first base model

app.py

@@ -1,64 +1,183 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import os
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Model configuration
+MODEL_NAME = "cybersectony/phishing-email-detection-distilbert_v2.4.1"
 
+# Global variables for model and tokenizer
+model = None
+tokenizer = None
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def load_model():
+    """Load model and tokenizer once at startup"""
+    global model, tokenizer
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+        model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+        model.eval()  # Set to evaluation mode
+        return True
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return False
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def predict_phishing(text):
+    """
+    Predict if email/URL is phishing or legitimate
+    """
+    global model, tokenizer
+    
+    if not text.strip():
+        return "Please enter some text to analyze", {}, ""
+    
+    try:
+        # Tokenize input
+        inputs = tokenizer(
+            text,
+            return_tensors="pt",
+            truncation=True,
+            max_length=512,
+            padding=True
+        )
+        
+        # Get prediction
+        with torch.no_grad():
+            outputs = model(**inputs)
+            predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        
+        # Get probabilities
+        probs = predictions[0].tolist()
+        
+        # Label mapping
+        labels = {
+            "Legitimate Email": probs[0],
+            "Phishing URL": probs[1], 
+            "Legitimate URL": probs[2],
+            "Phishing Email": probs[3] if len(probs) > 3 else 0
+        }
+        
+        # Find highest probability
+        max_label = max(labels.items(), key=lambda x: x[1])
+        prediction = max_label[0]
+        confidence = max_label[1]
+        
+        # Create confidence bar data
+        confidence_data = {label: f"{prob:.1%}" for label, prob in labels.items()}
+        
+        # Risk assessment
+        if "Phishing" in prediction:
+            risk_level = "🚨 HIGH RISK - Potential Phishing Detected"
+            risk_color = "red"
+        else:
+            risk_level = "✅ LOW RISK - Appears Legitimate"
+            risk_color = "green"
+        
+        # Format result
+        result = f"""
+### {risk_level}
+**Primary Classification:** {prediction}  
+**Confidence:** {confidence:.1%}
+        """
+        
+        return result, confidence_data, risk_color
+        
+    except Exception as e:
+        return f"Error during prediction: {str(e)}", {}, "orange"
 
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+# Load model at startup
+print("Loading model...")
+model_loaded = load_model()
+if not model_loaded:
+    print("Failed to load model!")
 
+# Create Gradio interface
+with gr.Blocks(
+    theme=gr.themes.Soft(),
+    title="Phishing Email & URL Detective",
+    css="""
+    .risk-high { color: #dc2626 !important; font-weight: bold; }
+    .risk-low { color: #16a34a !important; font-weight: bold; }
+    .main-container { max-width: 800px; margin: 0 auto; }
+    """
+) as demo:
+    
+    gr.Markdown("""
+    # 🛡️ Phishing Detection System
+    **Instantly detect phishing emails and malicious URLs using AI**
+    
+    Powered by DistilBERT • 99.58% Accuracy • Real-time Analysis
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            input_text = gr.Textbox(
+                label="📧 Email Content or URL",
+                placeholder="Paste suspicious email content or URL here...",
+                lines=8,
+                max_lines=15
+            )
+            
+            analyze_btn = gr.Button(
+                "🔍 Analyze for Phishing",
+                variant="primary",
+                size="lg"
+            )
+        
+        with gr.Column(scale=1):
+            result_output = gr.Markdown(label="Analysis Result")
+            
+            confidence_output = gr.Label(
+                label="Confidence Breakdown",
+                num_top_classes=4
+            )
+    
+    # Example inputs
+    gr.Markdown("### 📋 Try These Examples:")
+    
+    examples = [
+        ["Dear User, Your account will be suspended! Click here immediately: http://fake-bank-login.com/urgent"],
+        ["Hi Mufasa, Thanks for your email. The quarterly report is attached. Best regards, Simba"],
+        ["URGENT: Verify your PayPal account now or lose access: https://paypal-security-verify.suspicious.com"],
+        ["Meeting reminder: Project sync at 3 PM in conference room B. See you there!"]
+    ]
+    
+    gr.Examples(
+        examples=examples,
+        inputs=input_text,
+        outputs=[result_output, confidence_output]
+    )
+    
+    # Event handlers
+    analyze_btn.click(
+        fn=predict_phishing,
+        inputs=input_text,
+        outputs=[result_output, confidence_output, gr.State()]
+    )
+    
+    input_text.submit(
+        fn=predict_phishing,
+        inputs=input_text,
+        outputs=[result_output, confidence_output, gr.State()]
+    )
+    
+    gr.Markdown("""
+    ---
+    ### ℹ️ About This Tool and the team.
+    - **Model:** DistilBERT fine-tuned for phishing detection
+    - **Accuracy:** 99.58% on test dataset
+    - **Speed:** Real-time analysis
+    - **Privacy:** All processing happens locally, no data stored
+    
+    **⚠️ Disclaimer:** This tool is for educational purposes (Assignemnt) only, we currently hold no rights and responsibility to this tool. So please Always verify suspicious content through official channels.
+    """)
 
+# Launch configuration
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        share=False,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True,
+        quiet=False
+    )