |
||||
" \n", |
||||
" if not my_compiler:\n", |
||||
" my_compiler=[my_platform, \"Unavailable\", []]\n", |
||||
" \n", |
||||
" elif my_platform == \"Darwin\":\n", |
||||
" if os.path.isfile(\"./simple\"):\n", |
||||
" os.remove(\"./simple\")\n", |
||||
" compile_cmd = [\"clang++\", \"-Ofast\", \"-std=c++17\", \"-march=armv8.5-a\", \"-mtune=apple-m1\", \"-mcpu=apple-m1\", \"-o\", \"simple\", \"simple.cpp\"]\n", |
||||
" if run_cmd(compile_cmd):\n", |
||||
" if run_cmd([\"./simple\"]) == \"Hello\":\n", |
||||
" my_compiler = [\"Macintosh\", \"Clang++\", [\"clang++\", \"-Ofast\", \"-std=c++17\", \"-march=armv8.5-a\", \"-mtune=apple-m1\", \"-mcpu=apple-m1\", \"-o\", f\"{filename_base}\", f\"{filename_base}.cpp\"]]\n", |
||||
" \n", |
||||
" if not my_compiler:\n", |
||||
" my_compiler=[my_platform, \"Unavailable\", []]\n", |
||||
" except:\n", |
||||
" my_compiler=[my_platform, \"Unavailable\", []]\n", |
||||
" \n", |
||||
" if my_compiler:\n", |
||||
" return my_compiler\n", |
||||
" else:\n", |
||||
" return [\"Unknown\", \"Unavailable\", []]\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "f9ca2e6f-60c1-4e5f-b570-63c75b2d189b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"compiler_cmd = c_compiler_cmd(\"optimized\")\n", |
||||
"\n", |
||||
"with gr.Blocks(css=css) as ui:\n", |
||||
" gr.Markdown(\"## Convert code from Python to C++\")\n", |
||||
" with gr.Row():\n", |
||||
" python = gr.Textbox(label=\"Python code:\", value=python_hard, lines=10)\n", |
||||
" cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n", |
||||
" with gr.Row():\n", |
||||
" with gr.Column():\n", |
||||
" sample_program = gr.Radio([\"pi\", \"python_hard\"], label=\"Sample program\", value=\"python_hard\")\n", |
||||
" model = gr.Dropdown([\"GPT\", \"Claude\", \"Gemini\", \"CodeQwen\"], label=\"Select model\", value=\"GPT\")\n", |
||||
" with gr.Column():\n", |
||||
" architecture = gr.Radio([compiler_cmd[0]], label=\"Architecture\", interactive=False, value=compiler_cmd[0])\n", |
||||
" compiler = gr.Radio([compiler_cmd[1]], label=\"Compiler\", interactive=False, value=compiler_cmd[1])\n", |
||||
" with gr.Row():\n", |
||||
" convert = gr.Button(\"Convert code\")\n", |
||||
" with gr.Row():\n", |
||||
" python_run = gr.Button(\"Run Python\")\n", |
||||
" if not compiler_cmd[1] == \"Unavailable\":\n", |
||||
" cpp_run = gr.Button(\"Run C++\")\n", |
||||
" else:\n", |
||||
" cpp_run = gr.Button(\"No compiler to run C++\", interactive=False)\n", |
||||
" with gr.Row():\n", |
||||
" python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n", |
||||
" cpp_out = gr.TextArea(label=\"C++ result:\", elem_classes=[\"cpp\"])\n", |
||||
"\n", |
||||
" sample_program.change(select_sample_program, inputs=[sample_program], outputs=[python])\n", |
||||
" convert.click(optimize, inputs=[python, model], outputs=[cpp])\n", |
||||
" python_run.click(execute_python, inputs=[python], outputs=[python_out])\n", |
||||
" cpp_run.click(execute_cpp, inputs=[cpp], outputs=[cpp_out])\n", |
||||
"\n", |
||||
"ui.launch(inbrowser=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "9d0ad093-425b-488e-8c3f-67f729dd9c06", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
File diff suppressed because one or more lines are too long
@ -0,0 +1,925 @@
|
||||
{ |
||||
"cells": [ |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"# Code Generator\n", |
||||
"\n", |
||||
"The requirement: use an Open Source model to generate high performance C++ code from Python code\n", |
||||
"\n", |
||||
"To replicate this, you'll need to set up a HuggingFace endpoint as I do in the video. It's simple to do, and it's quite satisfying to see the results!\n", |
||||
"\n", |
||||
"It's also an important part of your learning; this is the first example of deploying an open source model to be behind an API. We'll return to this in Week 8, but this should plant a seed in your mind for what's involved in moving open source models into production.\n", |
||||
"\n", |
||||
"Added the use of inference providers that was introduced recently by Hugging Face to convert the code.\n", |
||||
"Improved the user prompt to include algorithic efficeiny and performance optimization.\n", |
||||
"\n", |
||||
"Added Java as a conversion option.\n", |
||||
"\n", |
||||
"Note: C++ commands work on windows environment.\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "markdown", |
||||
"id": "22e1567b-33fd-49e7-866e-4b635d15715a", |
||||
"metadata": {}, |
||||
"source": [ |
||||
"<table style=\"margin: 0; text-align: left;\">\n", |
||||
" <tr>\n", |
||||
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||
" </td>\n", |
||||
" <td>\n", |
||||
" <h1 style=\"color:#900;\">Important - Pause Endpoints when not in use</h1>\n", |
||||
" <span style=\"color:#900;\">\n", |
||||
