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{
"cells": [
{
"cell_type": "markdown",
"id": "75e2ef28-594f-4c18-9d22-c6b8cd40ead2",
"metadata": {},
"source": [
"# Day 3 - Conversational AI - aka Chatbot!"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861",
"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 gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "231605aa-fccb-447e-89cf-8b187444536a",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv()\n",
"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb",
"metadata": {},
"outputs": [],
"source": [
"google.generativeai.configure()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e16839b5-c03b-4d9d-add6-87a0f6f37575",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ba2123e7-77ed-43b4-8c37-03658fb42b78",
"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\"\n",
"\n",
"prompts = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
"\n",
"# 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": 7,
"id": "7b933ff3",
"metadata": {},
"outputs": [],
"source": [
"import google.generativeai as genai\n",
"\n",
"model = genai.GenerativeModel('gemini-1.5-flash')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "91578b16",
"metadata": {},
"outputs": [],
"source": [
"chat = model.start_chat(history=[])\n",
"response = chat.send_message('Hello! My name is Shardul.')\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c4bc38f",
"metadata": {},
"outputs": [],
"source": [
"response = chat.send_message('Can you tell something interesting about star wars?')\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "337bee91",
"metadata": {},
"outputs": [],
"source": [
"response = chat.send_message('Do you remember what my name is?')\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bcaf4d95",
"metadata": {},
"outputs": [],
"source": [
"chat.history"
]
},
{
"cell_type": "markdown",
"id": "98e97227-f162-4d1a-a0b2-345ff248cbe7",
"metadata": {},
"source": [
"# Please read this! A change from the video:\n",
"\n",
"In the video, I explain how we now need to write a function called:\n",
"\n",
"`chat(message, history)`\n",
"\n",
"Which expects to receive `history` in a particular format, which we need to map to the OpenAI format before we call OpenAI:\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",
"But Gradio has been upgraded! Now it will pass in `history` in the exact OpenAI format, perfect for us to send straight to OpenAI.\n",
"\n",
"So our work just got easier!\n",
"\n",
"We will write a function `chat(message, history)` where: \n",
"**message** is the prompt to use \n",
"**history** is the past conversation, in OpenAI format \n",
"\n",
"We will combine the system message, history and latest message, then call OpenAI."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\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",
" stream = gemini.generate_content(message, safety_settings=[\n",
" {\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\", \"threshold\": \"BLOCK_NONE\"},\n",
" {\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\", \"threshold\": \"BLOCK_NONE\"},\n",
" {\"category\": \"HARM_CATEGORY_HATE_SPEECH\", \"threshold\": \"BLOCK_NONE\"},\n",
" {\"category\": \"HARM_CATEGORY_HARASSMENT\", \"threshold\": \"BLOCK_NONE\"}], stream=True)\n",
"\n",
" response = \"\"\n",
" for chunk in stream:\n",
" print(chunk) # Print the chunk to understand its structure\n",
" # Adjust the following line based on the actual structure of the chunk\n",
" response += chunk.get('content', '') or ''\n",
" yield response"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6e745e1",
"metadata": {},
"outputs": [],
"source": [
"chat_model = genai.GenerativeModel('gemini-1.5-flash')\n",
"chat = chat_model.start_chat()\n",
"\n",
"msg = \"what is gen ai\"\n",
"stream = chat.send_message(msg, stream=True)\n",
"# print(\"Response:\", stream.text)\n",
"for chunk in stream:\n",
" print(chunk.text)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dce941ee",
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"\n",
"chat = model.start_chat(history=[])\n",
"\n",
"# Transform Gradio history to Gemini format\n",
"def transform_history(history):\n",
" new_history = []\n",
" for chat in history:\n",
" new_history.append({\"parts\": [{\"text\": chat[0]}], \"role\": \"user\"})\n",
" new_history.append({\"parts\": [{\"text\": chat[1]}], \"role\": \"model\"})\n",
" return new_history\n",
"\n",
"def response(message, history):\n",
" global chat\n",
" # The history will be the same as in Gradio, the 'Undo' and 'Clear' buttons will work correctly.\n",
" chat.history = transform_history(history)\n",
" response = chat.send_message(message)\n",
" response.resolve()\n",
"\n",
" # Each character of the answer is displayed\n",
" for i in range(len(response.text)):\n",
" time.sleep(0.01)\n",
" yield response.text[: i+1]\n",
"\n",
"gr.ChatInterface(response,\n",
" textbox=gr.Textbox(placeholder=\"Question to Gemini\")).launch(debug=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": "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"
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"nbformat": 4,
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