import pandas as pd

from backend.utils.addressMatch import AddressMatch


def df_has_single_uprn(df: pd.DataFrame, uprn: str, column: str = "uprn") -> bool:
    """
    Returns True if all non-null UPRNs in df match the given uprn.
    Returns False otherwise.
    """

    if column not in df.columns:
        return False

    uprns = df[column].dropna().astype(str).str.strip().unique()

    if len(uprns) == 0:
        return False

    return len(uprns) == 1 and uprns[0] == str(uprn)


def get_uprn_candidates(
    df: pd.DataFrame,
    user_address: str,
    address_column: str = "address",
    uprn_column: str = "uprn",
) -> pd.DataFrame:
    """
    Annotate EPC results with lexicographical similarity scores and ranks.

    Returns a DataFrame sorted by descending lexiscore.
    DOES NOT choose or return a UPRN.
    """

    if address_column not in df.columns:
        raise ValueError(f"Missing column: {address_column}")

    if uprn_column not in df.columns:
        raise ValueError(f"Missing column: {uprn_column}")

    out = df.copy()

    user_norm = AddressMatch.normalise_address(user_address)

    out["lexiscore"] = out[address_column].apply(
        lambda x: AddressMatch.levenshtein(user_norm, x)
    )

    out[uprn_column] = out[uprn_column].astype(str).str.replace(r"\.0$", "", regex=True)

    out["lexirank"] = out["lexiscore"].rank(method="dense", ascending=False).astype(int)

    return out.sort_values(
        ["lexirank", "lexiscore"],
        ascending=[True, False],
    )