from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import sys
import os
import logging

# Add the parent directory to sys.path so we can import the chat functionality
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

# Import and validate configuration on startup
from secure_config import validate_config
validate_config()

from chat_openai import query_qdrant, format_response
from ingest_local_docs import ingest_local_docs  # <- only import the main function, no circular import

# Create FastAPI app
app = FastAPI(
    title="AI Assistant Backend",
    description="Backend API for Docusaurus AI Assistant"
)

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=[
        "http://localhost:3000",
        "http://localhost:5173",
        "http://127.0.0.1:5173",
        "http://localhost:3001",
        "http://127.0.0.1:8000",
        "http://localhost:8000",
        "https://*.vercel.app"
    ],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Define request/response models
class ChatRequest(BaseModel):
    query: str

class ChatResponse(BaseModel):
    response: str

# ======================
# Startup Event
# ======================
@app.on_event("startup")
async def startup_event():
    """Ensure Qdrant collection exists and ingest docs if empty"""
    try:
        print("Starting ingestion process on startup...")
        # This will safely create collection if needed and ingest docs
        documents_indexed = ingest_local_docs()
        print(f"Ingestion completed. Total chunks stored: {documents_indexed}")
    except Exception as e:
        logging.error(f"Startup ingestion failed: {e}")
        # Continue running backend even if ingestion fails

# ======================
# Basic endpoints
# ======================
@app.get("/")
def read_root():
    return {"message": "AI Assistant Backend is running!"}

@app.get("/health")
def health_check():
    return {"status": "ok", "service": "backend", "errors": False}

# ======================
# Chat endpoint
# ======================
@app.post("/chat", response_model=ChatResponse)
async def chat_endpoint(request: ChatRequest):
    try:
        query = request.query
        if not query:
            raise HTTPException(status_code=400, detail="Query is required")

        # Query Qdrant database
        try:
            search_results = query_qdrant(query)
        except Exception as e:
            logging.error(f"Error querying Qdrant: {str(e)}")
            raise HTTPException(
                status_code=500,
                detail=f"Unable to connect to the knowledge base: {str(e)}"
            )

        # Format the response
        try:
            response = format_response(query, search_results)
        except Exception as e:
            logging.error(f"Error formatting response: {str(e)}")
            raise HTTPException(
                status_code=500,
                detail=f"Error generating response: {str(e)}"
            )

        return ChatResponse(response=response)

    except HTTPException:
        raise
    except Exception as e:
        logging.error(f"Unexpected error in chat endpoint: {str(e)}")
        raise HTTPException(
            status_code=500,
            detail=f"An unexpected error occurred: {str(e)}"
        )

# ======================
# Admin endpoints
# ======================
@app.post("/admin/ingest")
async def admin_ingest():
    """Manual ingestion endpoint"""
    try:
        documents_indexed = ingest_local_docs()
        return {
            "status": "success",
            "message": "Documents ingested successfully",
            "documents_indexed": documents_indexed
        }
    except Exception as e:
        logging.error(f"Error during manual ingestion: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error during ingestion: {str(e)}")

@app.get("/admin/status")
async def admin_status():
    """Check collection status"""
    try:
        from qdrant_client import QdrantClient
        from config import QDRANT_URL, QDRANT_API_KEY, COLLECTION_NAME

        qdrant_client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY, prefer_grpc=False)
        collection_info = qdrant_client.get_collection(collection_name=COLLECTION_NAME)
        point_count = getattr(collection_info, "points_count", 0)
        return {"collection_exists": True, "point_count": point_count, "status": "healthy"}
    except Exception as e:
        return {"collection_exists": False, "point_count": 0, "status": "missing", "error": str(e)}

@app.get("/debug/docs")
async def debug_docs():
    """Check if docs directory exists and its contents"""
    docs_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'docs')
    if os.path.exists(docs_path):
        files = os.listdir(docs_path)
        return {"docs_exists": True, "file_count": len(files), "files": files}
    else:
        return {"docs_exists": False, "file_count": 0, "files": []}

# ======================
# Run Uvicorn
# ======================
if __name__ == "__main__":
    import uvicorn
    port = int(os.environ.get("PORT", 8000))
    uvicorn.run(app, host="0.0.0.0", port=port)