app.py

import gradio as gr
import openai
import os
from typing import List, Tuple
from config import Config
from pdf_processor import PDFProcessor

# Validate configuration
try:
    Config.validate()
except ValueError as e:
    print(f"Configuration Error: {e}")
    exit(1)

# Initialize OpenAI client
client = openai.OpenAI(
    api_key=Config.OPENAI_API_KEY
)

# Initialize PDF processor
pdf_processor = PDFProcessor()

# Try to load existing vector store, otherwise process PDF
if not pdf_processor.load_vector_store():
    print("🔄 Processing PDF for the first time...")
    if pdf_processor.process_pdf():
        pdf_processor.save_vector_store()
    else:
        print("⚠️ PDF processing failed. Chatbot will work without PDF knowledge.")

def chat_with_bot(message: str, history: List[Tuple[str, str]]) -> Tuple[str, List[Tuple[str, str]]]:
    """
    Chat function that handles conversation with OpenAI API and PDF knowledge
    """
    if not message.strip():
        return "", history
    
    # Search for relevant PDF content
    pdf_context = ""
    try:
        if pdf_processor.vector_store:
            relevant_chunks = pdf_processor.search_similar_content(message, k=2)
            if relevant_chunks:
                pdf_context = "\n\nRelevant information from the Health Tech Hub Copenhagen document:\n" + "\n".join(relevant_chunks)
    except Exception as e:
        print(f"Warning: Could not search PDF content: {e}")
    
    # Prepare conversation history for OpenAI
    messages = [
        {"role": "system", "content": Config.SYSTEM_PROMPT + "\n\nYou have access to information about Health Tech Hub Copenhagen. Use this information when relevant to answer questions."}
    ]
    
    # Add conversation history
    for human, assistant in history:
        messages.append({"role": "user", "content": human})
        messages.append({"role": "assistant", "content": assistant})
    
    # Add current message with PDF context
    full_message = message
    if pdf_context:
        full_message = f"{message}\n\n{pdf_context}"
    
    messages.append({"role": "user", "content": full_message})
    
    try:
        # Get response from OpenAI
        response = client.chat.completions.create(
            model=Config.OPENAI_MODEL,
            messages=messages,
            max_tokens=Config.MAX_TOKENS,
            temperature=Config.TEMPERATURE
        )
        
        assistant_response = response.choices[0].message.content
        
        # Update history
        history.append((message, assistant_response))
        
        return "", history
        
    except Exception as e:
        error_message = f"Sorry, I encountered an error: {str(e)}"
        history.append((message, error_message))
        return "", history

def clear_chat():
    """Clear the chat history"""
    return []

# Create Gradio interface
with gr.Blocks(
    title=Config.GRADIO_TITLE,
    theme=gr.themes.Soft(),
    css="""
    .gradio-container {
        max-width: 800px;
        margin: auto;
    }
    """
) as demo:
    gr.Markdown(
        """
        # 🤖 AI Chatbot
        
        Welcome! I'm your AI assistant. Feel free to ask me anything!
        
        ---
        """
    )
    
    # Chat interface
    chatbot = gr.Chatbot(
        height=Config.GRADIO_HEIGHT,
        show_label=False,
        container=True,
        bubble_full_width=False
    )
    
    # Message input
    msg = gr.Textbox(
        placeholder="Type your message here...",
        show_label=False,
        container=False
    )
    
    # Clear button
    clear = gr.Button("Clear Chat", variant="secondary")
    
    # Set up event handlers
    msg.submit(
        chat_with_bot,
        inputs=[msg, chatbot],
        outputs=[msg, chatbot]
    )
    
    clear.click(
        clear_chat,
        outputs=chatbot
    )

# Launch the app
if __name__ == "__main__":
    demo.launch()