From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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574 lines
17 KiB
574 lines
17 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "8b0e11f2-9ea4-48c2-b8d2-d0a4ba967827", |
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"metadata": {}, |
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"source": [ |
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"# Gradio Day!\n", |
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"\n", |
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"Today we will build User Interfaces using the outrageously simple Gradio framework.\n", |
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"\n", |
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"Prepare for joy!\n", |
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"\n", |
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"Please note: your Gradio screens may appear in 'dark mode' or 'light mode' depending on your computer settings." |
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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": "c44c5494-950d-4d2f-8d4f-b87b57c5b330", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports\n", |
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"\n", |
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"import os\n", |
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"import requests\n", |
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"from bs4 import BeautifulSoup\n", |
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"from typing import List\n", |
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"from dotenv import load_dotenv\n", |
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"from openai import OpenAI\n", |
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"import google.generativeai\n", |
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"import anthropic" |
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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": "d1715421-cead-400b-99af-986388a97aff", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import gradio as gr # oh yeah!" |
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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": "337d5dfc-0181-4e3b-8ab9-e78e0c3f657b", |
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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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"# Print the key prefixes to help with any debugging\n", |
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"\n", |
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"load_dotenv()\n", |
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"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
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"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
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"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
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"\n", |
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"if openai_api_key:\n", |
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" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
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"else:\n", |
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" print(\"OpenAI API Key not set\")\n", |
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" \n", |
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"if anthropic_api_key:\n", |
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" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
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"else:\n", |
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" print(\"Anthropic API Key not set\")\n", |
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"\n", |
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"if google_api_key:\n", |
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" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
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"else:\n", |
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" print(\"Google API Key not set\")" |
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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": "22586021-1795-4929-8079-63f5bb4edd4c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n", |
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"\n", |
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"openai = OpenAI()\n", |
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"\n", |
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"claude = anthropic.Anthropic()\n", |
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"\n", |
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"google.generativeai.configure()" |
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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": "b16e6021-6dc4-4397-985a-6679d6c8ffd5", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# A generic system message - no more snarky adversarial AIs!\n", |
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"\n", |
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"system_message = \"You are a helpful assistant\"" |
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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": "02ef9b69-ef31-427d-86d0-b8c799e1c1b1", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's wrap a call to GPT-4o-mini in a simple function\n", |
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"\n", |
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"def message_gpt(prompt):\n", |
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" messages = [\n", |
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" {\"role\": \"system\", \"content\": system_message},\n", |
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" {\"role\": \"user\", \"content\": prompt}\n", |
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" ]\n", |
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" completion = openai.chat.completions.create(\n", |
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" model='gpt-4o-mini',\n", |
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" messages=messages,\n", |
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" )\n", |
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" return completion.choices[0].message.content" |
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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": "aef7d314-2b13-436b-b02d-8de3b72b193f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"message_gpt(\"What is today's date?\")" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "f94013d1-4f27-4329-97e8-8c58db93636a", |
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"metadata": {}, |
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"source": [ |
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"## User Interface time!" |
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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": "bc664b7a-c01d-4fea-a1de-ae22cdd5141a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# here's a simple function\n", |
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"\n", |
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"def shout(text):\n", |
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" print(f\"Shout has been called with input {text}\")\n", |
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" return text.upper()" |
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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": "083ea451-d3a0-4d13-b599-93ed49b975e4", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"shout(\"hello\")" |
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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": "08f1f15a-122e-4502-b112-6ee2817dda32", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# The simplicty of gradio. This might appear in \"light mode\" - I'll show you how to make this in dark mode later.\n", |
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"\n", |
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"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\").launch()" |
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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": "c9a359a4-685c-4c99-891c-bb4d1cb7f426", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Adding share=True means that it can be accessed publically\n", |
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"# A more permanent hosting is available using a platform called Spaces from HuggingFace, which we will touch on next week\n", |
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"# 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", |
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"\n", |
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"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)" |
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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": "cd87533a-ff3a-4188-8998-5bedd5ba2da3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Adding inbrowser=True opens up a new browser window automatically\n", |
