app.py

import os
import gradio as gr
from transformers import pipeline

MODEL_ID = "JyothikaShanmugam/scamshield-muril"
HF_TOKEN = os.getenv("HF_TOKEN")

classifier = pipeline(
    task="text-classification",
    model=MODEL_ID,
    tokenizer=MODEL_ID,
    token=HF_TOKEN,
    truncation=True
)

def analyze_text(text):
    text = (text or "").strip()

    if not text:
        return "Please enter a message, URL, or suspicious text.", {}

    result = classifier(text)[0]

    label = result["label"]
    confidence = round(float(result["score"]) * 100, 2)

    return (
        f"Prediction: {label} | Confidence: {confidence}%",
        {label: float(result["score"])}
    )

demo = gr.Interface(
    fn=analyze_text,
    inputs=gr.Textbox(
        label="Paste suspicious message, URL, or text",
        lines=6,
        placeholder="Example: Your KYC will be blocked today. Click this link immediately..."
    ),
    outputs=[
        gr.Textbox(label="ScamShield Result"),
        gr.Label(label="Model Confidence")
    ],
    title="ScamShield — Text Scam Detector",
    description="Privacy-first analysis of user-submitted content."
)

demo.launch()