import chromadb
import platform
import polars as pl
from chromadb.utils import embedding_functions
from typing import List, Tuple, Optional, Literal
from huggingface_hub import InferenceClient
from tqdm.contrib.concurrent import thread_map
from dotenv import load_dotenv
import os
from datetime import datetime
import stamina
import requests
import logging

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

load_dotenv()

# Top-level module variables
HF_TOKEN = os.getenv("HF_TOKEN")
EMBEDDING_MODEL_NAME = "Snowflake/snowflake-arctic-embed-m-long"
INFERENCE_MODEL_URL = (
    "https://pqzap00ebpl1ydt4.us-east-1.aws.endpoints.huggingface.cloud"
)
DATASET_PARQUET_URL = "hf://datasets/librarian-bots/dataset_cards_with_metadata_with_embeddings/data/train-00000-of-00001.parquet"
COLLECTION_NAME = "dataset_cards"
MAX_EMBEDDING_LENGTH = 8192


def get_save_path() -> Literal["chroma/"] | Literal["/data/chroma/"]:
    path = "chroma/" if platform.system() == "Darwin" else "/data/chroma/"
    logger.info(f"Using save path: {path}")
    return path


SAVE_PATH = get_save_path()


def get_chroma_client():
    logger.info("Initializing Chroma client")
    return chromadb.PersistentClient(path=SAVE_PATH)


def get_embedding_function():
    logger.info(f"Initializing embedding function with model: {EMBEDDING_MODEL_NAME}")
    return embedding_functions.SentenceTransformerEmbeddingFunction(
        model_name=EMBEDDING_MODEL_NAME, trust_remote_code=True
    )


def get_collection(chroma_client, embedding_function):
    logger.info(f"Getting or creating collection: {COLLECTION_NAME}")
    return chroma_client.create_collection(
        name=COLLECTION_NAME, get_or_create=True, embedding_function=embedding_function
    )


def get_last_modified_in_collection(collection) -> datetime | None:
    logger.info("Fetching last modified date from collection")
    all_items = collection.get(include=["metadatas"])
    if last_modified := [
        datetime.fromisoformat(item["last_modified"]) for item in all_items["metadatas"]
    ]:
        last_mod = max(last_modified)
        logger.info(f"Last modified date: {last_mod}")
        return last_mod
    else:
        logger.info("No last modified date found")
        return None


def parse_markdown_column(
    df: pl.DataFrame, markdown_column: str, dataset_id_column: str
) -> pl.DataFrame:
    logger.info("Parsing markdown column")
    return df.with_columns(
        parsed_markdown=(
            pl.col(markdown_column)
            .str.extract(r"(?s)^---.*?---\s*(.*)", group_index=1)
            .fill_null(pl.col(markdown_column))
            .str.strip_chars()
        ),
        prepended_markdown=(
            pl.concat_str(
                [
                    pl.lit("Dataset ID "),
                    pl.col(dataset_id_column).cast(pl.Utf8),
                    pl.lit("\n\n"),
                    pl.col(markdown_column)
                    .str.extract(r"(?s)^---.*?---\s*(.*)", group_index=1)
                    .fill_null(pl.col(markdown_column))
                    .str.strip_chars(),
                ]
            )
        ),
    )


def load_cards(
    min_len: int = 50,
    min_likes: int | None = None,
    last_modified: Optional[datetime] = None,
) -> Optional[Tuple[List[str], List[str], List[datetime]]]:
    logger.info(
        f"Loading cards with min_len={min_len}, min_likes={min_likes}, last_modified={last_modified}"
    )
    df = pl.read_parquet(DATASET_PARQUET_URL)
    df = parse_markdown_column(df, "card", "datasetId")
    df = df.with_columns(pl.col("parsed_markdown").str.len_chars().alias("card_len"))
    df = df.filter(pl.col("card_len") > min_len)
    if min_likes:
        df = df.filter(pl.col("likes") > min_likes)
    if last_modified:
        df = df.filter(pl.col("last_modified") > last_modified)
    if len(df) == 0:
        logger.info("No cards found matching criteria")
        return None

    cards = df.get_column("prepended_markdown").to_list()
    model_ids = df.get_column("datasetId").to_list()
    last_modifieds = df.get_column("last_modified").to_list()
    logger.info(f"Loaded {len(cards)} cards")
    return cards, model_ids, last_modifieds


@stamina.retry(on=requests.HTTPError, attempts=3, wait_initial=10)
def embed_card(text, client):
    text = text[:MAX_EMBEDDING_LENGTH]
    return client.feature_extraction(text)


def get_inference_client():
    logger.info(f"Initializing inference client with model: {INFERENCE_MODEL_URL}")
    return InferenceClient(
        model=INFERENCE_MODEL_URL,
        token=HF_TOKEN,
    )


def refresh_data(min_len: int = 200, min_likes: Optional[int] = None):
    logger.info(f"Starting data refresh with min_len={min_len}, min_likes={min_likes}")
    chroma_client = get_chroma_client()
    embedding_function = get_embedding_function()
    collection = get_collection(chroma_client, embedding_function)

    most_recent = get_last_modified_in_collection(collection)

    if data := load_cards(
        min_len=min_len, min_likes=min_likes, last_modified=most_recent
    ):
        _create_and_upsert_embeddings(data, collection)
    else:
        logger.info("No new data to refresh")


def _create_and_upsert_embeddings(data, collection):
    cards, model_ids, last_modifieds = data
    logger.info("Embedding cards...")
    inference_client = get_inference_client()
    results = thread_map(lambda card: embed_card(card, inference_client), cards)
    logger.info(f"Upserting {len(model_ids)} items to collection")
    collection.upsert(
        ids=model_ids,
        embeddings=[embedding.tolist()[0] for embedding in results],
        metadatas=[{"last_modified": str(lm)} for lm in last_modifieds],
    )
    logger.info("Data refresh completed successfully")


if __name__ == "__main__":
    refresh_data()