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Merge pull request #62 from codenigma1/day1_hw

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Fully Custmomize Airlines Project Added and New Refine query approach
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Ed Donner
2024-12-24 21:26:39 +00:00
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commit b68d129afe
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week2/community-contributions/day3-refine-user-query-by-llama.ipynb Normal file
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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": 1,
"id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "231605aa-fccb-447e-89cf-8b187444536a",
"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": 3,
"id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb",
"metadata": {},
"outputs": [],
"source": [
"# Initialize\n",
"\n",
"openai = OpenAI()\n",
"MODEL = 'gpt-4o-mini'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e16839b5-c03b-4d9d-add6-87a0f6f37575",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant\""
]
},
{
"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": 5,
"id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1",
"metadata": {},
"outputs": [],
"source": [
"# It's now just 1 line of code to prepare the input to OpenAI!\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 = 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",
"id": "1334422a-808f-4147-9c4c-57d63d9780d0",
"metadata": {},
"source": [
"## And then enter Gradio's magic!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0866ca56-100a-44ab-8bd0-1568feaf6bf2",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "1f91b414-8bab-472d-b9c9-3fa51259bdfe",
"metadata": {},
"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": 8,
"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",
" 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": "code",
"execution_count": null,
"id": "413e9e4e-7836-43ac-a0c3-e1ab5ed6b136",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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": null,
"id": "c602a8dd-2df7-4eb7-b539-4e01865a6351",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ce43fe80",
"metadata": {},
"outputs": [],
"source": [
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "4f28e3a8",
"metadata": {},
"outputs": [],
"source": [
"ollama_system_prompt = \"\"\"You assistant only to refine user query like check grammatical, capital, lower case that make sophisticated prompt.\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "667a7fbb",
"metadata": {},
"outputs": [],
"source": [
"ollama_user_prompt = \"\"\"You assistant like RAG technology use to add more informaiton about the content in the user query. \n",
"Please look at some content as following we don't have in our store:\n",
"pants\n",
"sunglasses\n",
"watch\n",
"underwear\n",
"If you find this items in user query, answer gently like The store does not sell like 'e.g. pants'; if they are asked for 'pants', be sure to point out other items on sale.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "4632f16b",
"metadata": {},
"outputs": [],
"source": [
"user_messages = [{\"role\": \"system\", \"content\": ollama_system_prompt}, {\"role\": \"user\", \"content\": ollama_user_prompt},]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "0a987a66-1061-46d6-a83a-a30859dc88bf",
"metadata": {},
"outputs": [],
"source": [
"# Fixed a bug in this function brilliantly identified by student Gabor M.!\n",
"# I've also improved the structure of this function\n",
"\n",
"def chat(message, history):\n",
" relevant_system_message = system_message\n",
"\n",
" # Refine the user query\n",
" try:\n",
" refine_query = ollama_via_openai.chat.completions.create(model='llama3.2', messages=user_messages)\n",
" refined_content = refine_query.choices[0].message.content\n",
" except Exception as e:\n",
" print(f\"Error in refinement: {e}\")\n",
" refined_content = \"Error in refining query.\"\n",
"\n",
" # Log the original and refined queries\n",