" If you do decide to use HuggingFace endpoints for this project, you should stop or pause the endpoints when you are done to avoid accruing unnecessary running cost. The costs are very low as long as you only run the endpoint when you're using it. Navigate to the HuggingFace endpoint UI <a href=\"https://ui.endpoints.huggingface.co/\">here,</a> open your endpoint, and click Pause to put it on pause so you no longer pay for it. \n", |
||||
"Many thanks to student John L. for raising this.\n", |
||||
"<br/><br/>\n", |
||||
"In week 8 we will use Modal instead of HuggingFace endpoints; with Modal you only pay for the time that you use it and you should get free credits.\n", |
||||
" </span>\n", |
||||
" </td>\n", |
||||
" </tr>\n", |
||||
"</table>" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 231, |
||||
"id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# imports\n", |
||||
"\n", |
||||
"import os\n", |
||||
"import io\n", |
||||
"import sys\n", |
||||
"import json\n", |
||||
"import requests\n", |
||||
"from dotenv import load_dotenv\n", |
||||
"from openai import OpenAI\n", |
||||
"import google.generativeai\n", |
||||
"import anthropic\n", |
||||
"from IPython.display import Markdown, display, update_display\n", |
||||
"import gradio as gr\n", |
||||
"import subprocess, re" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 198, |
||||
"id": "4f672e1c-87e9-4865-b760-370fa605e614", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# environment\n", |
||||
"\n", |
||||
"load_dotenv()\n", |
||||
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", |
||||
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n", |
||||
"os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 199, |
||||
"id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# initialize\n", |
||||
"\n", |
||||
"openai = OpenAI()\n", |
||||
"claude = anthropic.Anthropic()\n", |
||||
"OPENAI_MODEL = \"gpt-4o\"\n", |
||||
"CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 200, |
||||
"id": "2db60a72-d098-42ca-8ce2-1e037c86b718", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def system_prompt_for(language: str) -> str:\n", |
||||
" system_prompt = (\n", |
||||
" f\"You are an assistant that reimplements Python code in high performance {language.upper()} for an Windows intel i7.\"\n", |
||||
" f\"Respond only with {language.upper()} code; use comments sparingly and do not provide any explanation other than occasional comments.\"\n", |
||||
" f\"The {language.upper()} response needs to produce an identical output in the fastest possible time. Keep implementations of random number generators identical so that results match exactly.\"\n", |
||||
" )\n", |
||||
" return system_prompt" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 243, |
||||
"id": "70583432-e851-40d1-a219-2fb32b830dc8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"#updated the original prompt to include algorithic efficeiny and performance optimization\n", |
||||
"def user_prompt_for(python: str, language: str) -> str:\n", |
||||
" if language.lower() not in {\"cpp\", \"java\"}:\n", |
||||
" raise ValueError(\"Unsupported language. Please choose 'C++' or 'Java'.\")\n", |
||||
" \n", |
||||
" optimization_notes = {\n", |
||||
" \"cpp\": (\n", |
||||
" \"- Use `int64_t` instead of `int` where necessary to prevent overflows.\\n\"\n", |
||||
" \"- Ensure random number generation in C++ matches Python's output as closely as possible.\\n\"\n", |
||||
" \"- Avoid undefined behavior, such as bit shifts that exceed type width (`1UL << 32` is incorrect for `uint32_t`).\\n\"\n", |
||||
" \"- Utilize `std::vector` for dynamic arrays and prefer preallocation for efficiency.\\n\"\n", |
||||
" \"- Consider `std::array` or `std::span` when fixed-size arrays are sufficient.\\n\"\n", |
||||
" \"- Optimize with **SIMD**, cache-friendly structures, and memory alignment where necessary.\\n\"\n", |
||||
" ),\n", |
||||
" \"java\": (\n", |
||||
" \"- Use `long` instead of `int` where necessary to prevent overflows.\\n\"\n", |
||||
" \"- Ensure random number generation in Java matches Python's output as closely as possible.\\n\"\n", |
||||
" \"- Use `ArrayList` instead of primitive arrays if dynamic resizing is needed.\\n\"\n", |
||||
" \"- Utilize `BigInteger` if handling large numbers that could exceed `long`.\\n\"\n", |
||||
" \"- Optimize with **parallel streams** (`IntStream.parallel()`) and **efficient data structures** (`HashMap`, `LinkedList`, etc.).\\n\"\n", |
||||
" )\n", |
||||
" }\n", |
||||
"\n", |
||||
" user_prompt = (\n", |
||||
" f\"First, analyze the given Python code to understand its core purpose and algorithmic approach. \"\n", |
||||
" f\"Then, implement a {language} solution that achieves the same output while prioritizing:\\n\"\n", |
||||
" \"1. **Algorithmic Efficiency** - Optimize time and space complexity, even if it means using a different approach.\\n\"\n", |
||||
