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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "528b6777",
+   "metadata": {},
+   "source": [
+    "# Complete Chatbot Application Code Explanation\n",
+    "\n",
+    "## Overview: What This Application Does\n",
+    "\n",
+    "This code creates a sophisticated web-based chatbot interface using Gradio (a Python library for building user interfaces) that connects to OpenAI's language models. Think of it as building your own ChatGPT-like interface with customizable features.\n",
+    "\n",
+    "## Import Section: Setting Up the Tools\n",
+    "\n",
+    "```python\n",
+    "import gradio as gr\n",
+    "import openai\n",
+    "import json\n",
+    "import os\n",
+    "import time\n",
+    "from typing import List, Tuple, Optional\n",
+    "import requests\n",
+    "from datetime import datetime\n",
+    "```\n",
+    "\n",
+    "Let me explain what each import does:\n",
+    "- `gradio as gr`: This is the main library that creates the web interface with buttons, text boxes, and chat windows\n",
+    "- `openai`: The official OpenAI library to communicate with their AI models like GPT-3.5 and GPT-4\n",
+    "- `json`: Handles saving and loading conversation data in a structured format\n",
+    "- `os`: Interacts with the operating system (like creating folders for saved files)\n",
+    "- `time`: Could be used for adding delays or timestamps (though not heavily used in this code)\n",
+    "- `typing`: Provides type hints to make the code clearer about what kind of data functions expect\n",
+    "- `requests`: Makes HTTP requests to web services (imported but not actively used here)\n",
+    "- `datetime`: Handles dates and times for timestamping conversations\n",
+    "\n",
+    "## The ChatbotManager Class: The Brain of the Operation\n",
+    "\n",
+    "The `ChatbotManager` class is like the control center that manages everything about the chatbot. Let's break it down:\n",
+    "\n",
+    "### Initialization Method\n",
+    "\n",
+    "```python\n",
+    "def __init__(self):\n",
+    "    self.conversation_history = []\n",
+    "    self.current_api_key = None\n",
+    "    self.current_model = \"gpt-3.5-turbo\"\n",
+    "    self.system_prompt = \"You are a helpful AI assistant. Respond in a friendly and informative manner.\"\n",
+    "    self.max_tokens = 150\n",
+    "    self.temperature = 0.7\n",
+    "```\n",
+    "\n",
+    "When a `ChatbotManager` object is created, it sets up default values:\n",
+    "- `conversation_history`: An empty list that will store all the chat messages\n",
+    "- `current_api_key`: Starts as None until the user provides their OpenAI API key\n",
+    "- `current_model`: Defaults to GPT-3.5-turbo (a fast, cost-effective model)\n",
+    "- `system_prompt`: The instructions that tell the AI how to behave\n",
+    "- `max_tokens`: Limits response length to 150 tokens (roughly 100-120 words)\n",
+    "- `temperature`: Controls creativity (0.7 means moderately creative responses)\n",
+    "\n",
+    "### API Key Validation Method\n",
+    "\n",
+    "```python\n",
+    "def set_api_key(self, api_key: str) -> str:\n",
+    "    if not api_key.strip():\n",
+    "        return \"❌ Please enter a valid API key\"\n",
+    "    \n",
+    "    self.current_api_key = api_key.strip()\n",
+    "    openai.api_key = self.current_api_key\n",
+    "    \n",
+    "    try:\n",
+    "        openai.Model.list()\n",
+    "        return \"✅ API key validated successfully!\"\n",
+    "    except Exception as e:\n",
+    "        return f\"❌ Invalid API key: {str(e)}\"\n",
+    "```\n",
+    "\n",
+    "This method is like a security guard checking if someone has the right credentials:\n",
+    "1. First, it checks if the API key isn't empty (using `strip()` to remove spaces)\n",
+    "2. If valid, it stores the key and tells the OpenAI library to use it\n",
+    "3. It then tests the key by trying to list available models\n",
+    "4. If the test succeeds, it returns a success message\n",
+    "5. If it fails, it catches the error and returns a failure message\n",
+    "\n",
+    "### Settings Update Method\n",
+    "\n",
+    "```python\n",
+    "def update_settings(self, model: str, system_prompt: str, max_tokens: int, temperature: float) -> str:\n",
+    "    self.current_model = model\n",
+    "    self.system_prompt = system_prompt\n",
+    "    self.max_tokens = max_tokens\n",
+    "    self.temperature = temperature\n",
+    "    return f\"✅ Settings updated: Model={model}, Max Tokens={max_tokens}, Temperature={temperature}\"\n",
+    "```\n",
+    "\n",
+    "This is straightforward - it updates all the chatbot's configuration settings and returns a confirmation message showing what was changed.\n",
+    "\n",
+    "### Custom Data Integration Method\n",
+    "\n",
+    "```python\n",
+    "def preprocess_data(self, data_text: str) -> str:\n",
+    "    if not data_text.strip():\n",
+    "        return \"No custom data provided\"\n",
+    "    \n",