" # print(f\"Original User Query: {message}\")\n",
" # print(f\"Refined Query: {refined_content}\")\n",
" # print(\"============================== END of REFINE CODE ===================================\")\n",
"\n",
" relevant_system_message += refined_content\n",
" \n",
" messages = [{\"role\": \"system\", \"content\": relevant_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": "code",
"execution_count": null,
"id": "20570de2-eaad-42cc-a92c-c779d71b48b6",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"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": "llm_env",
"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
}

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week2/community-contributions/day4-airlines-project-fullyCustomize.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"id": "ddfa9ae6-69fe-444a-b994-8c4c5970a7ec",
"metadata": {},
"source": [
"# **Project - Airline AI Assistant**\n",
"\n",
"## **Important Features of Airline AI Assistant**\n",
"\n",
"### ✈️ **Flight Availability**\n",
"- Check available flights to a destination with:\n",
" - Airline name, departure time, price, and duration.\n",
"- Alerts user if no flights are found.\n",
"\n",
"### 🛫 **Step-by-step Flight Booking**\n",
"- Guides users through:\n",
" 1. Selecting source and destination cities.\n",
" 2. Choosing a flight option.\n",
" 3. Providing passenger details (name, age).\n",
"- Ensures source and destination are not the same.\n",
"\n",
"### 🌛 **Ticket Generation**\n",
"- Creates a unique ticket file: `firstName_lastName_bookingNumber.txt`.\n",
"- Ticket includes:\n",
" - Passenger details\n",
" - Flight details (airline, time, price, seat number)\n",
"\n",
"### 📊 **Generate Summary Report**\n",
"- Summarizes all bookings into a single file: `summary_report.txt`.\n",
"- Includes all flight and passenger details for review or administration.\n",
"\n",
"### 🪑 **Automated Seat Assignment**\n",
"- Assigns a random but consistent seat number for each booking.\n",
"- Ensures unique seats for each flight.\n",
"\n",
"### 💬 **Interactive Chat Interface**\n",
"- Real-time conversation via Gradio.\n",
"- Provides clear, polite responses based on user input.\n",
"\n",
"### 🛠️ **Modular Tool Support**\n",
"- Integrated tools for:\n",
" - Checking flight availability\n",
" - Booking flights\n",
" - Generating reports\n",
"- Easily extensible for future features.\n",
"\n",
"### 🛡️ **Error Handling**\n",
"- Validates user inputs and prevents invalid bookings.\n",
"- Graceful error messages for smooth user experience.\n",
"\n",
"---\n",
"\n",
"These features ensure a seamless, user-friendly experience while booking flights or managing ticket details!\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "747e8786-9da8-4342-b6c9-f5f69c2e22ae",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"import random\n",
"from dotenv import load_dotenv\n",
"import gradio as gr\n",
"from openai import OpenAI\n",
"\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\" # or \"gpt-3.5-turbo\", etc.\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 155,
"id": "0a521d84-d07c-49ab-a0df-d6451499ed97",
"metadata": {},
"outputs": [],
"source": [
"###############################################################################\n",
"# 1) System Prompt\n",
"###############################################################################\n",
"system_message = (\n",
" \"You are a helpful assistant for an Airline called FlightAI.\\n\\n\"\n",
" \"When the user wants to book a flight, follow these steps:\\n\"\n",
" \"1. Ask for the source city.\\n\"\n",
" \"2. Ask for the destination city (must be different from source).\\n\"\n",
" \"3. Call the function 'check_flight_availability' with the user's destination.\\n\"\n",
" \" - If it returns an empty list, say: 'No flights to that city'.\\n\"\n",
" \" - If it returns flights, list them EXACTLY, in a numbered list, showing airline, time, price, and duration.\\n\"\n",
" \"4. Wait for the user to pick one flight option by number.\\n\"\n",