" \"2. **Numerical Correctness** - Prevent integer overflows, use appropriate data types (`long`, `BigInteger`, etc.), \"\n", |
||||
" \"and ensure correct handling of edge cases.\\n\"\n", |
||||
" \"3. **Performance Optimization** - Utilize language-specific features for efficiency.\\n\\n\"\n", |
||||
" \n", |
||||
" \"### **Important Notes:**\\n\"\n", |
||||
" + optimization_notes[language.lower()] +\n", |
||||
" \"\\n### **Expected Response:**\\n\"\n", |
||||
" f\"Respond **only with {language} code**, including all necessary imports and ensuring the output matches the Python version exactly.\\n\\n\"\n", |
||||
" \n", |
||||
" \"Here's the Python code to analyze and optimize:\\n\\n\"\n", |
||||
" + python\n", |
||||
" )\n", |
||||
" \n", |
||||
" return user_prompt\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 202, |
||||
"id": "c6190659-f54c-4951-bef4-4960f8e51cc4", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def messages_for(python, language=\"cpp\"):\n", |
||||
" return [\n", |
||||
" {\"role\": \"system\", \"content\": system_prompt_for(language)},\n", |
||||
" {\"role\": \"user\", \"content\": user_prompt_for(python, language)}\n", |
||||
" ]" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 241, |
||||
"id": "71e1ba8c-5b05-4726-a9f3-8d8c6257350b", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# write to a file called optimized.cpp\n", |
||||
"\n", |
||||
"def write_output(code, file_name):\n", |
||||
" with open(file_name, \"w\") as f:\n", |
||||
" f.write(code)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 226, |
||||
"id": "e7d2fea8-74c6-4421-8f1e-0e76d5b201b9", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def optimize_gpt(python, language=\"cpp\"): \n", |
||||
" stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python, language), stream=True)\n", |
||||
" reply = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" fragment = chunk.choices[0].delta.content or \"\"\n", |
||||
" reply += fragment\n", |
||||
" print(fragment, end='', flush=True)\n", |
||||
" file_name= f\"optimized.{language}\"\n", |
||||
" if language == \"java\":\n", |
||||
" # Extract class name from Java code\n", |
||||
" match = re.search(r\"\\b(public\\s+)?class\\s+(\\w+)\", reply)\n", |
||||
" class_name = match.group(2) if match else \"OptimizedJava\"\n", |
||||
" file_name = f\"{class_name}.java\"\n", |
||||
" else:\n", |
||||
" file_name = f\"optimized.{language}\"\n", |
||||
" write_output(reply, file_name)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 227, |
||||
"id": "7cd84ad8-d55c-4fe0-9eeb-1895c95c4a9d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def optimize_claude(python, language=\"cpp\"):\n", |
||||
" result = claude.messages.stream(\n", |
||||
" model=CLAUDE_MODEL,\n", |
||||
" max_tokens=2000,\n", |
||||
" system=system_message,\n", |
||||
" messages=[{\"role\": \"user\", \"content\": user_prompt_for(python, language)}],\n", |
||||
" )\n", |
||||
" reply = \"\"\n", |
||||
" with result as stream:\n", |
||||
" for text in stream.text_stream:\n", |
||||
" reply += text\n", |
||||
" print(text, end=\"\", flush=True)\n", |
||||
" if language == \"java\":\n", |
||||
" # Extract class name from Java code\n", |
||||
" match = re.search(r\"\\b(public\\s+)?class\\s+(\\w+)\", reply)\n", |
||||
" class_name = match.group(2) if match else \"OptimizedJava\"\n", |
||||
" file_name = f\"{class_name}.java\"\n", |
||||
" else:\n", |
||||
" file_name = f\"optimized.{language}\"\n", |
||||
" write_output(reply, file_name)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 206, |
||||
"id": "a1cbb778-fa57-43de-b04b-ed523f396c38", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"pi = \"\"\"\n", |
||||
"import time\n", |
||||
"\n", |
||||
"def calculate(iterations, param1, param2):\n", |
||||
" result = 1.0\n", |
||||
" for i in range(1, iterations+1):\n", |
||||
" j = i * param1 - param2\n", |
||||
" result -= (1/j)\n", |
||||
" j = i * param1 + param2\n", |
||||
" result += (1/j)\n", |
||||
" return result\n", |
||||
"\n", |
||||
"start_time = time.time()\n", |
||||
"result = calculate(100_000_000, 4, 1) * 4\n", |
||||
"end_time = time.time()\n", |
||||
"\n", |
||||
"print(f\"Result: {result:.12f}\")\n", |
||||
"print(f\"Execution Time: {(end_time - start_time):.6f} seconds\")\n", |
||||
"\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 59, |
||||
"id": "7fe1cd4b-d2c5-4303-afed-2115a3fef200", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"exec(pi)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 91, |
||||
"id": "105db6f9-343c-491d-8e44-3a5328b81719", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"optimize_gpt(pi, \"java\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "bf26ee95-0c77-491d-9a91-579a1e96a8a3", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"exec(pi)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "4194e40c-04ab-4940-9d64-b4ad37c5bb40", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n", |
