Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import joblib
+from tensorflow.keras.models import load_model
+
+# --- 1. Load Model and Preprocessing Components ---
+try:
+    # Load the trained model
+    model = load_model('improved_intrusion_detection_model.h5')
+    
+    # Load the scaler, label encoder, and feature names
+    scaler = joblib.load('final_standard_scaler.pkl')
+    le = joblib.load('final_label_encoder.pkl')
+    feature_names = joblib.load('final_feature_names.pkl')
+    
+    print("Model and preprocessing components loaded successfully.")
+    
+except Exception as e:
+    print(f"Error loading files: {e}")
+    # Exit or raise error if crucial files are missing
+    raise
+
+# --- 2. Define the Prediction Function ---
+def predict_intrusion(
+    duration, src_bytes, dst_bytes, land, wrong_fragment, urgent, hot,
+    num_failed_logins, logged_in, num_compromised, root_shell, su_attempted,
+    num_root, num_file_creations, num_shells, num_access_files, 
+    num_outbound_cmds, is_host_login, is_guest_login, count, srv_count, 
+    serror_rate, srv_serror_rate, rerror_rate, srv_rerror_rate, same_srv_rate,
+    diff_srv_rate, srv_diff_host_rate, dst_host_count, dst_host_srv_count,
+    dst_host_same_srv_rate, dst_host_diff_srv_rate, dst_host_same_src_port_rate,
+    dst_host_srv_diff_host_rate, dst_host_serror_rate, dst_host_srv_serror_rate,
+    dst_host_rerror_rate, dst_host_srv_rerror_rate, 
+    # Categorical features - Use integer/dummy values for simple Gradio input
+    protocol_type, service, flag
+):
+    # --- A. Create raw DataFrame (Using the first 41 features, others are 0) ---
+    # The full model requires 119 features, including one-hot encoded categories.
+    
+    # We create a template DataFrame with all 119 features initialized to zero
+    data_dict = {name: [0.0] for name in feature_names}
+    input_df = pd.DataFrame(data_dict)
+    
+    # Map the simple inputs to the DataFrame
+    numerical_features = {
+        'duration': duration, 'src_bytes': src_bytes, 'dst_bytes': dst_bytes,
+        'land': land, 'wrong_fragment': wrong_fragment, 'urgent': urgent,
+        'hot': hot, 'num_failed_logins': num_failed_logins,
+        'logged_in': logged_in, 'num_compromised': num_compromised,
+        'root_shell': root_shell, 'su_attempted': su_attempted,
+        'num_root': num_root, 'num_file_creations': num_file_creations,
+        'num_shells': num_shells, 'num_access_files': num_access_files,
+        'num_outbound_cmds': num_outbound_cmds, 'is_host_login': is_host_login,
+        'is_guest_login': is_guest_login, 'count': count, 'srv_count': srv_count,
+        'serror_rate': serror_rate, 'srv_serror_rate': srv_serror_rate,
+        'rerror_rate': rerror_rate, 'srv_rerror_rate': srv_rerror_rate,
+        'same_srv_rate': same_srv_rate, 'diff_srv_rate': diff_srv_rate,
+        'srv_diff_host_rate': srv_diff_host_rate, 'dst_host_count': dst_host_count,
+        'dst_host_srv_count': dst_host_srv_count,
+        'dst_host_same_srv_rate': dst_host_same_srv_rate,
+        'dst_host_diff_srv_rate': dst_host_diff_srv_rate,
+        'dst_host_same_src_port_rate': dst_host_same_src_port_rate,
+        'dst_host_srv_diff_host_rate': dst_host_srv_diff_host_rate,
+        'dst_host_serror_rate': dst_host_serror_rate,
+        'dst_host_srv_serror_rate': dst_host_srv_serror_rate,
+        'dst_host_rerror_rate': dst_host_rerror_rate,
+        'dst_host_srv_rerror_rate': dst_host_srv_rerror_rate
+    }
+
+    # Update numerical features
+    for col, val in numerical_features.items():
+        if col in input_df.columns:
+            input_df[col] = val
+
+    # Update one-hot encoded features
+    if f'protocol_type_{protocol_type}' in input_df.columns:
+        input_df[f'protocol_type_{protocol_type}'] = 1.0
+    if f'service_{service}' in input_df.columns:
+        input_df[f'service_{service}'] = 1.0
+    if f'flag_{flag}' in input_df.columns:
+        input_df[f'flag_{flag}'] = 1.0
+
+    # Ensure the order of columns matches the training data
+    input_df = input_df[feature_names]
+
+    # --- B. Scale the input data ---
+    X_scaled = scaler.transform(input_df.values)
+
+    # --- C. Reshape for CNN (3D input) ---
+    X_cnn = X_scaled.reshape(1, X_scaled.shape[1], 1)
+
+    # --- D. Predict ---
+    prediction_proba = model.predict(X_cnn)[0][0]
+    
+    # --- E. Decode Result ---
+    # Threshold at 0.5
+    prediction_class = (prediction_proba > 0.5).astype(int)
+    
+    # Decode the result (assuming 0 is 'normal' and 1 is 'attack' based on your previous output)
+    decoded_prediction = le.classes_[prediction_class]
+
+    # Format the output for Gradio
+    probability_percent = f"{prediction_proba * 100:.2f}%"
+    
+    if decoded_prediction == 'attack':
+        result_message = f"🚨 INTRUSION DETECTED! (Attack Probability: {probability_percent})"
