diff --git a/week2/community-contributions/day3-refine-user-query-by-llama.ipynb b/week2/community-contributions/day3-refine-user-query-by-llama.ipynb
new file mode 100644
index 0000000..1034274
--- /dev/null
+++ b/week2/community-contributions/day3-refine-user-query-by-llama.ipynb
@@ -0,0 +1,364 @@
+{
+ "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": [
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " Business Applications\n",
+ " 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",
+ "
\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.\n",
+ " | \n",
+ "
\n",
+ "
"
+ ]
+ },
+ {
+ "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
+}
diff --git a/week2/community-contributions/day4-airlines-project-fullyCustomize.ipynb b/week2/community-contributions/day4-airlines-project-fullyCustomize.ipynb
new file mode 100644
index 0000000..04a0cf4
--- /dev/null
+++ b/week2/community-contributions/day4-airlines-project-fullyCustomize.ipynb
@@ -0,0 +1,640 @@
+{
+ "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
+}