" \"5. Then ask for passenger first name, last name, and age.\\n\"\n",
" \"6. Finally call 'book_flight' to confirm and show the user the real seat number and booking details.\\n\\n\"\n",
" \"You also have a tool 'generate_report' which summarizes ALL booked tickets in a single file.\\n\\n\"\n",
" \"IMPORTANT:\\n\"\n",
" \"- Always call 'check_flight_availability' if user mentions a new destination.\\n\"\n",
" \"- Do not invent flights or seat numbers. Use what the function calls return.\\n\"\n",
" \"- Source and destination cannot be the same.\\n\"\n",
" \"- Every time a flight is booked, produce a new ticket file named firstName_lastName_bookingNumber.txt.\\n\"\n",
" \"- If a city is not in flight_availability, say 'No flights found for that city'.\\n\"\n",
" \"If the user wants all tickets summarized, call 'generate_report' with no arguments (the function has none).\\n\"\n",
" \"If you don't know something, say so.\\n\"\n",
" \"Keep answers short and courteous.\\n\"\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 156,
"id": "61a2a15d-b559-4844-b377-6bd5cb4949f6",
"metadata": {},
"outputs": [],
"source": [
"###############################################################################\n",
"# 2) Flight Availability with Price & Duration\n",
"###############################################################################\n",
"flight_availability = {\n",
" \"london\": [\n",
" {\n",
" \"airline\": \"AirlinesAI\",\n",
" \"time\": \"10:00 AM\",\n",
" \"price\": \"$799\",\n",
" \"duration\": \"8 hours\"\n",
" },\n",
" {\n",
" \"airline\": \"IndianAirlinesAI\",\n",
" \"time\": \"3:00 PM\",\n",
" \"price\": \"$899\",\n",
" \"duration\": \"8 hours\"\n",
" },\n",
" {\n",
" \"airline\": \"AmericanAirlinesAI\",\n",
" \"time\": \"8:00 PM\",\n",
" \"price\": \"$999\",\n",
" \"duration\": \"8 hours\"\n",
" },\n",
" ],\n",
" \"paris\": [\n",
" {\n",
" \"airline\": \"EuropeanAirlinesAI\",\n",
" \"time\": \"11:00 AM\",\n",
" \"price\": \"$399\",\n",
" \"duration\": \"7 hours\"\n",
" },\n",
" {\n",
" \"airline\": \"BudgetAirlines\",\n",
" \"time\": \"6:00 PM\",\n",
" \"price\": \"$2399\",\n",
" \"duration\": \"7 hours\"\n",
" },\n",
" ],\n",
" \"tokyo\": [\n",
" {\n",
" \"airline\": \"TokyoAirlinesAI\",\n",
" \"time\": \"12:00 PM\",\n",
" \"price\": \"$4000\",\n",
" \"duration\": \"5 hours\"\n",
" },\n",
" {\n",
" \"airline\": \"FastFly\",\n",
" \"time\": \"7:00 PM\",\n",
" \"price\": \"$1400\",\n",
" \"duration\": \"5 hours\"\n",
" },\n",
" ],\n",
" \"berlin\": [\n",
" {\n",
" \"airline\": \"BerlinAirlinesAI\",\n",
" \"time\": \"9:00 AM\",\n",
" \"price\": \"$499\",\n",
" \"duration\": \"6 hours\"\n",
" },\n",
" {\n",
" \"airline\": \"AmericanAirlinesAI\",\n",
" \"time\": \"4:00 PM\",\n",
" \"price\": \"$899\",\n",
" \"duration\": \"6 hours\"\n",
" },\n",
" ],\n",
" \"nagpur\": [\n",
" {\n",
" \"airline\": \"IndianAirlinesAI\",\n",
" \"time\": \"8:00 AM\",\n",
" \"price\": \"$1000\",\n",
" \"duration\": \"10 hours\"\n",
" },\n",
" {\n",
" \"airline\": \"JetAirlines\",\n",
" \"time\": \"2:00 PM\",\n",
" \"price\": \"$1500\",\n",
" \"duration\": \"10 hours\"\n",
" },\n",
" {\n",
" \"airline\": \"AirlinesAI\",\n",
" \"time\": \"10:00 PM\",\n",
" \"price\": \"$800\",\n",
" \"duration\": \"10 hours\"\n",
" },\n",
" ],\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 157,
"id": "0696acb1-0b05-4dc2-80d5-771be04f1fb2",
"metadata": {},
"outputs": [],
"source": [
"# A global list of flight bookings\n",
"flight_bookings = []\n"
]
},
{
"cell_type": "code",
"execution_count": 158,
"id": "80ca4e09-6287-4d3f-997d-fa6afbcf6c85",
"metadata": {},
"outputs": [],
"source": [
"###############################################################################\n",
"# 3) Helper Functions\n",
"###############################################################################\n",
"def generate_seat_numbers(seed_value):\n",
" random.seed(seed_value)\n",
" return [\n",
" f\"{random.choice('ABCDEFGHIJKLMNOPQRSTUVWXYZ')}{random.randint(1, 99):02}\"\n",
" for _ in range(5)\n",
" ]\n",
"\n",