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"\n", |
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"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(inbrowser=True)" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "b42ec007-0314-48bf-84a4-a65943649215", |
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"metadata": {}, |
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"source": [ |
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"## Forcing dark mode\n", |
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"\n", |
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"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." |
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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": "e8129afa-532b-4b15-b93c-aa9cca23a546", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Define this variable and then pass js=force_dark_mode when creating the Interface\n", |
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"\n", |
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"force_dark_mode = \"\"\"\n", |
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"function refresh() {\n", |
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" const url = new URL(window.location);\n", |
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" if (url.searchParams.get('__theme') !== 'dark') {\n", |
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" url.searchParams.set('__theme', 'dark');\n", |
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" window.location.href = url.href;\n", |
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" }\n", |
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"}\n", |
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"\"\"\"\n", |
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"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\", js=force_dark_mode).launch()" |
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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": "3cc67b26-dd5f-406d-88f6-2306ee2950c0", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Inputs and Outputs\n", |
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"\n", |
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"view = gr.Interface(\n", |
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" fn=shout,\n", |
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" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", |
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" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", |
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" flagging_mode=\"never\"\n", |
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")\n", |
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"view.launch()" |
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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": "f235288e-63a2-4341-935b-1441f9be969b", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# And now - changing the function from \"shout\" to \"message_gpt\"\n", |
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"\n", |
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"view = gr.Interface(\n", |
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" fn=message_gpt,\n", |
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" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", |
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" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", |
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" flagging_mode=\"never\"\n", |
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")\n", |
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"view.launch()" |
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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": "af9a3262-e626-4e4b-80b0-aca152405e63", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's use Markdown\n", |
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"# Are you wondering why it makes any difference to set system_message when it's not referred to in the code below it?\n", |
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"# I'm taking advantage of system_message being a global variable, used back in the message_gpt function (go take a look)\n", |
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"# Not a great software engineering practice, but quite sommon during Jupyter Lab R&D!\n", |
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"\n", |
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"system_message = \"You are a helpful assistant that responds in markdown\"\n", |
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"\n", |
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"view = gr.Interface(\n", |
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" fn=message_gpt,\n", |
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" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
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" outputs=[gr.Markdown(label=\"Response:\")],\n", |
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" flagging_mode=\"never\"\n", |
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")\n", |
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"view.launch()" |
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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": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's create a call that streams back results\n", |
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"# If you'd like a refresher on Generators (the \"yield\" keyword),\n", |
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"# Please take a look at the Intermediate Python notebook in week1 folder.\n", |
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"\n", |
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"def stream_gpt(prompt):\n", |
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" messages = [\n", |
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" {\"role\": \"system\", \"content\": system_message},\n", |
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" {\"role\": \"user\", \"content\": prompt}\n", |
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" ]\n", |
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" stream = openai.chat.completions.create(\n", |
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" model='gpt-4o-mini',\n", |
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" messages=messages,\n", |
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" stream=True\n", |
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" )\n", |
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" result = \"\"\n", |
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" for chunk in stream:\n", |
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" result += chunk.choices[0].delta.content or \"\"\n", |
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" yield result" |
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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": "0bb1f789-ff11-4cba-ac67-11b815e29d09", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"view = gr.Interface(\n", |
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" fn=stream_gpt,\n", |
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" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
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" outputs=[gr.Markdown(label=\"Response:\")],\n", |
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" flagging_mode=\"never\"\n", |
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")\n", |
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"view.launch()" |
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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": "bbc8e930-ba2a-4194-8f7c-044659150626", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def stream_claude(prompt):\n", |
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" result = claude.messages.stream(\n", |
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" model=\"claude-3-haiku-20240307\",\n", |
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" max_tokens=1000,\n", |
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" temperature=0.7,\n", |
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" system=system_message,\n", |
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" messages=[\n", |
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" {\"role\": \"user\", \"content\": prompt},\n", |
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" ],\n", |
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" )\n", |
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" response = \"\"\n", |
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" with result as stream:\n", |
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" for text in stream.text_stream:\n", |
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" response += text or \"\"\n", |
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" yield response" |
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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": "a0066ffd-196e-4eaf-ad1e-d492958b62af", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"view = gr.Interface(\n", |
