main.py

import asyncio
from fastapi import FastAPI, HTTPException, Query
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
import uuid
import uvicorn
from typing import List, Optional
import traceback

# Import your existing modules
from database import db
from models import ChatMessage, ChatRequest, ChatResponse, Product, SearchRequest, Conversation, KnowledgeDocument, Document, SourceInfo
from config import settings
from rag_system import rag_pipeline

app = FastAPI(
    title="RAG Chatbot API",
    description="Lightweight RAG Chatbot using MongoDB Atlas and Gemini",
    version="1.0.0"
)

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)
embeddings_generated = False


@app.on_event("startup")
async def startup_event():
    """Run on application startup - WITHOUT embedding generation"""
    try:
        print("🚀 Starting RAG Chatbot API...")
        
        # Initialize database connection
        await db.connect()
        
        # Check database status (but don't generate embeddings)
        stats = await db.get_collection_stats()
        print(f"📊 Database status: {stats}")
        
        if stats["documents_with_embeddings"] == 0:
            print("⚠️  No embeddings found in database. Please pre-compute embeddings separately.")
            print("💡 Run the embedding generation script locally and upload to MongoDB Atlas.")
        else:
            print(f"✅ Ready! Using {stats['documents_with_embeddings']} documents with embeddings from MongoDB Atlas")
            
        print("✅ RAG Chatbot API is ready!")
        
    except Exception as e:
        print(f"❌ Startup error: {e}")
        raise

@app.get("/")
async def root():
    return {"message": "RAG Chatbot API is running!", "status": "healthy"}

@app.get("/health")
async def health_check():
    return {"status": "healthy", "service": "rag-chatbot"}

@app.post("/chat")
async def chat_with_assistant(request: ChatRequest):
    """Main chat endpoint for product queries"""
    try:
        print(f"💬 Received chat request: {request.message}")
        response, sources = await rag_pipeline.generate_response(request.message)
        
        suggested_questions = rag_pipeline.generate_followup_questions(
            request.message, 
            sources
        )
        
        # Convert to SourceInfo objects
        source_objects = []
        for product in sources:
            source_objects.append(SourceInfo(
                id=product.get("id", ""),
                name=product.get("source", "Product"),
                category=product.get("metadata", {}).get("category", "N/A"),
                price=str(product.get("metadata", {}).get("price", "N/A")),
                similarity_score=product.get("metadata", {}).get("similarity_score", 0)
            ))
        
        return ChatResponse(
            response=response,
            sources=source_objects,
            suggested_questions=suggested_questions,
            conversation_id=request.conversation_id
        )
    except Exception as e:
        print(f"❌ Error in /chat endpoint: {traceback.format_exc()}")
        raise HTTPException(status_code=500, detail=f"Error processing request: {str(e)}")

@app.get("/debug/database-stats")
async def debug_database_stats():
    """Get detailed database statistics"""
    try:
        stats = await db.get_collection_stats()
        
        # Sample some documents to see their structure
        sample_docs = []
        cursor = db.collection.find({"embedding": {"$exists": True}}).limit(3)
        async for doc in cursor:
            sample_docs.append({
                "id": str(doc["_id"]),
                "title": doc.get("title", "N/A"),
                "category": doc.get("category", "N/A"),
                "has_embedding": "embedding" in doc,
                "embedding_length": len(doc.get("embedding", [])),
                "content_preview": f"{doc.get('title', '')} - {doc.get('product_description', '')[:50]}..."
            })
        
        return {
            "database": settings.DATABASE_NAME,
            "collection": settings.COLLECTION_NAME,
            "statistics": stats,
            "sample_documents_with_embeddings": sample_docs
        }
    except Exception as e:
        return {"error": str(e)}

    
if __name__ == "__main__":
    uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=True)