main.py

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

import chromadb
from cashews import cache
from fastapi import FastAPI, HTTPException, Query
from httpx import AsyncClient
from huggingface_hub import DatasetCard
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=1000")

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

async_client = AsyncClient(
    follow_redirects=True,
)


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")


async def try_get_card(hub_id: str) -> Optional[str]:
    try:
        response = await async_client.get(
            f"https://huggingface.co/datasets/{hub_id}/raw/main/README.md"
        )
        if response.status_code == 200:
            card = DatasetCard(response.text)
            return card.text
    except Exception as e:
        logger.error(f"Error fetching card for hub_id {hub_id}: {str(e)}")
        return None


@app.get("/similar", response_model=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.get("embeddings"):
            logger.info(f"Dataset not found: {dataset_id}")
            try:
                embedding_function = get_embedding_function()
                card = await try_get_card(dataset_id)
                if card is None:
                    return QueryResponse(message="No dataset card available for recommendations.")
                embeddings = embedding_function(card)
                collection.upsert(ids=[dataset_id], embeddings=embeddings[0])
                logger.info(f"Dataset {dataset_id} added to collection")
                result = collection.get(ids=[dataset_id], include=["embeddings"])
            except Exception as e:
                logger.error(
                    f"Error adding dataset {dataset_id} to collection: {str(e)}"
                )
                return QueryResponse(message="No dataset card available for recommendations.")

        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 QueryResponse(message="No similar datasets found.")

        # 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))

if __name__ == "__main__":
    import uvicorn

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