import logging
from fastapi import FastAPI
from datasets import load_dataset
import pandas as pd
import numpy as np

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

app = FastAPI()

# -------------------------------------------------
# Lazy dataset cache
# -------------------------------------------------
DF = None


def load_data_once():
    """
    Load the dataset only once using streaming mode.
    Hugging Face Spaces cannot download large datasets at startup,
    so streaming=True avoids timeouts and memory issues.
    """
    global DF

    if DF is None:
        logger.info("Loading dataset (streaming mode): kurry/sp500_earnings_transcripts ...")

        ds = load_dataset(
            "kurry/sp500_earnings_transcripts",
            split="train",
            streaming=True
        )

        # Convert streaming dataset → pandas DataFrame
        # Limit rows to avoid memory overload
        rows = []
        for i, item in enumerate(ds):
            rows.append(item)
            if i > 5000:   # Safety limit for Spaces
                break

        DF = pd.DataFrame(rows)
        logger.info(f"Loaded {len(DF)} rows into DataFrame")

    return DF


# -------------------------------------------------
# Utility: convert NumPy → Python
# -------------------------------------------------
def to_python(obj):
    if isinstance(obj, (np.integer, np.int64)):
        return int(obj)
    if isinstance(obj, (np.floating, np.float64)):
        return float(obj)
    if isinstance(obj, (np.bool_)):
        return bool(obj)
    if isinstance(obj, pd.Timestamp):
        return obj.isoformat()
    return obj


def clean_dict(d):
    return {k: to_python(v) for k, v in d.items()}


# -------------------------------------------------
# Routes
# -------------------------------------------------

@app.on_event("startup")
def startup_event():
    logger.info("🚀 Earnings Transcript API starting up")


@app.get("/")
def root():
    logger.info("Root endpoint called")
    return {"message": "Earnings Transcript API is running"}


@app.get("/tickers")
def list_tickers():
    logger.info("Listing all tickers")
    df = load_data_once()

    if "symbol" not in df.columns:
        return {"error": "Dataset does not contain 'symbol' column"}

    tickers = sorted(df["symbol"].dropna().unique().tolist())
    logger.info(f"Returned {len(tickers)} tickers")
    return {"tickers": tickers}


@app.get("/transcript/{symbol}")
def get_transcript(symbol: str):
    logger.info(f"Transcript request received for symbol: {symbol}")

    df = load_data_once()
    symbol = symbol.upper()

    if "symbol" not in df.columns:
        return {"error": "Dataset missing 'symbol' column"}

    rows = df[df["symbol"] == symbol]

    if rows.empty:
        logger.warning(f"No transcripts found for symbol: {symbol}")
        return {"error": f"No transcripts found for symbol {symbol}"}

    row = rows.iloc[0]
    base_info = clean_dict(row.to_dict())

    # Extract structured content (correct column name)
    segments = row.get("structured_content", None)

    if isinstance(segments, list):
        logger.info(f"Cleaning {len(segments)} segments for {symbol}")
        cleaned_segments = [
            clean_dict(seg) for seg in segments if isinstance(seg, dict)
        ]
        base_info["segments"] = cleaned_segments
    else:
        logger.info(f"No structured_content found for {symbol}")
        base_info["segments"] = []

    logger.info(f"Returning transcript for {symbol}")
    return base_info