app.py

import gradio as gr
import openai
import json
import os
import time
from typing import List, Tuple, Optional
import requests
from datetime import datetime

class ChatbotManager:
    def __init__(self):
        self.conversation_history = []
        self.current_api_key = None
        self.current_model = "gpt-3.5-turbo"
        self.system_prompt = "You are a helpful AI assistant. Respond in a friendly and informative manner."
        self.max_tokens = 150
        self.temperature = 0.7
        
    def set_api_key(self, api_key: str) -> str:
        """Set the OpenAI API key"""
        if not api_key.strip():
            return "❌ Please enter a valid API key"
        
        self.current_api_key = api_key.strip()
        openai.api_key = self.current_api_key
        
        # Test the API key
        try:
            openai.Model.list()
            return "✅ API key validated successfully!"
        except Exception as e:
            return f"❌ Invalid API key: {str(e)}"
    
    def update_settings(self, model: str, system_prompt: str, max_tokens: int, temperature: float) -> str:
        """Update chatbot settings"""
        self.current_model = model
        self.system_prompt = system_prompt
        self.max_tokens = max_tokens
        self.temperature = temperature
        return f"✅ Settings updated: Model={model}, Max Tokens={max_tokens}, Temperature={temperature}"
    
    def preprocess_data(self, data_text: str) -> str:
        """Preprocess and integrate custom data into the system prompt"""
        if not data_text.strip():
            return "No custom data provided"
        
        # Add custom data to system prompt
        self.system_prompt += f"\n\nAdditional Context:\n{data_text}"
        return f"✅ Custom data integrated ({len(data_text)} characters)"
    
    def generate_response(self, user_input: str, history: List[Tuple[str, str]]) -> Tuple[str, List[Tuple[str, str]]]:
        """Generate response using the selected LLM model"""
        if not self.current_api_key:
            return "❌ Please set your API key first!", history
        
        if not user_input.strip():
            return "Please enter a message.", history
        
        try:
            # Prepare conversation context
            messages = [{"role": "system", "content": self.system_prompt}]
            
            # Add conversation history
            for user_msg, assistant_msg in history:
                messages.append({"role": "user", "content": user_msg})
                messages.append({"role": "assistant", "content": "🤖 " + assistant_msg})
            
            # Add current user input
            messages.append({"role": "user", "content": user_input})
            
            # Generate response
            response = openai.ChatCompletion.create(
                model=self.current_model,
                messages=messages,
                max_tokens=self.max_tokens,
                temperature=self.temperature,
                n=1,
                stop=None,
            )
            
            assistant_response = response.choices[0].message.content.strip()
            
            # Update history
            history.append((user_input, assistant_response))
            
            return assistant_response, history
            
        except Exception as e:
            error_msg = f"❌ Error generating response: {str(e)}"
            return error_msg, history
    
    def clear_conversation(self) -> Tuple[str, List[Tuple[str, str]]]:
        """Clear conversation history"""
        self.conversation_history = []
        return "", []
    
    def export_conversation(self, history: List[Tuple[str, str]]) -> str:
        """Export conversation history to JSON format"""
        if not history:
            return "No conversation to export"
        
        export_data = {
            "timestamp": datetime.now().isoformat(),
            "model": self.current_model,
            "conversation": [
                {"user": user_msg, "assistant": assistant_msg}
                for user_msg, assistant_msg in history
            ]
        }
        
        filename = f"conversation_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
        
        try:
            with open(filename, 'w', encoding='utf-8') as f:
                json.dump(export_data, f, indent=2, ensure_ascii=False)
            return f"✅ Conversation exported to {filename}"
        except Exception as e:
            return f"❌ Export failed: {str(e)}"

# Initialize chatbot manager
chatbot = ChatbotManager()

# Define available models
AVAILABLE_MODELS = [
    "gpt-3.5-turbo",
    "gpt-3.5-turbo-16k",
    "gpt-4",
    "gpt-4-32k",
    "gpt-4-turbo-preview",
    "gpt-4o",
    "gpt-4o-mini"
]

def create_interface():
    """Create the Gradio interface"""
    
    with gr.Blocks(title="LLM-Based Chatbot", theme=gr.themes.Soft()) as demo:
        gr.Markdown("""
        # 🤖 LLM-Based Conversational AI Chatbot
        
        This chatbot leverages powerful Language Models to provide intelligent conversations.
        Enter your OpenAI API key to get started!
        """)
        
        with gr.Tab("💬 Chat Interface"):
            with gr.Row():
                with gr.Column(scale=3):
                    chatbot_interface = gr.Chatbot(
                        label="Conversation",
                        height=400,
                        show_label=True,
                        avatar_images=("👤", "🤖")
                    )
                    
                    with gr.Row():
                        user_input = gr.Textbox(
                            placeholder="Type your message here...",
                            scale=4,
                            show_label=False,
                            show_copy_button=True
                        )
                        send_btn = gr.Button("Send", variant="primary", scale=1)
                    
                    with gr.Row():
                        clear_btn = gr.Button("Clear Chat", variant="secondary")
                        export_btn = gr.Button("Export Chat", variant="secondary")
                
                with gr.Column(scale=1):
                    gr.Markdown("### 🔧 Quick Settings")
                    
                    api_key_input = gr.Textbox(
                        label="OpenAI API Key",
                        placeholder="sk-...",
                        type="password"
                    )
                    api_status = gr.Textbox(
                        label="API Status",
                        interactive=False,
                        value="❌ No API key provided"
                    )
                    
