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Update main.py
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import os, re
import gradio as gr
# Keep Transformers quiet & CPU-only friendly
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
# -------- Config --------
URL_MODEL_ID = "CrabInHoney/urlbert-tiny-v4-malicious-url-classifier"
URL_LABEL_MAP = {
"LABEL_0": "benign",
"LABEL_1": "defacement",
"LABEL_2": "malware",
"LABEL_3": "phishing",
}
URL_RE = re.compile(r"""(?xi)\b(?:https?://|www\.)[a-z0-9\-._~%]+(?:/[^\s<>"']*)?""")
_pipe = None # created on first analyze()
def _extract_urls(t: str):
return sorted(set(m.group(0) for m in URL_RE.finditer(t or "")))
def _pretty(raw, id2label):
if id2label:
if raw in id2label:
return id2label[raw]
k = raw.replace("LABEL_", "")
if k in id2label:
return id2label[k]
return URL_LABEL_MAP.get(raw, raw)
def analyze(text: str) -> str:
text = (text or "").strip()
if not text:
return "Paste an email body or a URL."
# Use single-URL mode if it looks like one; else extract from email text
urls = [text] if (text.lower().startswith(("http://","https://","www.")) and " " not in text) else _extract_urls(text)
if not urls:
return "No URLs detected in the text."
# Lazy import + pipeline creation keeps startup instant
global _pipe
if _pipe is None:
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tok = AutoTokenizer.from_pretrained(URL_MODEL_ID)
mdl = AutoModelForSequenceClassification.from_pretrained(URL_MODEL_ID)
_pipe = pipeline("text-classification", model=mdl, tokenizer=tok, device=-1, top_k=None)
id2label = getattr(_pipe.model.config, "id2label", None)
lines = []
unsafe = False
for u in urls:
scores = sorted(_pipe(u)[0], key=lambda s: s["score"], reverse=True)
top = scores[0]
lbl = _pretty(top["label"], id2label)
conf = 100 * float(top["score"])
lines.append(f"- **{u}** → **{lbl}** ({conf:.2f}%)")
if lbl.lower() in {"phishing", "malware", "defacement"}:
unsafe = True
verdict = "🔴 **UNSAFE (links flagged)**" if unsafe else "🟢 **SAFE (all links benign)**"
return verdict + "\n\n" + "\n".join(lines)
demo = gr.Interface(
fn=analyze,
inputs=gr.Textbox(lines=6, label="Email or URL", placeholder="Paste a URL or a full email…"),
outputs=gr.Markdown(label="Result"),
title="🛡️ Phishing Detector (via Link Analysis)",
description="We extract links and classify each with a compact malicious-URL model (CPU-only, free tier).",
)
if __name__ == "__main__":
demo.launch()