"def check_flight_availability(destination_city: str):\n",
" \"\"\"\n",
" Return the flights for a given city from 'flight_availability'.\n",
" If city not in dictionary, return an empty list.\n",
" \"\"\"\n",
" print(f\"[TOOL] check_flight_availability({destination_city})\")\n",
" city = destination_city.lower()\n",
" return flight_availability.get(city, [])\n",
"\n",
"def generate_ticket_file(booking_dict, booking_number):\n",
" \"\"\"\n",
" Create a text file: firstName_lastName_bookingNumber.txt\n",
" containing flight details.\n",
" \"\"\"\n",
" fname = booking_dict[\"first_name\"].replace(\" \", \"_\")\n",
" lname = booking_dict[\"last_name\"].replace(\" \", \"_\")\n",
" filename = f\"{fname}_{lname}_{booking_number}.txt\"\n",
"\n",
" content = (\n",
" \"Flight Ticket\\n\"\n",
" \"=============\\n\"\n",
" f\"Booking # : {booking_number}\\n\"\n",
" f\"Passenger : {booking_dict['first_name']} {booking_dict['last_name']}, Age {booking_dict['age']}\\n\"\n",
" f\"Source : {booking_dict['source']}\\n\"\n",
" f\"Destination : {booking_dict['destination']}\\n\"\n",
" f\"Airline : {booking_dict['airline']}\\n\"\n",
" f\"Departure : {booking_dict['time']}\\n\"\n",
" f\"Price : {booking_dict['price']}\\n\"\n",
" f\"Duration : {booking_dict['duration']}\\n\"\n",
" f\"Seat Number : {booking_dict['seat']}\\n\"\n",
" )\n",
" with open(filename, \"w\") as f:\n",
" f.write(content)\n",
"\n",
" print(f\"[TOOL] Ticket file generated => {filename}\")\n",
" return filename\n",
"\n",
"def book_flight(source, destination, option_index, first_name, last_name, age):\n",
" \"\"\"\n",
" Book a flight using an option index for the chosen city.\n",
" - source != destination\n",
" - index is 1-based => we do pick = idx - 1\n",
" - create new booking record, seat assignment, & ticket file\n",
" \"\"\"\n",
" print(f\"[TOOL] book_flight({source=}, {destination=}, {option_index=})\")\n",
"\n",
" if source.lower() == destination.lower():\n",
" return \"Error: source and destination must not be the same.\"\n",
"\n",
" # Convert option index from string to integer\n",
" try:\n",
" idx = int(option_index)\n",
" except ValueError:\n",
" return \"Error: flight option number is not a valid integer.\"\n",
"\n",
" flights = check_flight_availability(destination)\n",
" if not flights:\n",
" return f\"Error: No flights found for {destination.title()}.\"\n",
"\n",
" pick = idx - 1\n",
" if pick < 0 or pick >= len(flights):\n",
" return f\"Error: Invalid flight option #{idx} for {destination.title()}.\"\n",
"\n",
" chosen_flight = flights[pick]\n",
" airline = chosen_flight[\"airline\"]\n",
" dep_time = chosen_flight[\"time\"]\n",
" price = chosen_flight[\"price\"]\n",
" duration = chosen_flight[\"duration\"]\n",
"\n",
" # Generate seat\n",
" seat_list = generate_seat_numbers(hash(destination + airline + str(len(flight_bookings))))\n",
" chosen_seat = seat_list[0]\n",
"\n",
" new_booking = {\n",
" \"source\": source.title(),\n",
" \"destination\": destination.title(),\n",
" \"airline\": airline,\n",
" \"time\": dep_time,\n",
" \"price\": price,\n",
" \"duration\": duration,\n",
" \"seat\": chosen_seat,\n",
" \"first_name\": first_name.title(),\n",
" \"last_name\": last_name.title(),\n",
" \"age\": age,\n",
" }\n",
" flight_bookings.append(new_booking)\n",
"\n",
" booking_number = len(flight_bookings)\n",
" ticket_filename = generate_ticket_file(new_booking, booking_number)\n",
"\n",
" confirmation = (\n",
" f\"Booking #{booking_number} confirmed for {first_name.title()} {last_name.title()}. \"\n",
" f\"Flight from {source.title()} to {destination.title()} on {airline} at {dep_time}. \"\n",
" f\"Ticket saved to {ticket_filename}.\"\n",
" )\n",
" print(f\"[TOOL] {confirmation}\")\n",
" return confirmation\n",
"\n",
"def generate_report():\n",
" \"\"\"\n",
" Summarize ALL tickets in a single file called summary_report.txt.\n",
" \"\"\"\n",
" print(f\"[TOOL] generate_report called.\")\n",
"\n",
" report_content = \"Flight Booking Summary Report\\n\"\n",
" report_content += \"=============================\\n\"\n",
"\n",
" if not flight_bookings:\n",
