Create app.py

app.py

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+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 = [
+    # Phishing
+    ["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"],
+
+    # Legitimate
+    ["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()