||||
"!optimized.exe" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "983a11fe-e24d-4c65-8269-9802c5ef3ae6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"optimize_claude(pi)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "d5a766f9-3d23-4bb4-a1d4-88ec44b61ddf", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n", |
||||
"!optimized.exe" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 207, |
||||
"id": "c3b497b3-f569-420e-b92e-fb0f49957ce0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"python_hard = \"\"\"# Be careful to support large number sizes\n", |
||||
"\n", |
||||
"def lcg(seed, a=1664525, c=1013904223, m=2**32):\n", |
||||
" value = seed\n", |
||||
" while True:\n", |
||||
" value = (a * value + c) % m\n", |
||||
" yield value\n", |
||||
" \n", |
||||
"def max_subarray_sum(n, seed, min_val, max_val):\n", |
||||
" lcg_gen = lcg(seed)\n", |
||||
" random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n", |
||||
" max_sum = float('-inf')\n", |
||||
" for i in range(n):\n", |
||||
" current_sum = 0\n", |
||||
" for j in range(i, n):\n", |
||||
" current_sum += random_numbers[j]\n", |
||||
" if current_sum > max_sum:\n", |
||||
" max_sum = current_sum\n", |
||||
" return max_sum\n", |
||||
"\n", |
||||
"def total_max_subarray_sum(n, initial_seed, min_val, max_val):\n", |
||||
" total_sum = 0\n", |
||||
" lcg_gen = lcg(initial_seed)\n", |
||||
" for _ in range(20):\n", |
||||
" seed = next(lcg_gen)\n", |
||||
" total_sum += max_subarray_sum(n, seed, min_val, max_val)\n", |
||||
" return total_sum\n", |
||||
"\n", |
||||
"# Parameters\n", |
||||
"n = 10000 # Number of random numbers\n", |
||||
"initial_seed = 42 # Initial seed for the LCG\n", |
||||
"min_val = -10 # Minimum value of random numbers\n", |
||||
"max_val = 10 # Maximum value of random numbers\n", |
||||
"\n", |
||||
"# Timing the function\n", |
||||
"import time\n", |
||||
"start_time = time.time()\n", |
||||
"result = total_max_subarray_sum(n, initial_seed, min_val, max_val)\n", |
||||
"end_time = time.time()\n", |
||||
"\n", |
||||
"print(\"Total Maximum Subarray Sum (20 runs):\", result)\n", |
||||
"print(\"Execution Time: {:.6f} seconds\".format(end_time - start_time))\n", |
||||
"\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 23, |
||||
"id": "dab5e4bc-276c-4555-bd4c-12c699d5e899", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"exec(python_hard)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e8d24ed5-2c15-4f55-80e7-13a3952b3cb8", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"optimize_gpt(python_hard)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e0b3d073-88a2-40b2-831c-6f0c345c256f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n", |
||||
"!optimized.exe" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "e9305446-1d0c-4b51-866a-b8c1e299bf5c", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"optimize_claude(python_hard)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "0c181036-8193-4fdd-aef3-fc513b218d43", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n", |
||||
"!optimized.exe" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 240, |
||||
"id": "0be9f47d-5213-4700-b0e2-d444c7c738c0", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_gpt(python, language=\"cpp\"): \n", |
||||
" stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python, language), stream=True)\n", |
||||
" reply = \"\"\n", |
||||
" code_block = f\"```{language}\\n\"\n", |
||||
" for chunk in stream:\n", |
||||
" fragment = chunk.choices[0].delta.content or \"\"\n", |
||||
" reply += fragment\n", |
||||
" cleaned_reply = reply.replace(code_block,'').replace('```','')\n", |
||||
" yield cleaned_reply" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 239, |
||||
"id": "8669f56b-8314-4582-a167-78842caea131", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def stream_claude(python, language=\"cpp\"):\n", |
||||
" result = claude.messages.stream(\n", |
||||
" model=CLAUDE_MODEL,\n", |
||||
" max_tokens=2000,\n", |
||||
" system=system_message,\n", |
||||
" messages=[{\"role\": \"user\", \"content\": user_prompt_for(python, language)}],\n", |
||||
" )\n", |
||||
" reply = \"\"\n", |
||||
" code_block = f\"```{language}\\n\"\n", |
||||
" with result as stream:\n", |
||||
" for text in stream.text_stream:\n", |
||||
" reply += text\n", |
||||
" cleaned_reply = reply.replace(code_block,'').replace('```','')\n", |
||||
" yield cleaned_reply" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 186, |
||||
"id": "2f1ae8f5-16c8-40a0-aa18-63b617df078d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def optimize(python, model):\n", |
||||
" if model==\"GPT\":\n", |
||||
" result = stream_gpt(python)\n", |
||||
" elif model==\"Claude\":\n", |
||||
" result = stream_claude(python)\n", |
||||
" else:\n", |
||||