" report_content += \"No bookings found.\\n\"\n",
" else:\n",
" for i, booking in enumerate(flight_bookings, start=1):\n",
" report_content += (\n",
" f\"Booking # : {i}\\n\"\n",
" f\"Passenger : {booking['first_name']} {booking['last_name']}, Age {booking['age']}\\n\"\n",
" f\"Source : {booking['source']}\\n\"\n",
" f\"Destination : {booking['destination']}\\n\"\n",
" f\"Airline : {booking['airline']}\\n\"\n",
" f\"Departure : {booking['time']}\\n\"\n",
" f\"Price : {booking['price']}\\n\"\n",
" f\"Duration : {booking['duration']}\\n\"\n",
" f\"Seat Number : {booking['seat']}\\n\"\n",
" \"-------------------------\\n\"\n",
" )\n",
"\n",
" filename = \"summary_report.txt\"\n",
" with open(filename, \"w\") as f:\n",
" f.write(report_content)\n",
"\n",
" msg = f\"Summary report generated => {filename}\"\n",
" print(f\"[TOOL] {msg}\")\n",
" return msg\n"
]
},
{
"cell_type": "code",
"execution_count": 159,
"id": "39fb9008",
"metadata": {},
"outputs": [],
"source": [
"###############################################################################\n",
"# 4) Tools JSON Schemas\n",
"###############################################################################\n",
"price_function = {\n",
" \"name\": \"get_ticket_price\",\n",
" \"description\": \"Get the price of a return ticket for the city from the flight list data (not strictly needed now).\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"City name.\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" },\n",
"}\n",
"\n",
"availability_function = {\n",
" \"name\": \"check_flight_availability\",\n",
" \"description\": (\n",
" \"Check flight availability for the specified city. \"\n",
" \"Returns a list of {airline, time, price, duration}.\"\n",
" ),\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"City name to check in flight_availability dict.\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" },\n",
"}\n",
"\n",
"book_function = {\n",
" \"name\": \"book_flight\",\n",
" \"description\": (\n",
" \"Book a flight using an option index for the chosen city. \"\n",
" \"Generates a unique ticket file firstName_lastName_{bookingNumber}.txt each time.\"\n",
" ),\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"source\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"User's source city (must differ from destination).\",\n",
" },\n",
" \"destination\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"User's destination city.\",\n",
" },\n",
" \"option_index\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"1-based flight option number the user selected from check_flight_availability.\",\n",
" },\n",
" \"first_name\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Passenger's first name.\",\n",
" },\n",
" \"last_name\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Passenger's last name.\",\n",
" },\n",
" \"age\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Passenger's age.\",\n",
" },\n",
" },\n",
" \"required\": [\"source\", \"destination\", \"option_index\", \"first_name\", \"last_name\", \"age\"],\n",
" },\n",
"}\n",
"\n",
"report_function = {\n",
" \"name\": \"generate_report\",\n",
" \"description\": (\n",
" \"Generates a summary report of ALL tickets in summary_report.txt.\"\n",
" ),\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" },\n",
" \"required\": [],\n",
" },\n",
"}\n",
"\n",
"tools = [\n",
" {\"type\": \"function\", \"function\": price_function},\n",
" {\"type\": \"function\", \"function\": availability_function},\n",
" {\"type\": \"function\", \"function\": book_function},\n",
" {\"type\": \"function\", \"function\": report_function},\n",
"]\n"
]
},
{
"cell_type": "code",
"execution_count": 160,
"id": "1f003836",
"metadata": {},
"outputs": [],
"source": [
"###############################################################################\n",
"# 5) Handle Tool Calls\n",
"###############################################################################\n",
"def handle_tool_call(message):\n",
" \"\"\"\n",
" The LLM can request to call a function in 'tools'. We parse the JSON arguments\n",
