main.py

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

import chromadb
from cashews import cache
from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction
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 starlette.status import (
    HTTP_403_FORBIDDEN,
    HTTP_404_NOT_FOUND,
    HTTP_500_INTERNAL_SERVER_ERROR,
)

from load_card_data import card_embedding_function, refresh_card_data
from load_viewer_data import refresh_viewer_data
from utils import get_save_path, get_collection, get_chroma_client

# 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
client = get_chroma_client()

async_client = AsyncClient(
    follow_redirects=True,
)


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Startup: refresh data and initialize collection
    logger.info("Starting up the application")
    try:
        # Refresh data
        logger.info("Starting refresh of card data")
        refresh_card_data()
        logger.info("Card data refresh completed")
        logger.info("Starting refresh of viewer data")
        await refresh_viewer_data()
        logger.info("Viewer data refresh completed")
        logger.info("Data refresh completed successfully")
    except Exception as e:
        logger.error(f"Error during startup: {str(e)}")
        logger.warning("Application starting with potential data issues")
    yield

    # 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


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


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


class DatasetCardNotFoundError(HTTPException):
    def __init__(self, dataset_id: str):
        super().__init__(
            status_code=HTTP_404_NOT_FOUND,
            detail=f"No dataset card available for dataset: {dataset_id}",
        )


class DatasetNotForAllAudiencesError(HTTPException):
    def __init__(self, dataset_id: str):
        super().__init__(
            status_code=HTTP_403_FORBIDDEN,
            detail=f"Dataset {dataset_id} is not for all audiences and not supported in this service.",
        )


@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)):
    embedding_function = card_embedding_function()
    collection = get_collection(client, embedding_function, "dataset_cards")
    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:
                card = await try_get_card(dataset_id)
                if card is None:
                    raise DatasetCardNotFoundError(dataset_id)
                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"])
                if result.get("not-for-all-audiences"):
                    raise DatasetNotForAllAudiencesError(dataset_id)
            except (DatasetCardNotFoundError, DatasetNotForAllAudiencesError):
                raise
            except Exception as e:
                logger.error(
                    f"Error adding dataset {dataset_id} to collection: {str(e)}"
                )
                raise DatasetCardNotFoundError(dataset_id) from e

        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}")
            raise HTTPException(
                status_code=HTTP_404_NOT_FOUND, detail="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 (HTTPException, DatasetCardNotFoundError):
        raise
    except Exception as e:
        logger.error(f"Error querying dataset {dataset_id}: {str(e)}")
        raise HTTPException(
            status_code=HTTP_500_INTERNAL_SERVER_ERROR,
            detail="An unexpected error occurred.",
        ) from e


@app.get("/similar-text", response_model=QueryResponse)
@cache(ttl="1h")
async def api_query_by_text(query: str, n: int = Query(default=10, ge=1, le=100)):
    try:
        logger.info(f"Querying datasets by text: {query}")
        collection = client.get_collection(
            name="dataset_cards", embedding_function=card_embedding_function()
        )
        print(query)
        query_result = collection.query(
            query_texts=query, n_results=n, include=["distances"]
        )
        print(query_result)

        if not query_result["ids"]:
            logger.info(f"No similar datasets found for query: {query}")
            raise HTTPException(
                status_code=HTTP_404_NOT_FOUND, detail="No similar datasets found."
            )

        # Prepare the response
        results = [
            QueryResult(dataset_id=str(id), similarity=float(1 - distance))
            for id, distance in zip(
                query_result["ids"][0], query_result["distances"][0]
            )
        ]
        logger.info(f"Found {len(results)} similar datasets for query: {query}")
        return QueryResponse(results=results)

    except Exception as e:
        logger.error(f"Error querying datasets by text {query}: {str(e)}")
        raise HTTPException(
            status_code=HTTP_500_INTERNAL_SERVER_ERROR,
            detail="An unexpected error occurred.",
        ) from e


@app.get("/search-viewer", response_model=QueryResponse)
@cache(ttl="1h")
async def api_search_viewer(query: str, n: int = Query(default=10, ge=1, le=100)):
    try:
        embedding_function = SentenceTransformerEmbeddingFunction(
            model_name="davanstrien/query-to-dataset-viewer-descriptions",
            trust_remote_code=True,
        )
        collection = client.get_collection(
            name="dataset-viewer-descriptions",
            embedding_function=embedding_function,
        )
        query = f"USER_QUERY: {query}"
        query_result = collection.query(
            query_texts=query, n_results=n, include=["distances"]
        )
        print(query_result)

        if not query_result["ids"]:
            logger.info(f"No similar datasets found for query: {query}")
            raise HTTPException(
                status_code=HTTP_404_NOT_FOUND, detail="No similar datasets found."
            )

        # Prepare the response
        results = [
            QueryResult(dataset_id=str(id), similarity=float(1 - distance))
            for id, distance in zip(
                query_result["ids"][0], query_result["distances"][0]
            )
        ]
        logger.info(f"Found {len(results)} similar datasets for query: {query}")
        return QueryResponse(results=results)

    except Exception as e:
        logger.error(f"Error querying datasets by text {query}: {str(e)}")
        raise HTTPException(
            status_code=HTTP_500_INTERNAL_SERVER_ERROR,
            detail="An unexpected error occurred.",
        ) from e


if __name__ == "__main__":
    import uvicorn

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