" raise ValueError(\"Unknown model\")\n", |
||||
" for stream_so_far in result:\n", |
||||
" yield stream_so_far " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 189, |
||||
"id": "f1ddb38e-6b0a-4c37-baa4-ace0b7de887a", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"http://127.0.0.1:7888/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 189, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"source": [ |
||||
"with gr.Blocks() as ui:\n", |
||||
" with gr.Row():\n", |
||||
" python = gr.Textbox(label=\"Python code:\", lines=10, value=python_hard)\n", |
||||
" cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n", |
||||
" with gr.Row():\n", |
||||
" model = gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")\n", |
||||
" convert = gr.Button(\"Convert code\")\n", |
||||
"\n", |
||||
" convert.click(optimize, inputs=[python, model], outputs=[cpp])\n", |
||||
"\n", |
||||
"ui.launch(inbrowser=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 210, |
||||
"id": "19bf2bff-a822-4009-a539-f003b1651383", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def execute_python(code):\n", |
||||
" try:\n", |
||||
" output = io.StringIO()\n", |
||||
" sys.stdout = output\n", |
||||
" exec(code)\n", |
||||
" finally:\n", |
||||
" sys.stdout = sys.__stdout__\n", |
||||
" return output.getvalue()" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 211, |
||||
"id": "9a2274f1-d03b-42c0-8dcc-4ce159b18442", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"css = \"\"\"\n", |
||||
".python {background-color: #306998;}\n", |
||||
".cpp {background-color: #050;}\n", |
||||
".java {background-color: #306775;}\n", |
||||
"\"\"\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 97, |
||||
"id": "f1303932-160c-424b-97a8-d28c816721b2", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"http://127.0.0.1:7868/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 97, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"source": [ |
||||
"with gr.Blocks(css=css) as ui:\n", |
||||
" gr.Markdown(\"## Convert code from Python to C++\")\n", |
||||
" with gr.Row():\n", |
||||
" python = gr.Textbox(label=\"Python code:\", value=python_hard, lines=10)\n", |
||||
" cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n", |
||||
" with gr.Row():\n", |
||||
" model = gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")\n", |
||||
" with gr.Row():\n", |
||||
" convert = gr.Button(\"Convert code\")\n", |
||||
" with gr.Row():\n", |
||||
" python_run = gr.Button(\"Run Python\")\n", |
||||
" cpp_run = gr.Button(\"Run C++\")\n", |
||||
" with gr.Row():\n", |
||||
" python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n", |
||||
" cpp_out = gr.TextArea(label=\"C++ result:\", elem_classes=[\"cpp\"])\n", |
||||
"\n", |
||||
" convert.click(optimize, inputs=[python, model], outputs=[cpp])\n", |
||||
" python_run.click(execute_python, inputs=[python], outputs=[python_out])\n", |
||||
" cpp_run.click(execute_cpp, inputs=[cpp], outputs=[cpp_out])\n", |
||||
"\n", |
||||
"ui.launch(inbrowser=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 191, |
||||
"id": "bb8c5b4e-ec51-4f21-b3f8-6aa94fede86d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"from huggingface_hub import login, InferenceClient" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 117, |
||||
"id": "13347633-4606-4e38-9927-80c39e65c1f1", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stderr", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"hf_token = os.environ['HF_TOKEN']\n", |
||||
"login(hf_token)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 118, |
||||
"id": "ef60a4df-6267-4ebd-8eed-dcb917af0a5e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"code_qwen = \"Qwen/CodeQwen1.5-7B-Chat\"\n", |
||||
"code_gemma = \"google/codegemma-7b-it\"\n", |
||||
"messages=messages_for(pi)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 119, |
||||
"id": "3825d77a-03c6-42b2-89bc-ccbcb1585740", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"ename": "HfHubHTTPError", |
||||
"evalue": "402 Client Error: Payment Required for url: https://huggingface.co/api/inference-proxy/sambanova/v1/chat/completions (Request ID: Root=1-67afb729-1eb9aff1704314144ef14e59;2df843ad-b7d2-4145-bb7b-1cfd94ae19ef)\n\nYou have exceeded your monthly included credits for Inference Endpoints. Subscribe to PRO to get 20x more monthly allowance.", |
||||
"output_type": "error", |
||||
"traceback": [ |
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", |
||||
"\u001b[1;31mHTTPError\u001b[0m Traceback (most recent call last)", |
||||
"File \u001b[1;32m~\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\utils\\_http.py:406\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[1;34m(response, endpoint_name)\u001b[0m\n\u001b[0;32m 405\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 406\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 407\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n", |
