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import gradio as gr |
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import torch |
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from transformers import pipeline |
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device = 0 if torch.cuda.is_available() else -1 |
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detector = pipeline( |
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"text-classification", |
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model="songhieng/roberta-phishing-content-detector-3.0", |
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device=device, |
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top_k=1, |
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) |
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def classify_text(text: str): |
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if not text or not text.strip(): |
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return "⚠️ Please enter some text", 0.0 |
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preds = detector(text) |
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first = preds[0][0] if isinstance(preds[0], list) else preds[0] |
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raw = first["label"] |
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label = "Phishing" if raw == "LABEL_1" else "Legitimate" |
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score = float(first["score"]) |
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return label, round(score, 4) |
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examples = [ |
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["Congratulations! You've won a $1,000 gift card. Click here to claim: http://bit.ly/free-gift"], |
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["URGENT: Your PayPal account has been limited. Verify at https://secure-paypal-login.com"], |
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["Alert: Unrecognized login. Reset your password: http://tinyurl.com/reset-now"], |
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["Invoice overdue—pay now to avoid suspension: http://billing.example.com/pay"], |
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["Security Notice: Confirm your bank details here: https://bank-secure-update.com"], |
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["Your Amazon order has shipped! Track here: https://amazon.com/track"], |
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["Reminder: Zoom meeting with Marketing tomorrow at 3:00 PM."], |
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["Hey Jane, lunch at the café this Friday? 😊"], |
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["Your May utility bill is available. No action needed if on autopay."], |
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["Welcome to Acme’s Newsletter—our latest updates inside!"], |
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] |
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with gr.Blocks(theme="default") as demo: |
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gr.Markdown( |
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""" |
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# 🚨 Phishing Content Detector |
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Paste any email or message snippet below and this model will predict whether it's **Phishing** or **Legitimate**. |
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""" |
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) |
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inp = gr.Textbox( |
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label="Input Text", |
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placeholder="Paste email or message here…", |
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lines=6, |
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) |
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label_out = gr.Textbox(label="Predicted Label") |
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score_out = gr.Number(label="Confidence Score (0–1)") |
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classify_btn = gr.Button("Classify") |
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classify_btn.click( |
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fn=classify_text, |
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inputs=inp, |
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outputs=[label_out, score_out], |
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) |
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gr.Examples( |
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examples=examples, |
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inputs=inp, |
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cache_examples=False, |
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label="Example Test Cases", |
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) |
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gr.Markdown( |
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""" |
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**Model:** Version 3.0 |
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""" |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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