Create app.py

app.py

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+import gradio as gr
+import spaces
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "AntiSpamInstitute/bert-MoE-Phishing-detection-v2.4"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+@spaces.GPU
+def predict_phishing(text):
+    model.to('cuda')
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True)
+    inputs = {k: v.to('cuda') for k, v in inputs.items()}
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        prediction = torch.argmax(probabilities, dim=-1)
+        confidence = probabilities[0][prediction].item()
+    
+    result = "🚨 Phishing" if prediction.item() == 1 else "✅ Legitimate"
+    confidence_pct = f"{confidence * 100:.2f}%"
+    
+    return {
+        "Prediction": result,
+        "Confidence": confidence_pct,
+        "Probability Breakdown": {
+            "Legitimate": f"{probabilities[0][0].item():.4f}",
+            "Phishing": f"{probabilities[0][1].item():.4f}"
+        }
+    }
+
+EXAMPLES = [
+    ["Dear Customer, We've detected unusual activity on your account. Click here to verify: http://amaz0n-security.net/verify"],
+    ["Hi John, Please review the Q4 sales report I've attached. Let me know if you need any clarification. Best regards, Sarah"],
+    ["URGENT: Your PayPal account has been limited. Login here to restore access: http://paypa1-secure.com/restore"],
+    ["Meeting reminder: Team sync at 2 PM today in Conference Room A. Agenda attached."],
+    ["Congratulations! You've won a $1000 Amazon gift card. Claim now: http://free-prizes.net/claim"],
+    ["Dear team, The deadline for submitting Q1 expense reports is next Friday. Please use the standard template."]
+]
+
+demo = gr.Interface(
+    fn=predict_phishing,
+    inputs=gr.Textbox(
+        lines=8,
+        placeholder="Enter email text here...",
+        label="Email Content",
+        elem_id="email_input"
+    ),
+    outputs=gr.JSON(label="Analysis Results"),
+    title="📧 Phishing Email Detector",
+    description="""
+    ### Analyze emails for potential phishing attempts
+    This tool uses a BERT-based model to detect phishing emails. Enter the content of an email to analyze it.
+    The model will classify the email and provide confidence scores.
+    """,
+    examples=EXAMPLES,
+    theme=gr.themes.Base().set(
+        body_background_fill="*neutral_50",
+        block_background_fill="*neutral_100",
+        block_label_background_fill="*neutral_100",
+        input_background_fill="*neutral_0"
+    ),
+    css="""
+        #email_input { font-family: monospace; }
+        .gradio-container { max-width: 800px; margin: auto; }
+    """
+)
+
+if __name__ == "__main__":
+    demo.queue().launch(
+        share=False,
+        debug=False,
+        show_api=False,
+        server_name="0.0.0.0"
+    )