import os, re
import gradio as gr

os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")

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 _markdown_table(rows):
    lines = ["| URL | Prediction | Confidence (%) |", "|---|---|---|"]
    for u, lbl, conf in rows:
        lines.append(f"| `{u}` | **{lbl}** | {conf:.2f} |")
    return "\n".join(lines)

def analyze(text: str) -> str:
    text = (text or "").strip()
    if not text:
        return "Paste an email body or a URL."

    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."

    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)
    rows, 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"])
        rows.append([u, lbl, conf])
        if lbl.lower() in {"phishing","malware","defacement"}:
            unsafe = True

    verdict = "🔴 **UNSAFE (links flagged)**" if unsafe else "🟢 **SAFE (all links benign)**"
    return verdict + "\n\n" + _markdown_table(rows)

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="Results"),
    title="🛡️ PhishingMail (Link Analysis)",
    description="Extracts links from your text and classifies each with a compact malicious-URL model.",
)

if __name__ == "__main__":
    demo.launch()