                    model_dropdown = gr.Dropdown(
                        choices=AVAILABLE_MODELS,
                        value="gpt-3.5-turbo",
                        label="Model"
                    )
                    
                    max_tokens_slider = gr.Slider(
                        minimum=50,
                        maximum=500,
                        value=150,
                        step=10,
                        label="Max Tokens"
                    )
                    
                    temperature_slider = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=0.7,
                        step=0.1,
                        label="Temperature"
                    )
        
        with gr.Tab("⚙️ Advanced Settings"):
            gr.Markdown("### System Prompt Configuration")
            system_prompt_input = gr.Textbox(
                label="System Prompt",
                value="You are a helpful AI assistant. Respond in a friendly and informative manner.",
                lines=5,
                placeholder="Enter custom system prompt..."
            )
            
            gr.Markdown("### 📊 Custom Data Integration")
            custom_data_input = gr.Textbox(
                label="Custom Training Data",
                lines=10,
                placeholder="Enter custom data, FAQs, or domain-specific information..."
            )
            
            with gr.Row():
                update_settings_btn = gr.Button("Update Settings", variant="primary")
                integrate_data_btn = gr.Button("Integrate Custom Data", variant="secondary")
            
            settings_status = gr.Textbox(
                label="Settings Status",
                interactive=False
            )
        
        with gr.Tab("📋 Usage Guide"):
            gr.Markdown("""
            ## 🚀 Getting Started
            
            ### 1. **Set Up API Key**
            - Obtain an OpenAI API key from [OpenAI Platform](https://platform.openai.com/)
            - Enter your API key in the "OpenAI API Key" field
            - Wait for the green checkmark confirmation
            
            ### 2. **Configure Settings**
            - **Model**: Choose from available GPT models
            - **Max Tokens**: Control response length (50-500)
            - **Temperature**: Adjust creativity (0.0 = focused, 1.0 = creative)
            
            ### 3. **Advanced Customization**
            - **System Prompt**: Define the AI's personality and behavior
            - **Custom Data**: Add domain-specific information or FAQs
            
            ### 4. **Chat Features**
            - Type messages and get intelligent responses
            - Clear conversation history anytime
            - Export chat history as JSON
            
            ## 🛠️ Technical Features
            
            - **Multi-model support**: GPT-3.5, GPT-4, and variants
            - **Conversation memory**: Maintains context throughout the session
            - **Custom data integration**: Enhance responses with your own data
            - **Export functionality**: Save conversations for later analysis
            - **Real-time validation**: API key and settings verification
            
            ## 💡 Use Cases
            
            - **Customer Support**: Create domain-specific support chatbots
            - **Education**: Build tutoring assistants with custom curriculum
            - **Business**: Develop FAQ bots with company-specific information
            - **Research**: Analyze conversations and response patterns
            """)
        
        # Event handlers
        def handle_api_key(api_key):
            status = chatbot.set_api_key(api_key)
            return status
        
        def handle_chat(user_input, history):
            if not user_input.strip():
                return history, ""
            
            response, updated_history = chatbot.generate_response(user_input, history)
            return updated_history, ""
        
        def handle_settings_update(model, system_prompt, max_tokens, temperature):
            status = chatbot.update_settings(model, system_prompt, max_tokens, temperature)
            return status
        
        def handle_data_integration(custom_data):
            status = chatbot.preprocess_data(custom_data)
            return status
        
        def handle_clear():
            return chatbot.clear_conversation()
        
        def handle_export(history):
            return chatbot.export_conversation(history)
        
        # Connect events
        api_key_input.change(
            handle_api_key,
            inputs=[api_key_input],
            outputs=[api_status]
        )
        
        send_btn.click(
            handle_chat,
            inputs=[user_input, chatbot_interface],
            outputs=[chatbot_interface, user_input]
        )
        
        user_input.submit(
            handle_chat,
            inputs=[user_input, chatbot_interface],
            outputs=[chatbot_interface, user_input]
        )
        
        update_settings_btn.click(
            handle_settings_update,
            inputs=[model_dropdown, system_prompt_input, max_tokens_slider, temperature_slider],
            outputs=[settings_status]
        )
        
        integrate_data_btn.click(
            handle_data_integration,
            inputs=[custom_data_input],
            outputs=[settings_status]
        )
        
        clear_btn.click(
            handle_clear,
            outputs=[user_input, chatbot_interface]
        )
        
        export_btn.click(
            handle_export,
            inputs=[chatbot_interface],
            outputs=[settings_status]
        )
    
    return demo

# Requirements and setup instructions
def print_setup_instructions():
    """Print setup instructions"""
    print("""
    🤖 LLM-Based Chatbot Setup Instructions
    =====================================
    
    📦 Required Dependencies:
    pip install gradio openai requests
    
    🔑 API Key Setup:
    1. Visit https://platform.openai.com/
    2. Create an account and generate an API key
    3. Enter the API key in the interface
    
    🚀 Running the Application:
    python app.py
    
    📂 Files Created:
    - conversation_YYYYMMDD_HHMMSS.json (exported chats)
    """)

if __name__ == "__main__":
    print_setup_instructions()
    
    # Create and launch the interface
    demo = create_interface()
    
    # Launch with custom settings
    demo.launch(
        share=True            
    )