" and run the Python function. Then we return a 'tool' message with the result.\n",
" \"\"\"\n",
" tool_call = message.tool_calls[0]\n",
" fn_name = tool_call.function.name\n",
" args = json.loads(tool_call.function.arguments)\n",
"\n",
" if fn_name == \"get_ticket_price\":\n",
" city = args.get(\"destination_city\")\n",
" flights = check_flight_availability(city)\n",
" # In this code, we do not strictly store a single 'price' per city,\n",
" # but let's just return the flights with price or \"No flights\".\n",
" if not flights:\n",
" response_content = {\"destination_city\": city, \"price\": \"No flights found.\"}\n",
" else:\n",
" # Return the first flight's price or something\n",
" response_content = {\n",
" \"destination_city\": city,\n",
" \"price\": flights[0][\"price\"]\n",
" }\n",
"\n",
" elif fn_name == \"check_flight_availability\":\n",
" city = args.get(\"destination_city\")\n",
" flights = check_flight_availability(city)\n",
" response_content = {\"destination_city\": city, \"availability\": flights}\n",
"\n",
" elif fn_name == \"book_flight\":\n",
" src = args.get(\"source\")\n",
" dest = args.get(\"destination\")\n",
" idx = args.get(\"option_index\")\n",
" first_name = args.get(\"first_name\")\n",
" last_name = args.get(\"last_name\")\n",
" age = args.get(\"age\")\n",
"\n",
" confirmation = book_flight(src, dest, idx, first_name, last_name, age)\n",
" response_content = {\n",
" \"source\": src,\n",
" \"destination\": dest,\n",
" \"option_index\": idx,\n",
" \"first_name\": first_name,\n",
" \"last_name\": last_name,\n",
" \"age\": age,\n",
" \"confirmation\": confirmation\n",
" }\n",
"\n",
" elif fn_name == \"generate_report\":\n",
" # No args needed\n",
" msg = generate_report()\n",
" response_content = {\"report\": msg}\n",
"\n",
" else:\n",
" response_content = {\"error\": f\"Unknown tool: {fn_name}\"}\n",
"\n",
" return {\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps(response_content),\n",
" \"tool_call_id\": tool_call.id,\n",
" }, args\n"
]
},
{
"cell_type": "code",
"execution_count": 161,
"id": "f6b34b32",
"metadata": {},
"outputs": [],
"source": [
"###############################################################################\n",
"# 6) Main Chat Function\n",
"###############################################################################\n",
"def chat(message, history):\n",
" \"\"\"\n",
" The main chat loop that handles the conversation with the user,\n",
" passing 'tools' definitions to the LLM for function calling.\n",
" \"\"\"\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
" try:\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=messages,\n",
" tools=tools\n",
" )\n",
"\n",
" # If the LLM requests a function call, handle it\n",
" while response.choices[0].finish_reason == \"tool_calls\":\n",
" msg = response.choices[0].message\n",
" print(f\"[INFO] Tool call requested: {msg.tool_calls[0]}\")\n",
" tool_response, tool_args = handle_tool_call(msg)\n",
" print(f\"[INFO] Tool response: {tool_response}\")\n",
"\n",
" # Add both the LLM's request and our tool response to the conversation\n",
" messages.append(msg)\n",
" messages.append(tool_response)\n",
"\n",
" # Re-send updated conversation to get final or next step\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=messages\n",
" )\n",
"\n",
" # Return normal text response (finish_reason = \"stop\")\n",
" return response.choices[0].message.content\n",
"\n",
" except Exception as e:\n",
" print(f\"[ERROR] {e}\")\n",
" return \"I'm sorry, something went wrong while processing your request.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cea4b097",
"metadata": {},
"outputs": [],
"source": [
"###############################################################################\n",
"# 7) Launch Gradio\n",
"###############################################################################\n",
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b39d5a6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "llm_env",
"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
}