||||
"File \u001b[1;32m~\\anaconda3\\envs\\llms\\Lib\\site-packages\\requests\\models.py:1024\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1023\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[1;32m-> 1024\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n", |
||||
"\u001b[1;31mHTTPError\u001b[0m: 402 Client Error: Payment Required for url: https://huggingface.co/api/inference-proxy/sambanova/v1/chat/completions", |
||||
"\nThe above exception was the direct cause of the following exception:\n", |
||||
"\u001b[1;31mHfHubHTTPError\u001b[0m Traceback (most recent call last)", |
||||
"Cell \u001b[1;32mIn[119], line 5\u001b[0m\n\u001b[0;32m 1\u001b[0m client \u001b[38;5;241m=\u001b[39m InferenceClient(\n\u001b[0;32m 2\u001b[0m \tprovider\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msambanova\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 3\u001b[0m \tapi_key\u001b[38;5;241m=\u001b[39mhf_token\n\u001b[0;32m 4\u001b[0m )\n\u001b[1;32m----> 5\u001b[0m stream \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompletions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m\t\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mQwen/Qwen2.5-Coder-32B-Instruct\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[43m\t\u001b[49m\u001b[43mmessages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[43m\t\u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m500\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9\u001b[0m \u001b[43m\t\u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m 10\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m stream:\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28mprint\u001b[39m(chunk\u001b[38;5;241m.\u001b[39mchoices[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mdelta\u001b[38;5;241m.\u001b[39mcontent, end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", |
||||
"File \u001b[1;32m~\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\inference\\_client.py:970\u001b[0m, in \u001b[0;36mInferenceClient.chat_completion\u001b[1;34m(self, messages, model, stream, frequency_penalty, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream_options, temperature, tool_choice, tool_prompt, tools, top_logprobs, top_p)\u001b[0m\n\u001b[0;32m 943\u001b[0m parameters \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 944\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m: payload_model,\n\u001b[0;32m 945\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrequency_penalty\u001b[39m\u001b[38;5;124m\"\u001b[39m: frequency_penalty,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 961\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream_options\u001b[39m\u001b[38;5;124m\"\u001b[39m: stream_options,\n\u001b[0;32m 962\u001b[0m }\n\u001b[0;32m 963\u001b[0m request_parameters \u001b[38;5;241m=\u001b[39m provider_helper\u001b[38;5;241m.\u001b[39mprepare_request(\n\u001b[0;32m 964\u001b[0m inputs\u001b[38;5;241m=\u001b[39mmessages,\n\u001b[0;32m 965\u001b[0m parameters\u001b[38;5;241m=\u001b[39mparameters,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 968\u001b[0m api_key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoken,\n\u001b[0;32m 969\u001b[0m )\n\u001b[1;32m--> 970\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inner_post\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest_parameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 972\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stream:\n\u001b[0;32m 973\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _stream_chat_completion_response(data) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n", |
||||
"File \u001b[1;32m~\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\inference\\_client.py:327\u001b[0m, in \u001b[0;36mInferenceClient._inner_post\u001b[1;34m(self, request_parameters, stream)\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InferenceTimeoutError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInference call timed out: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrequest_parameters\u001b[38;5;241m.\u001b[39murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merror\u001b[39;00m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[0;32m 326\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 327\u001b[0m \u001b[43mhf_raise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 328\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\u001b[38;5;241m.\u001b[39miter_lines() \u001b[38;5;28;01mif\u001b[39;00m stream \u001b[38;5;28;01melse\u001b[39;00m response\u001b[38;5;241m.\u001b[39mcontent\n\u001b[0;32m 329\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m error:\n", |
||||
