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

def needs_vs_funding_gap_analysis(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    needs_col: str,
    funding_col: str,
    group_by: Optional[List[str]] = None
) -> pl.DataFrame:
    """
    Needs vs Funding Gap Analysis
    =============================

    What
    ----
    Compare total needs vs available funding to compute gaps.

    When
    ----
    Allocation meetings and advocacy.

    Why
    ---
    Show where funding is insufficient relative to needs.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
    needs_col : str
        Column for total needs value.
    funding_col : str
        Column for funding amount.
    group_by : list[str] | None
        Optional grouping.

    Returns
    -------
    pl.DataFrame
        Placeholder gap metric.

    Example
    -------
    >>> needs_vs_funding_gap_analysis(df, needs_col="need", funding_col="funding")
    """
    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 pl.DataFrame({"gap": [0.0]})