+    "    base_prompt = \"You are a helpful AI assistant. Respond in a friendly and informative manner.\"\n",
+    "    self.system_prompt = base_prompt + f\"\\n\\nAdditional Context:\\n{data_text}\"\n",
+    "    return f\"✅ Custom data integrated ({len(data_text)} characters)\"\n",
+    "```\n",
+    "\n",
+    "This method allows users to add custom information to the chatbot's knowledge. It takes the default system prompt and appends the custom data, creating a more specialized AI assistant that knows about specific topics or company information.\n",
+    "\n",
+    "### The Core Response Generation Method\n",
+    "\n",
+    "```python\n",
+    "def generate_response(self, user_input: str, history: List[Tuple[str, str]]) -> Tuple[str, List[Tuple[str, str]]]:\n",
+    "    if not self.current_api_key:\n",
+    "        return \"❌ Please set your API key first!\", history\n",
+    "    \n",
+    "    if not user_input.strip():\n",
+    "        return \"Please enter a message.\", history\n",
+    "    \n",
+    "    try:\n",
+    "        messages = [{\"role\": \"system\", \"content\": self.system_prompt}]\n",
+    "        \n",
+    "        for user_msg, assistant_msg in history:\n",
+    "            messages.append({\"role\": \"user\", \"content\": user_msg})\n",
+    "            messages.append({\"role\": \"assistant\", \"content\": assistant_msg})\n",
+    "        \n",
+    "        messages.append({\"role\": \"user\", \"content\": user_input})\n",
+    "        \n",
+    "        response = openai.ChatCompletion.create(\n",
+    "            model=self.current_model,\n",
+    "            messages=messages,\n",
+    "            max_tokens=self.max_tokens,\n",
+    "            temperature=self.temperature,\n",
+    "            n=1,\n",
+    "            stop=None,\n",
+    "        )\n",
+    "        \n",
+    "        assistant_response = response.choices[0].message.content.strip()\n",
+    "        history.append((user_input, assistant_response))\n",
+    "        \n",
+    "        return assistant_response, history\n",
+    "        \n",
+    "    except Exception as e:\n",
+    "        error_msg = f\"❌ Error generating response: {str(e)}\"\n",
+    "        return error_msg, history\n",
+    "```\n",
+    "\n",
+    "This is the heart of the chatbot. Here's what happens step by step:\n",
+    "\n",
+    "1. **Safety Checks**: First, it verifies that an API key exists and the user actually typed something\n",
+    "2. **Message Formatting**: It creates a list of messages in the format OpenAI expects:\n",
+    "   - Starts with the system prompt (tells the AI how to behave)\n",
+    "   - Adds all previous conversation history in order\n",
+    "   - Adds the new user message\n",
+    "3. **API Call**: It sends all this information to OpenAI's servers using the specified model and settings\n",
+    "4. **Response Processing**: It extracts the AI's response and adds both the user input and AI response to the conversation history\n",
+    "5. **Error Handling**: If anything goes wrong, it catches the error and returns a helpful message\n",
+    "\n",
+    "### Utility Methods\n",
+    "\n",
+    "```python\n",
+    "def clear_conversation(self) -> Tuple[str, List[Tuple[str, str]]]:\n",
+    "    self.conversation_history = []\n",
+    "    return \"\", []\n",
+    "\n",
+    "def export_conversation(self, history: List[Tuple[str, str]]) -> Tuple[str, Optional[str]]:\n",
+    "    # Creates a JSON file with the conversation data\n",
+    "```\n",
+    "\n",
+    "These methods handle housekeeping tasks like clearing the chat and saving conversations to files.\n",
+    "\n",
+    "## Model Configuration\n",
+    "\n",
+    "```python\n",
+    "AVAILABLE_MODELS = [\n",
+    "    \"gpt-3.5-turbo\",\n",
+    "    \"gpt-3.5-turbo-16k\",\n",
+    "    \"gpt-4\",\n",
+    "    \"gpt-4-32k\",\n",
+    "    \"gpt-4-0613\",\n",
+    "    \"gpt-4-32k-0613\"\n",
+    "]\n",
+    "```\n",
+    "\n",
+    "This list defines which AI models users can choose from. Each has different capabilities:\n",
+    "- GPT-3.5 models are faster and cheaper\n",
+    "- GPT-4 models are more capable but slower and more expensive\n",
+    "- The numbers (16k, 32k) refer to how much text they can process at once\n",
+    "\n",
+    "## The User Interface Creation\n",
+    "\n",
+    "The `create_interface()` function builds the entire web interface using Gradio. Think of it as designing a website layout:\n",
+    "\n",
+    "### Main Structure\n",
+    "\n",
+    "```python\n",
+    "with gr.Blocks(title=\"LLM-Based Chatbot\", theme=gr.themes.Ocean()) as demo:\n",
+    "```\n",
+    "\n",
+    "This creates the main container with a title and applies a blue ocean theme for visual appeal.\n",
+    "\n",
+    "### Chat Interface Tab\n",
+    "\n",
+    "The first tab contains the main chat functionality:\n",
+    "\n",
+    "```python\n",
+    "chatbot_interface = gr.Chatbot(\n",
+    "    label=\"Conversation\",\n",
+    "    height=400,\n",
+    "    show_label=True,\n",
+    "    avatar_images=(\"user.png\", \"assistant.png\"),\n",