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" fn=stream_claude,\n", |
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" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
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" outputs=[gr.Markdown(label=\"Response:\")],\n", |
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" flagging_mode=\"never\"\n", |
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")\n", |
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"view.launch()" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "bc5a70b9-2afe-4a7c-9bed-2429229e021b", |
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"metadata": {}, |
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"source": [ |
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"## Minor improvement\n", |
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"\n", |
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"I've made a small improvement to this code.\n", |
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"\n", |
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"Previously, it had these lines:\n", |
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"\n", |
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"```\n", |
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"for chunk in result:\n", |
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" yield chunk\n", |
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"```\n", |
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"\n", |
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"There's actually a more elegant way to achieve this (which Python people might call more 'Pythonic'):\n", |
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"\n", |
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"`yield from result`\n", |
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"\n", |
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"I cover this in more detail in the Intermediate Python notebook in the week1 folder - take a look if you'd like more." |
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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": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def stream_model(prompt, model):\n", |
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" if model==\"GPT\":\n", |
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" result = stream_gpt(prompt)\n", |
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" elif model==\"Claude\":\n", |
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" result = stream_claude(prompt)\n", |
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" else:\n", |
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" raise ValueError(\"Unknown model\")\n", |
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" yield from result" |
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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": "8d8ce810-997c-4b6a-bc4f-1fc847ac8855", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"view = gr.Interface(\n", |
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" fn=stream_model,\n", |
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" inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")],\n", |
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" outputs=[gr.Markdown(label=\"Response:\")],\n", |
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" flagging_mode=\"never\"\n", |
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")\n", |
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"view.launch()" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "d933865b-654c-4b92-aa45-cf389f1eda3d", |
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"metadata": {}, |
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"source": [ |
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"# Building a company brochure generator\n", |
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"\n", |
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"Now you know how - it's simple!" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "92d7c49b-2e0e-45b3-92ce-93ca9f962ef4", |
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"metadata": {}, |
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"source": [ |
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"<table style=\"margin: 0; text-align: left;\">\n", |
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" <tr>\n", |
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
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" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
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" </td>\n", |
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" <td>\n", |
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" <h2 style=\"color:#900;\">Before you read the next few cells</h2>\n", |
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" <span style=\"color:#900;\">\n", |
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" 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", |
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" </span>\n", |
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" </td>\n", |
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" </tr>\n", |
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"</table>" |
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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": "1626eb2e-eee8-4183-bda5-1591b58ae3cf", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# A class to represent a Webpage\n", |
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"\n", |
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"class Website:\n", |
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" url: str\n", |
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" title: str\n", |
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" text: str\n", |
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"\n", |
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" def __init__(self, url):\n", |
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" self.url = url\n", |
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" response = requests.get(url)\n", |
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" self.body = response.content\n", |
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" soup = BeautifulSoup(self.body, 'html.parser')\n", |
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" self.title = soup.title.string if soup.title else \"No title found\"\n", |
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" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
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" irrelevant.decompose()\n", |
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" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
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"\n", |
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" def get_contents(self):\n", |
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" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\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": "c701ec17-ecd5-4000-9f68-34634c8ed49d", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# With massive thanks to Bill G. who noticed that a prior version of this had a bug! Now fixed.\n", |
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"\n", |
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"system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n", |
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"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\"" |
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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": "5def90e0-4343-4f58-9d4a-0e36e445efa4", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def stream_brochure(company_name, url, model):\n", |
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" prompt = f\"Please generate a company brochure for {company_name}. Here is their landing page:\\n\"\n", |
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" prompt += Website(url).get_contents()\n", |
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" if model==\"GPT\":\n", |
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" result = stream_gpt(prompt)\n", |
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" elif model==\"Claude\":\n", |
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" result = stream_claude(prompt)\n", |
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" else:\n", |
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" raise ValueError(\"Unknown model\")\n", |
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" yield from result" |
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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": "66399365-5d67-4984-9d47-93ed26c0bd3d", |
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"metadata": {}, |
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"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": "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": "3.11.9" |
|
} |
|
}, |
|
"nbformat": 4, |
|
"nbformat_minor": 5 |
|
}
|
|
|