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

def regression_models(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    y: str,
    X: List[str],
    kind: Literal["linear","logistic"] = "linear",
    add_intercept: bool = True
) -> Dict[str, Any]:
    """
    Regression Models (Linear / Logistic)
    =====================================

    What
    ----
    Estimate relationships between a target and predictors.

    When
    ----
    To quantify effect sizes and make predictions.

    Why
    ---
    Evidence-based decisions and understanding drivers.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input dataset.
    y : str
        Dependent variable (numeric for linear; 0/1 for logistic).
    X : list[str]
        Predictor columns.
    kind : "linear" | "logistic", default "linear"
        Model type.
    add_intercept : bool, default True
        Whether to include an intercept.

    Returns
    -------
    dict
        Placeholder with inputs; replace with fitted model outputs later.

    Example
    -------
    >>> regression_models(df, y="reached", X=["in_need","funding"], kind="linear")
    """
    # Placeholder implementation
    return {"y": y, "X": X, "kind": kind, "add_intercept": add_intercept}