"File \u001b[1;32m~\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\utils\\_http.py:477\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[1;34m(response, endpoint_name)\u001b[0m\n\u001b[0;32m 473\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m _format(HfHubHTTPError, message, response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m 475\u001b[0m \u001b[38;5;66;03m# Convert `HTTPError` into a `HfHubHTTPError` to display request information\u001b[39;00m\n\u001b[0;32m 476\u001b[0m \u001b[38;5;66;03m# as well (request id and/or server error message)\u001b[39;00m\n\u001b[1;32m--> 477\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m _format(HfHubHTTPError, \u001b[38;5;28mstr\u001b[39m(e), response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n", |
||||
"\u001b[1;31mHfHubHTTPError\u001b[0m: 402 Client Error: Payment Required for url: https://huggingface.co/api/inference-proxy/sambanova/v1/chat/completions (Request ID: Root=1-67afb729-1eb9aff1704314144ef14e59;2df843ad-b7d2-4145-bb7b-1cfd94ae19ef)\n\nYou have exceeded your monthly included credits for Inference Endpoints. Subscribe to PRO to get 20x more monthly allowance." |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"client = InferenceClient(\n", |
||||
"\tprovider=\"sambanova\",\n", |
||||
"\tapi_key=hf_token\n", |
||||
")\n", |
||||
"stream = client.chat.completions.create(\n", |
||||
"\tmodel=\"Qwen/Qwen2.5-Coder-32B-Instruct\", \n", |
||||
"\tmessages=messages, \n", |
||||
"\tmax_tokens=500,\n", |
||||
"\tstream=True\n", |
||||
")\n", |
||||
"\n", |
||||
"for chunk in stream:\n", |
||||
" print(chunk.choices[0].delta.content, end=\"\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 65, |
||||
"id": "cc0c3e9c-2572-41d1-a476-6eae96b20695", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# using inference providers\n", |
||||
"def stream_code_qwen(python):\n", |
||||
" messages = messages_for(python)\n", |
||||
" client = InferenceClient(\n", |
||||
" \tprovider=\"sambanova\",\n", |
||||
" \tapi_key=hf_token\n", |
||||
" )\n", |
||||
" stream = client.chat.completions.create(\n", |
||||
" \tmodel=\"Qwen/Qwen2.5-Coder-32B-Instruct\", \n", |
||||
" \tmessages=messages, \n", |
||||
" \tmax_tokens=500,\n", |
||||
" \tstream=True\n", |
||||
" )\n", |
||||
" result = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" if chunk.choices and chunk.choices[0].delta.content:\n", |
||||
" result += chunk.choices[0].delta.content\n", |
||||
" yield result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 212, |
||||
"id": "a82387d1-7651-4923-995b-fe18356fcaa6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def optimize(python, model, language):\n", |
||||
" if model==\"GPT\":\n", |
||||
" result = stream_gpt(python, language)\n", |
||||
" elif model==\"Claude\":\n", |
||||
" result = stream_claude(python, language)\n", |
||||
" elif model==\"CodeQwen\":\n", |
||||
" result = stream_code_qwen(python, language)\n", |
||||
" else:\n", |
||||
" raise ValueError(\"Unknown model\")\n", |
||||
" for stream_so_far in result:\n", |
||||
" yield stream_so_far " |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 213, |
||||
"id": "4ba311ec-c16a-4fe0-946b-4b940704cf65", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def select_sample_program(sample_program):\n", |
||||
" if sample_program==\"pi\":\n", |
||||
" return pi\n", |
||||
" elif sample_program==\"python_hard\":\n", |
||||
" return python_hard\n", |
||||
" else:\n", |
||||
" return \"Type your Python program here\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 214, |
||||
"id": "06148e88-501b-4686-a41d-c3be528d8e6f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def execute_cpp(code):\n", |
||||
" write_output(code, \"optimized.exe\")\n", |
||||
" try:\n", |
||||
" compile_cmd = [\"g++\", \"-Ofast\", \"-std=c++17\", \"-march=native\", \"-mtune=intel\", \"-o\", \"optimized\", \"optimized.cpp\"]\n", |
||||
" compile_result = subprocess.run(compile_cmd, check=True, text=True, capture_output=True)\n", |
||||
" run_cmd = [\"optimized.exe\"]\n", |
||||
" run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True)\n", |
||||
" return run_result.stdout\n", |
||||
" except subprocess.CalledProcessError as e:\n", |
||||
" return f\"An error occurred:\\n{e.stderr}\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 236, |
||||
"id": "a42e3871-f3a5-4f14-836c-1e8ecacb56b5", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def execute_java(code):\n", |
||||
" # Extract the class name from the Java code\n", |
||||
" match = re.search(r\"\\b(public\\s+)?class\\s+(\\w+)\", code)\n", |
||||
" class_name = match.group(2) if match else \"OptimizedJava\"\n", |
||||
"\n", |
||||
" file_name = f\"{class_name}.java\"\n", |
||||
" write_output(code, file_name)\n", |
||||
" try:\n", |
||||
" compile_cmd =[\"javac\", file_name]\n", |
||||
" subprocess.run(compile_cmd, check=True, text=True, capture_output=True)\n", |
||||
" run_cmd = [\"java\", class_name]\n", |
||||
" run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True)\n", |
