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

def forecasting_needs(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    value_column: str,
    date_column: str = "date",
    model: str = "arima",
    forecast_periods: int = 3
) -> pl.DataFrame:
    """
    Forecasting Needs (ARIMA/Prophet-style interface)
    ================================================

    What
    ----
    Predict future values of needs/beneficiaries for planning.

    When
    ----
    Before proposal or allocation to estimate coming months.

    Why
    ---
    Anticipate gaps and prepare resources.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input dataset with dates and values.
    value_column : str
        Numeric column to forecast.
    date_column : str, default "date"
        Date column (YYYY-MM-DD recommended).
    model : str, default "arima"
        Forecasting model name (e.g., "arima", "prophet").
    forecast_periods : int, default 3
        Number of future periods to forecast.

    Returns
    -------
    pl.DataFrame
        Historical values plus a simple placeholder forecast column.

    Example
    -------
    >>> forecasting_needs(df, value_column="beneficiaries", forecast_periods=6)
    """
    # Placeholder: returns input plus a dummy last_row repeated as forecast marker
    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(model).alias("forecast_model"))