+    else:
+        result_message = f"✅ Normal Traffic. (Attack Probability: {probability_percent})"
+        
+    return result_message
+
+# --- 3. Gradio Interface Setup ---
+
+# Create simplified input components for the most critical features
+inputs = [
+    gr.Number(label="Duration (seconds)", value=0),
+    gr.Number(label="Source Bytes", value=491),
+    gr.Number(label="Destination Bytes", value=0),
+    gr.Dropdown(label="Protocol Type", choices=['tcp', 'udp', 'icmp'], value='tcp'),
+    gr.Dropdown(label="Service", choices=['ftp_data', 'http', 'private', 'domain_u', 'other'], value='ftp_data'),
+    gr.Dropdown(label="Flag", choices=['SF', 'S0', 'REJ', 'RSTO'], value='SF'),
+    gr.Slider(0, 1, label="Logged In (1=Yes, 0=No)", step=1, value=1),
+    gr.Number(label="Count (connections to same host)", value=2),
+    gr.Number(label="Service Count (connections to same service)", value=2),
+    gr.Slider(0.0, 1.0, label="Same Service Rate", step=0.01, value=1.0),
+    gr.Slider(0.0, 1.0, label="SError Rate", step=0.01, value=0.0)
+]
+
+# Add more numerical inputs (optional, for a complete interface)
+# NOTE: The function takes 39 raw inputs + 3 categoricals. We'll simplify the interface.
+# For simplicity, we are only showing the 11 most important inputs in the Gradio interface
+# and setting defaults for the remaining 31 numerical and 3 categorical features.
+
+# For a full interface, you would need to list all 41 raw features here!
+# For a practical demo, the current 11 inputs and the fixed defaults will work.
+
+# The function call below must pass ALL 39 numerical features (plus the 3 categoricals).
+# To make this demo work without 39 explicit inputs, we use a wrapper:
+
+def predict_wrapper(*args):
+    # Map the simplified 11 inputs to the full 39+3 required by the main function
+    simplified_inputs = args
+    
+    # Default values for all 39 numerical inputs, ordered by the original feature list
+    defaults = [0.0] * 39
+    
+    # Replace the defaults with the 11 provided inputs
+    # The index mapping must be carefully checked against your feature_names list!
+    
+    # Simplified mapping for demonstration (adjust based on your full feature list order)
+    # This is a highly simplified mapping and assumes the features are in this order:
+    
+    # Numerical features (39)
+    defaults[0] = simplified_inputs[0]  # duration
+    defaults[1] = simplified_inputs[1]  # src_bytes
+    defaults[2] = simplified_inputs[2]  # dst_bytes
+    # ... many features skipped for simplicity ...
+    defaults[8] = simplified_inputs[6]  # logged_in (index 8)
+    # ... many features skipped for simplicity ...
+    defaults[19] = simplified_inputs[7] # count (index 19)
+    defaults[20] = simplified_inputs[8] # srv_count (index 20)
+    defaults[21] = simplified_inputs[9] # serror_rate (index 21)
+    
+    # Placeholder for the remaining features (need to be filled with defaults)
+    
+    # For a proper demo, it's better to use the full feature list:
+    full_args = list(defaults)
+    full_args[0] = simplified_inputs[0] # duration
+    full_args[1] = simplified_inputs[1] # src_bytes
+    full_args[2] = simplified_inputs[2] # dst_bytes
+    full_args[8] = simplified_inputs[6] # logged_in
+    full_args[19] = simplified_inputs[7] # count
+    full_args[20] = simplified_inputs[8] # srv_count
+    full_args[21] = simplified_inputs[9] # serror_rate
+    full_args[22] = simplified_inputs[9] # srv_serror_rate (Assuming serror_rate = srv_serror_rate for simplicity)
+    full_args[23] = simplified_inputs[10] # rerror_rate (Assuming rerror_rate = srv_rerror_rate for simplicity)
+    full_args[24] = simplified_inputs[10] # srv_rerror_rate
+    full_args[25] = simplified_inputs[9] # same_srv_rate (Assuming same_srv_rate is also serror_rate's complement)
+    
+    # The actual numerical inputs from the list of 39 in the original feature_names
+    # The missing 31 features are currently stuck at 0.0 in the `defaults` list.
+    
+    # The three categorical inputs:
+    protocol_type = simplified_inputs[3]
+    service = simplified_inputs[4]
+    flag = simplified_inputs[5]
+
+    # Combine all numerical features and categorical strings
+    final_args = full_args + [protocol_type, service, flag]
+    
+    # Call the main prediction function
+    return predict_intrusion(*final_args)
+
+
+# Define the Gradio interface
+demo = gr.Interface(
+    fn=predict_wrapper,
+    inputs=inputs,
+    outputs=gr.Label(label="Intrusion Detection Result"),
+    title="Intrusion Detection System (KDD Cup '99)",
+    description="Predicts whether a network connection is 'normal' or an 'attack' using a 1D CNN model. The model achieved 99.28% accuracy on the test set."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    demo.launch()