Update app.py

app.py

@@ -8,53 +8,75 @@ detector = pipeline(
     "text-classification",
     model="songhieng/roberta-phishing-content-detector-1.0",
     device=device,
-    return_all_scores=False,
+    top_k=1,                   # replaces deprecated return_all_scores=False
 )
 
 # 2. Wrap prediction in a function for Gradio
 def classify_text(text: str):
     """
-    Given an input text, returns the label ("PHISHING" or "LEGIT") and the confidence score.
+    Returns (label, score) for the given text.
     """
     if not text or not text.strip():
-        return {"Label": "⚠️ Please enter some text", "Score": 0.0}
+        return "⚠️ Please enter some text", 0.0
+    
     result = detector(text)[0]
     label = result["label"]
     score = float(result["score"])
-    return {"Label": label, "Score": round(score, 4)}
+    return label, round(score, 4)
 
-# 3. Build Gradio Interface with expanded examples
+# 3. Realistic example test cases
 examples = [
-    # Phishing Examples
-    ["Congratulations! You've won a $1,000 gift card. Click here to claim now: http://bit.ly/free-gift"],
-    ["URGENT: Your PayPal account has been limited. Verify your identity here: https://secure-paypal-login.com"],
-    ["Alert: We detected a login from an unrecognized device. Reset your password immediately: http://tinyurl.com/reset-now"],
-    ["Important: Your invoice for last month's subscription is overdue. Pay now to avoid suspension: http://billing.example.com/pay"],
-    ["Security Notice: Your bank account will be closed unless you confirm your details here: https://bank-secure-update.com"],
-    
-    # Legitimate Examples
-    ["Your Amazon order has shipped! Track your package here: https://amazon.com/track"],
-    ["Reminder: Your Zoom meeting with the Marketing team is scheduled for tomorrow at 3:00 PM."],
-    ["Hey Jane, just checking in—are we still on for lunch at the café this Friday? 😊"],
-    ["Your utility bill for May is now available in your online account. No action required if you're already enrolled in autopay."],
-    ["Welcome to the Acme Newsletter! We're excited to share our latest product updates and tips with you."],
+    # 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"],
+
+    # Legit
+    ["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() as demo:
-    gr.Markdown("# 🚨 Phishing Content Detector")
-    inp = gr.Textbox(lines=6, placeholder="Paste email or message here…")
-    label_out = gr.Textbox(label="Label")
-    score_out = gr.Number(label="Confidence")
+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 **LEGIT**.
+        """
+    )
+
+    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]
+        outputs=[label_out, score_out],
+    )
+
+    gr.Examples(
+        examples=examples,
+        inputs=inp,
+        cache_examples=False,
+        label="Example Test Cases",
     )
-    gr.Examples(examples=examples, inputs=inp, label="Example Test Cases")
+
     gr.Markdown(
-        "**Model:** V1.0"
+        """
+        **Model:** Version 1.0
+        """
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    # set share=True if you want a public link
+    demo.launch()