import re
import joblib
import pandas as pd
import numpy as np
from typing import Dict, Any

_SUSPICIOUS_TOKENS = [
    "login", "verify", "secure", "update", "bank", "pay", "account", "webscr"
]
_IPV4_PATTERN = re.compile(r"(?:\d{1,3}\.){3}\d{1,3}")


def _engineer_features(url_series: pd.Series) -> pd.DataFrame:
    s = url_series.astype(str)
    out = pd.DataFrame(index=s.index)
    out["url_len"] = s.str.len().fillna(0)
    out["count_dot"] = s.str.count(r"\.")
    out["count_hyphen"] = s.str.count("-")
    out["count_digit"] = s.str.count(r"\d")
    out["count_at"] = s.str.count("@")
    out["count_qmark"] = s.str.count("\?")
    out["count_eq"] = s.str.count("=")
    out["count_slash"] = s.str.count("/")
    out["digit_ratio"] = (out["count_digit"] / out["url_len"].replace(0, np.nan)).fillna(0)
    out["has_ip"] = s.str.contains(_IPV4_PATTERN).astype(int)
    for tok in _SUSPICIOUS_TOKENS:
        out[f"has_{tok}"] = s.str.contains(tok, case=False, regex=False).astype(int)
    out["starts_https"] = s.str.startswith("https").astype(int)
    out["ends_with_exe"] = s.str.endswith(".exe").astype(int)
    out["ends_with_zip"] = s.str.endswith(".zip").astype(int)
    return out


def load_bundle(path: str) -> Dict[str, Any]:
    """Load the saved joblib bundle produced by the notebook.

    Returns a dict with keys: model, feature_cols, url_col, label_col, model_type
    """
    bundle = joblib.load(path)
    required = {"model", "feature_cols", "url_col", "label_col", "model_type"}
    missing = required - set(bundle.keys())
    if missing:
        raise ValueError(f"Bundle missing keys: {missing}")
    return bundle


def predict_url(url: str, bundle: Dict[str, Any], threshold: float = 0.5) -> Dict[str, Any]:
    """Predict phishing probability for a single URL using the saved bundle."""
    url_col = bundle["url_col"]
    feature_cols = bundle["feature_cols"]
    model_type = bundle.get("model_type", "xgboost_bst")
    model = bundle["model"]

    row = pd.DataFrame({url_col: [url]})
    feats = _engineer_features(row[url_col])[feature_cols]

    if model_type == "xgboost_bst":
        import xgboost as xgb  # local import to keep base env minimal
        dmat = xgb.DMatrix(feats)
        proba = float(model.predict(dmat)[0])
    elif model_type == "cuml_rf":
        try:
            import cudf  # type: ignore
            gfeats = cudf.DataFrame.from_pandas(feats)
            proba = float(model.predict_proba(gfeats)[:, 1].to_pandas().values[0])
        except Exception as e:  # pragma: no cover
            raise RuntimeError("cudf/cuml required for this bundle but not available") from e
    else:
        proba = float(model.predict_proba(feats)[:, 1][0])

    pred = int(proba >= threshold)
    return {
        "url": url,
        "phishing_probability": proba,
        "predicted_label": pred,
        "backend": model_type,
    }


if __name__ == "__main__":
    # Simple manual test (optional)
    try:
        bundle = load_bundle("rf_url_phishing_xgboost_bst.joblib")
        print(
            predict_url(
                "http://secure-login-account-update.example.com/session?id=123",
                bundle=bundle,
            )
        )
    except FileNotFoundError:
        print("Bundle not found in current directory. This is expected inside the source repo.")