||||
" return run_result.stdout\n", |
||||
" except subprocess.CalledProcessError as e:\n", |
||||
" return f\"Error during compilation or execution:\\n{e.stderr}\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 238, |
||||
"id": "f9ca2e6f-60c1-4e5f-b570-63c75b2d189b", |
||||
"metadata": { |
||||
"scrolled": true |
||||
}, |
||||
"outputs": [ |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"http://127.0.0.1:7901/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 238, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"source": [ |
||||
"with gr.Blocks(css=css) as ui:\n", |
||||
" gr.Markdown(\"## Convert code from Python to C++ or Java\")\n", |
||||
" #input and output\n", |
||||
" with gr.Row():\n", |
||||
" python = gr.Textbox(label=\"Python code:\", value=python_hard, lines=10)\n", |
||||
" converted_code = gr.Textbox(label=\"Converted code:\", lines=10)\n", |
||||
" # java = gr.Textbox(label=\"Java code:\", lines=10)\n", |
||||
" #sample programs\n", |
||||
" with gr.Row():\n", |
||||
" with gr.Column():\n", |
||||
" sample_program = gr.Radio([\"pi\", \"python_hard\"], label=\"Sample program\", value=\"python_hard\")\n", |
||||
" #select model and language\n", |
||||
" with gr.Row():\n", |
||||
" with gr.Column():\n", |
||||
" model = gr.Dropdown([\"GPT\", \"Claude\", \"CodeQwen\"], label=\"Select model\", value=\"GPT\")\n", |
||||
" language = gr.Dropdown([\"C++\",\"Java\"], label=\"Select language\", value=\"C++\")\n", |
||||
" with gr.Row():\n", |
||||
" convert = gr.Button(\"Convert\")\n", |
||||
" #Code execution\n", |
||||
" with gr.Row():\n", |
||||
" python_run = gr.Button(\"Run Python\")\n", |
||||
" converted_run = gr.Button(\"Run converted code\")\n", |
||||
" with gr.Row():\n", |
||||
" python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n", |
||||
" output = gr.TextArea(label=\"Converted code result:\", elem_classes=[\"cpp\"])\n", |
||||
" \n", |
||||
" # Function to convert Python code based on language\n", |
||||
" def convert_code(python_code, model, selected_language):\n", |
||||
" if selected_language == \"C++\":\n", |
||||
" for chunk in optimize(python_code, model, \"cpp\"):\n", |
||||
" yield chunk # Stream each chunk\n", |
||||
" elif selected_language == \"Java\":\n", |
||||
" for chunk in optimize(python_code, model, \"java\"):\n", |
||||
" yield chunk\n", |
||||
" return \"\"\n", |
||||
"\n", |
||||
" # Function to execute converted code\n", |
||||
" def run_code(converted_code, selected_language):\n", |
||||
" if selected_language == \"C++\":\n", |
||||
" return execute_cpp(converted_code)\n", |
||||
" elif selected_language == \"Java\":\n", |
||||
" return execute_java(converted_code)\n", |
||||
" return \"Invalid language selection\"\n", |
||||
"\n", |
||||
" sample_program.change(select_sample_program, inputs=[sample_program], outputs=[python])\n", |
||||
" convert.click(convert_code, inputs=[python, model, language], outputs=[converted_code])\n", |
||||
" converted_run.click(run_code, inputs=[converted_code, language], outputs=[output]) \n", |
||||
" python_run.click(execute_python, inputs=[python], outputs=[python_out])\n", |
||||
"\n", |
||||
"ui.launch(inbrowser=True)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "9d0ad093-425b-488e-8c3f-67f729dd9c06", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
@ -0,0 +1,29 @@
|
||||
# Script Overview |
||||
|
||||
The documentation will show you how to run the python script generate_doc_string.py. It is designed to take input |
||||
from an existing python file and create a new one with a suffix ('claude' or 'gpt'). If you do not specify and llm |
||||
model, it will default to claude. |
||||
|
||||
# How to run |
||||
|
||||
```powershell |
||||
conda activate llms |
||||
cd <script_location> |
||||
python generate_doc_string -fp <full_file_path> -llm <name_of_model> |
||||
``` |
||||
|
||||
# Show Help Instructions |
||||
|
||||
```shell |
||||
python generate_doc_string --help |
||||
``` |
||||
|
||||
# Error Checking |
||||
|
||||
1) File Path Existence |
||||
|
||||
If the file path doesn't exist, the script will stop running and print out an error. |
||||
|
||||
2) LLM Model Choice |
||||
|
||||
If you choose something other than 'gpt' or 'claude', it will show and assertion error. |
@ -0,0 +1,19 @@
|
||||
|
||||
def calculate(iterations, param1, param2): |
||||
result = 1.0 |
||||
for i in range(1, iterations+1): |
||||
j = i * param1 - param2 |
||||
result -= (1/j) |
||||
j = i * param1 + param2 |
||||
result += (1/j) |
||||
return result |
||||
|
||||
|
||||
def calculate_2(iterations, param1, param2): |
||||
result = 1.0 |
||||
for i in range(1, iterations+1): |
||||
j = i * param1 - param2 |
||||
result -= (1/j) |
||||
j = i * param1 + param2 |
||||
result += (1/j) |
||||
return result |
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