+    "    show_copy_button=True,\n",
+    "    bubble_full_width=False,\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "This creates the chat window where conversations appear, with customizable height, avatars for user and AI, and a copy button for responses.\n",
+    "\n",
+    "### Input Components\n",
+    "\n",
+    "```python\n",
+    "user_input = gr.Textbox(\n",
+    "    placeholder=\"Type your message here...\",\n",
+    "    scale=4,\n",
+    "    show_label=False,\n",
+    "    container=False\n",
+    ")\n",
+    "send_btn = gr.Button(\"📤 Send\", variant=\"primary\", scale=1)\n",
+    "```\n",
+    "\n",
+    "These create the text input box where users type messages and the send button to submit them.\n",
+    "\n",
+    "### Settings Panel\n",
+    "\n",
+    "The right side contains controls for configuring the chatbot:\n",
+    "- API key input (password type for security)\n",
+    "- Model selection dropdown\n",
+    "- Token limit slider\n",
+    "- Temperature slider for creativity control\n",
+    "- Status displays showing current settings\n",
+    "\n",
+    "### Advanced Settings Tab\n",
+    "\n",
+    "This tab provides more sophisticated configuration options:\n",
+    "- Custom system prompt editing\n",
+    "- Custom data integration\n",
+    "- Preset prompt buttons for different use cases\n",
+    "- Settings management buttons\n",
+    "\n",
+    "## Event Handling: Making It Interactive\n",
+    "\n",
+    "The code then connects user actions to functions using event handlers:\n",
+    "\n",
+    "```python\n",
+    "send_btn.click(\n",
+    "    handle_chat,\n",
+    "    inputs=[user_input, chatbot_interface],\n",
+    "    outputs=[chatbot_interface, user_input]\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "This tells Gradio: \"When the send button is clicked, run the `handle_chat` function with the current user input and chat history, then update the chat display and clear the input box.\"\n",
+    "\n",
+    "### Handler Functions\n",
+    "\n",
+    "The handler functions act as translators between the user interface and the ChatbotManager:\n",
+    "\n",
+    "```python\n",
+    "def handle_chat(user_input, history):\n",
+    "    if not user_input.strip():\n",
+    "        return history or [], \"\"\n",
+    "    \n",
+    "    response, updated_history = chatbot.generate_response(user_input, history or [])\n",
+    "    return updated_history, \"\"\n",
+    "```\n",
+    "\n",
+    "This function takes what the user typed, passes it to the chatbot manager, and returns the updated conversation to display in the interface.\n",
+    "\n",
+    "## Advanced Features\n",
+    "\n",
+    "### Preset System Prompts\n",
+    "\n",
+    "The application includes predefined prompts for different use cases:\n",
+    "- Customer Support: Professional, solution-focused responses\n",
+    "- Educational Tutor: Patient, explanatory teaching style\n",
+    "- Creative Assistant: Imaginative, inspiring writing help\n",
+    "- Technical Writer: Clear, precise documentation style\n",
+    "\n",
+    "### Data Integration\n",
+    "\n",
+    "Users can add custom data to make the chatbot an expert on specific topics, like company FAQs or technical documentation.\n",
+    "\n",
+    "### Conversation Export\n",
+    "\n",
+    "The system can save conversations as JSON files with timestamps and metadata for later analysis or record-keeping.\n",
+    "\n",
+    "## Launch Configuration\n",
+    "\n",
+    "```python\n",
+    "if __name__ == \"__main__\":\n",
+    "    demo = create_interface()\n",
+    "    demo.launch(share=True)\n",
+    "```\n",
+    "\n",
+    "This final section starts the application:\n",
+    "- Creates the interface\n",
+    "- Launches it with `share=True`, which creates a public URL that others can access\n",
+    "- The application runs on your computer but can be accessed from anywhere via the generated link\n",
+    "\n",
+    "## How It All Works Together\n",
+    "\n",
+    "When you run this code, here's the complete flow:\n",
+    "\n",
+    "1. **Startup**: The application creates a ChatbotManager instance and builds the web interface\n",
+    "2. **User Setup**: User enters their OpenAI API key, which gets validated\n",
+    "3. **Configuration**: User can adjust model settings, system prompts, and add custom data\n",
+    "4. **Conversation**: User types a message, clicks send\n",
+    "5. **Processing**: The system formats the message with conversation history and sends it to OpenAI\n",
+    "6. **Response**: OpenAI returns a response, which gets displayed in the chat interface\n",
+    "7. **Continuation**: The conversation continues with full context maintained\n",
+    "\n",
+    "This creates a fully functional, customizable chatbot interface that rivals commercial applications while giving users complete control over the AI's behavior and capabilities."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07498f29",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}