File size: 2,449 Bytes
31dea96 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | #!/usr/bin/env python
"""
Inference untuk `Febriyansyah/phishing-email-classifier` — TF-IDF char_wb + LogReg.
Payload: model.joblib = {featurizer: hfhub.textfeat.Featurizer, learner: LogisticRegression, gateway: float}
Threshold operasional 0.488 (bukan 0.5).
Penggunaan:
python predict.py "tempel-teks-email-di-sini"
python predict.py --root ./models/phishing_model "Hello world"
"""
from __future__ import annotations
import argparse
import pickle
import sys
from pathlib import Path
# Pastikan hfhub.textfeat dapat di-import saat unpickle (lokal & Hub)
ROOT = Path(__file__).resolve().parents[1] if (Path(__file__).parent / "predict.py").exists() else Path.cwd()
for cand in [ROOT / "src", Path(__file__).resolve().parents[2] / "src", Path.cwd() / "src"]:
if cand.exists() and str(cand) not in sys.path:
sys.path.insert(0, str(cand))
def load_payload(model_root: Path):
joblib = model_root / "model.joblib"
meta = model_root / "metadata.json"
if not joblib.exists():
raise SystemExit(f"❌ '{joblib}' tidak ditemukan. Jalankan train_phishing_model.py dulu.")
with joblib.open("rb") as fh:
pl = pickle.load(fh)
cfg = {}
if meta.exists():
import json
cfg = json.loads(meta.read_text())
return pl, cfg
def classify_one(root: Path, text: str) -> tuple[int, float, float]:
"""Return (label, proba_phishing, threshold)."""
pl, _ = load_payload(root)
featurizer = pl["featurizer"]
clf = pl["learner"]
thr = float(pl.get("gateway", 0.488))
vec = featurizer.transform([text])
proba = float(clf.predict_proba(vec)[0, 1])
label = int(proba >= thr)
return label, proba, thr
def main(argv=None):
ap = argparse.ArgumentParser(description="Predict phishing probability")
ap.add_argument("text", nargs="?", help="badan email")
ap.add_argument("--root", default=str(Path(__file__).parent.parent /
".." / "models" / "phishing_model"))
args = ap.parse_args(argv)
txt = args.text or ""
if not txt.strip():
txt = ("URGENT!! Your account will be suspended today. Verify now: "
"http://secure-login.bank-update.info/panel")
lbl, pr, thr = classify_one(Path(args.root), txt)
tag = "PHISHING" if lbl == 1 else "AMAN"
print(f"\n[{tag}] prob-phishing={pr:.4f} (thr={thr:.3f})")
return 0
if __name__ == "__main__":
sys.exit(main())
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