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