from io import StringIO

import pandas as pd


def normalize_label(label: any) -> float | None:
    if pd.isna(label):
        return None

    if isinstance(label, (int, float)):
        if label in [0, 1, 0.0, 1.0]:
            return float(label)
        return None

    if isinstance(label, str):
        label_stripped = label.strip()
        if label_stripped in ["0", "1", "0.0", "1.0"]:
            return float(label_stripped)
        return None

    return None


def validate_csv(csv_content: str, true_labels: dict[str, float]) -> tuple[bool, str, pd.DataFrame | None]:
    if not csv_content or not csv_content.strip():
        return False, "CSV content is empty", None

    try:
        df = pd.read_csv(StringIO(csv_content))
    except Exception as e:
        return False, f"Invalid CSV format: {str(e)}", None

    if "id" not in df.columns:
        return False, "CSV must contain 'id' column", None

    if "label" not in df.columns:
        return False, "CSV must contain 'label' column", None

    if df.empty:
        return False, "CSV is empty", None

    if df["id"].isna().any():
        return False, "id column contains missing values", None

    if df["label"].isna().any():
        return False, "label column contains missing values", None

    df["id"] = df["id"].astype(str).str.strip()

    normalized_labels = []
    invalid_labels = []

    for idx, row in df.iterrows():
        id_val = str(row["id"]).strip()
        label = normalize_label(row["label"])

        if label is None:
            invalid_labels.append(f"Row {idx + 1}: invalid label value '{row['label']}' (must be 0, 1, 0.0, or 1.0)")
        else:
            normalized_labels.append(label)

    if invalid_labels:
        return False, "Invalid labels found:\n" + "\n".join(invalid_labels[:5]), None

    df["label"] = normalized_labels

    unknown_ids = []
    for id_val in df["id"]:
        if str(id_val) not in true_labels:
            unknown_ids.append(str(id_val))

    if unknown_ids:
        return (
            False,
            f"Unknown IDs found: {', '.join(unknown_ids[:5])}{'...' if len(unknown_ids) > 5 else ''}",
            None,
        )

    missing_ids = []
    for true_id in true_labels.keys():
        if true_id not in df["id"].values:
            missing_ids.append(true_id)

    if missing_ids:
        return (
            False,
            f"Missing IDs from true labels: {', '.join(missing_ids[:5])}{'...' if len(missing_ids) > 5 else ''} (total: {len(missing_ids)})",
            None,
        )

    return True, "CSV is valid", df