Update app.py

app.py

@@ -1,175 +1,47 @@
 import gradio as gr
-import tensorflow as tf
 import numpy as np
-import os
 
-print("🚀 Starting Intrusion Detection System...")
-
-# Load your trained model
-try:
-    model = tf.keras.models.load_model('improved_intrusion_detection_model.h5')
-    print("✅ Model loaded successfully!")
-except Exception as e:
-    print(f"❌ Model loading failed: {e}")
-    # Create a dummy model for testing
-    print("⚠️ Using dummy model for testing")
+print("✅ App starting without TensorFlow...")
 
 def predict_intrusion(features_input):
-    """
-    Predict if network traffic is normal or attack
-    """
     try:
-        # Convert input to array
         features = [float(x.strip()) for x in features_input.split(',') if x.strip()]
         
-        # Validate input length
         if len(features) != 119:
-            return {
-                "prediction": "ERROR",
-                "confidence": 0,
-                "message": f"❌ Need exactly 119 features, but got {len(features)}"
-            }
-        
-        # Reshape for CNN model (1, 119, 1)
-        features_array = np.array(features).reshape(1, 119, 1)
+            return {"Prediction": "ERROR", "Confidence": "0%", "Message": f"Need 119 features, got {len(features)}"}
         
-        # Make prediction
-        prediction_prob = model.predict(features_array, verbose=0)[0][0]
-        confidence = float(prediction_prob)
+        # Simple simulation (remove when TensorFlow works)
+        avg_value = sum(features) / len(features)
         
-        # Determine result
-        if confidence > 0.5:
-            result = {
-                "prediction": "🚨 ATTACK DETECTED",
-                "confidence": round(confidence * 100, 2),
-                "message": f"🚨 SECURITY ALERT! Potential intrusion detected with {confidence:.2%} confidence"
+        if avg_value > 0.4:
+            return {
+                "Prediction": "🚨 ATTACK DETECTED", 
+                "Confidence": "85%", 
+                "Message": "Potential threat detected (SIMULATION MODE)"
             }
         else:
-            result = {
-                "prediction": "✅ NORMAL TRAFFIC", 
-                "confidence": round((1 - confidence) * 100, 2),
-                "message": f"✅ Traffic appears normal with {(1-confidence):.2%} confidence"
+            return {
+                "Prediction": "✅ NORMAL TRAFFIC", 
+                "Confidence": "92%", 
+                "Message": "Traffic appears normal (SIMULATION MODE)"
             }
-        
-        return result
-        
+            
     except Exception as e:
-        return {
-            "prediction": "ERROR",
-            "confidence": 0,
-            "message": f"❌ Error: {str(e)}"
-        }
-
-def generate_sample_features():
-    """Generate sample feature values for testing"""
-    # Normal traffic sample (mostly zeros and low values)
-    normal_features = [0.0] * 50 + [0.1, 0.2, 0.05, 0.0, 0.15] + [0.0] * 64
-    # Attack traffic sample (higher values)
-    attack_features = [0.8, 0.9, 0.7, 0.6, 0.85] + [0.0] * 50 + [0.9, 0.8, 0.95] + [0.0] * 61
-    
-    return {
-        "normal": ", ".join(map(str, normal_features)),
-        "attack": ", ".join(map(str, attack_features))
-    }
-
-# Generate samples
-samples = generate_sample_features()
+        return {"Prediction": "ERROR", "Confidence": "0%", "Message": f"Error: {str(e)}"}
 
-# Create Gradio interface
-with gr.Blocks(theme=gr.themes.Soft(), title="Network Intrusion Detection") as demo:
-    gr.Markdown(
-        """
-        # 🔒 Network Intrusion Detection System
-        **AI-Powered Threat Detection with 99.28% Accuracy**
-        """
-    )
+# Create interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🔒 Network Intrusion Detection System")
+    gr.Markdown("**Simulation Mode - TensorFlow installing...**")
     
     with gr.Row():
         with gr.Column():
-            gr.Markdown("## 📊 Input Features")
-            
-            features_input = gr.Textbox(
-                label="Enter 119 Network Features (comma-separated)",
-                placeholder="0.0, 0.1, 0.2, 0.0, 0.15, ... (119 values total)",
-                lines=5
-            )
-            
-            with gr.Row():
-                analyze_btn = gr.Button("🚀 Analyze Traffic", variant="primary")
-                clear_btn = gr.Button("🗑️ Clear")
-            
-            gr.Markdown("### 🧪 Sample Data")
-            with gr.Row():
-                normal_btn = gr.Button("📋 Load Normal Sample")
-                attack_btn = gr.Button("⚠️ Load Attack Sample")
+            features = gr.Textbox(label="Enter 119 features (comma-separated)", lines=5)
+            btn = gr.Button("Analyze Traffic")
         
         with gr.Column():
-            gr.Markdown("## 📈 Prediction Results")
-            
-            prediction_output = gr.Label(
-                label="Detection Result",
-                value={"Prediction": "Waiting for input...", "Confidence": "0%"}
-            )
-            
-            message_output = gr.Textbox(
-                label="Security Alert",
-                interactive=False,
-                lines=3
-            )
-            
-            gr.Markdown("### 📊 Model Information")
-            gr.Markdown("""
-            - **Model Type**: CNN Deep Learning
-            - **Accuracy**: 99.28%
-            - **Attack Detection**: 99.40%
-            - **False Positive Rate**: 0.90%
-            - **Input Features**: 119
-            """)
-    
-    # Button actions
-    def load_normal_sample():
-        return samples["normal"]
-    
-    def load_attack_sample():
-        return samples["attack"]
+            result = gr.Label(label="Prediction Result")
     
-    def clear_inputs():
-        return "", {"Prediction": "Waiting for input...", "Confidence": "0%"}, ""
-    
-    # Event handlers
-    analyze_btn.click(
-        fn=predict_intrusion,
-        inputs=features_input,
-        outputs=[prediction_output, message_output]
-    )
-    
-    normal_btn.click(
-        fn=load_normal_sample,
-        outputs=features_input
-    )
-    
-    attack_btn.click(
-        fn=load_attack_sample,
-        outputs=features_input
-    )
-    
-    clear_btn.click(
-        fn=clear_inputs,
-        outputs=[features_input, prediction_output, message_output]
-    )
-    
-    gr.Markdown(
-        """
-        ---
-        **How to use:**
-        1. Enter 119 comma-separated numerical values
-        2. Click "Analyze Traffic" to get prediction
-        3. Use sample buttons to test with pre-loaded data
-        
-        **Note**: This is a demo interface. For production use, ensure proper model deployment.
-        """
-    )
+    btn.click(fn=predict_intrusion, inputs=features, outputs=result)
 
-# Launch the application
-if __name__ == "__main__":
-    demo.launch(debug=True)
+demo.launch()