import sys
import pandas as pd
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix


def resolve_label(df: pd.DataFrame) -> pd.Series:
    label_cols = [c for c in df.columns if c.startswith("label_")]
    if len(label_cols) == 1:
        return df[label_cols[0]]
    raise ValueError(f"Expected one label column, found: {label_cols}")


def align_frames(preds: pd.DataFrame, truth: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
    if len(preds) != len(truth):
        raise ValueError(
            f"Row count mismatch: predictions has {len(preds)} rows, truth has {len(truth)} rows"
        )

    if "scenario_id" in preds.columns and "scenario_id" in truth.columns:
        preds = preds.sort_values("scenario_id").reset_index(drop=True)
        truth = truth.sort_values("scenario_id").reset_index(drop=True)

        if not preds["scenario_id"].equals(truth["scenario_id"]):
            raise ValueError("scenario_id mismatch after alignment between predictions and truth")

    return preds, truth


def score(predictions_path: str, ground_truth_path: str) -> dict:
    preds = pd.read_csv(predictions_path)
    truth = pd.read_csv(ground_truth_path)

    preds, truth = align_frames(preds, truth)

    y_pred = resolve_label(preds)
    y_true = resolve_label(truth)

    accuracy = accuracy_score(y_true, y_pred)
    precision = precision_score(y_true, y_pred, zero_division=0)
    recall = recall_score(y_true, y_pred, zero_division=0)
    f1 = f1_score(y_true, y_pred, zero_division=0)

    tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=[0, 1]).ravel()

    false_safe_rate = fn / (fn + tp) if (fn + tp) > 0 else 0.0

    return {
        "accuracy": accuracy,
        "precision": precision,
        "recall_cascade_detection": recall,
        "false_safe_rate": false_safe_rate,
        "f1": f1,
        "confusion_matrix": {
            "tp": int(tp),
            "fp": int(fp),
            "tn": int(tn),
            "fn": int(fn),
        },
    }


if __name__ == "__main__":
    if len(sys.argv) != 3:
        raise SystemExit("Usage: python scorer.py <predictions.csv> <ground_truth.csv>")
    print(score(sys.argv[1], sys.argv[2]))