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

def coverage_gap_analysis(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    needs_columns: List[str],
    reached_columns: List[str],
    group_by: Optional[List[str]] = None
) -> pl.DataFrame:
    """
    Coverage Gap Analysis
    =====================

    What
    ----
    Compare needs vs reached to compute coverage and gaps.

    When
    ----
    Monitoring reports and dashboard summaries.

    Why
    ---
    Identify where assistance is insufficient.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
    needs_columns : list[str]
        Needs indicator columns.
    reached_columns : list[str]
        Reached indicator columns (aligned order with needs).
    group_by : list[str] | None
        Optional grouping (e.g., province).

    Returns
    -------
    pl.DataFrame
        Placeholder coverage and gap.

    Example
    -------
    >>> coverage_gap_analysis(df, ["need"],["reached"], group_by=["province"]) 
    """
    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({"coverage_pct": [0.0], "gap": [0.0]})
