import gradio as gr
import os
from pipeline.rag_pipeline import RAGPipeline
from dotenv import load_dotenv

load_dotenv()

# Initialize pipeline
print("Initializing RAG Pipeline...")
pipeline = RAGPipeline()

def chat_fn(message, history):
    try:
        # Pipeline expects message, history is handled by ChatInterface
        result = pipeline.answer(message)
        answer = result["answer"]
        
        # We can also append sources to the answer if we want
        sources = [c["doc_id"] for c in result["chunks"]]
        if sources:
            answer += f"\n\n---\n**Sources:** {', '.join(set(sources))}"
            
        return answer
    except Exception as e:
        return f"Error: {str(e)}"

# Customizing ChatInterface for a better look
demo = gr.ChatInterface(
    fn=chat_fn,
    title="🐍 Python RAG Assistant",
    description="Ask questions about Python documentation. This assistant uses Hybrid Search and Reranking for high-accuracy retrieval.",
    examples=[
        "What are mutable default arguments in Python?",
        "How does Python's garbage collection work?",
        "Explain the difference between multiprocessing and multithreading.",
        "How do closures work in Python?"
    ],
    cache_examples=False,
)

if __name__ == "__main__":
    # server_name="0.0.0.0" allows access within a local network
    # share=False avoids Windows Defender false positive blocks
    demo.launch(
        server_name="0.0.0.0", 
        server_port=7860, 
        share=True,
        theme=gr.themes.Soft(primary_hue="emerald", secondary_hue="slate")
    )