import polars as pl
import pandas as pd
from typing import Union, Optional, List
import io

def seasonality_detection(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    value_column: str,
    date_column: str = "date",
    group_by: Optional[List[str]] = None
) -> pl.DataFrame:
    """
    Seasonality Detection
    =====================

    What
    ----
    Detect repeating seasonal patterns (e.g., higher needs in winter).

    When
    ----
    Use on monthly/weekly series spanning at least one full year.

    Why
    ---
    Helps plan resources by season and anticipate peaks.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input dataset (CSV path, DataFrame, or bytes).
    value_column : str
        Numeric column with values over time.
    date_column : str, default "date"
        Column with date strings (YYYY-MM-DD recommended).
    group_by : list[str] | None
        Optional columns to analyze seasonality per group (e.g., province).

    Returns
    -------
    pl.DataFrame
        Original data with placeholder seasonality flags/summary.

    Example
    -------
    >>> seasonality_detection(df, value_column="beneficiaries")
    """
    if isinstance(data, str):
        df = pl.read_csv(data)
    elif isinstance(data, io.BytesIO):
        df = pl.read_csv(data)
    elif isinstance(data, pd.DataFrame):
        df = pl.from_pandas(data)
    elif isinstance(data, pl.DataFrame):
        df = data
    else:
        raise TypeError("Unsupported data type")

    return df.with_columns(pl.lit(False).alias("is_seasonal_placeholder"))
