import logging
from contextlib import asynccontextmanager
from typing import List, Optional

import chromadb
from cashews import cache
from fastapi import FastAPI, HTTPException, Query
from pydantic import BaseModel
from starlette.responses import RedirectResponse

from load_data import get_embedding_function, get_save_path, refresh_data

# Set up logging
logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)

# Set up caching
cache.setup("mem://?check_interval=10&size=10000")

# Initialize Chroma client
SAVE_PATH = get_save_path()
client = chromadb.PersistentClient(path=SAVE_PATH)
collection = None


class QueryResult(BaseModel):
    dataset_id: str
    similarity: float


class QueryResponse(BaseModel):
    results: List[QueryResult]


@asynccontextmanager
async def lifespan(app: FastAPI):
    global collection
    # Startup: refresh data and initialize collection
    logger.info("Starting up the application")
    try:
        # Create or get the collection
        embedding_function = get_embedding_function()
        collection = client.get_or_create_collection(
            name="dataset_cards", embedding_function=embedding_function
        )
        logger.info("Collection initialized successfully")

        # Refresh data
        refresh_data()
        logger.info("Data refresh completed successfully")
    except Exception as e:
        logger.error(f"Error during startup: {str(e)}")
        raise

    yield  # Here the app is running and handling requests

    # Shutdown: perform any cleanup
    logger.info("Shutting down the application")
    # Add any cleanup code here if needed


app = FastAPI(lifespan=lifespan)


@app.get("/", include_in_schema=False)
def root():
    return RedirectResponse(url="/docs")


@app.get("/query", response_model=Optional[QueryResponse])
@cache(ttl="1h")
async def api_query_dataset(dataset_id: str, n: int = Query(default=10, ge=1, le=100)):
    try:
        logger.info(f"Querying dataset: {dataset_id}")
        # Get the embedding for the given dataset_id
        result = collection.get(ids=[dataset_id], include=["embeddings"])

        if not result["embeddings"]:
            logger.info(f"Dataset not found: {dataset_id}")
            raise HTTPException(status_code=404, detail="Dataset not found")

        embedding = result["embeddings"][0]

        # Query the collection for similar datasets
        query_result = collection.query(
            query_embeddings=[embedding], n_results=n, include=["distances"]
        )

        if not query_result["ids"]:
            logger.info(f"No similar datasets found for: {dataset_id}")
            return None

        # Prepare the response
        results = [
            QueryResult(dataset_id=id, similarity=1 - distance)
            for id, distance in zip(
                query_result["ids"][0], query_result["distances"][0]
            )
        ]

        logger.info(f"Found {len(results)} similar datasets for: {dataset_id}")
        return QueryResponse(results=results)

    except Exception as e:
        logger.error(f"Error querying dataset {dataset_id}: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e)) from e


if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=8000)