Create app.py

app.py

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+import joblib
+import numpy as np
+import tensorflow as tf
+import pandas as pd
+import gradio as gr
+import os
+
+# --- Configuration ---
+MODEL_PATH = "improved_intrusion_detection_model_SIMPLIFIED.h5"
+SCALER_PATH = "standard_scaler.pkl"
+FEATURES_PATH = "feature_names.pkl"
+LABEL_ENCODER_PATH = "label_encoder.pkl"
+FINAL_THRESHOLD = 0.7 
+CATEGORICAL_COLS = ['protocol_type', 'service', 'flag']
+
+# --- Load Artifacts ---
+# The model and preprocessors are loaded once when the app starts
+try:
+    model = tf.keras.models.load_model(MODEL_PATH)
+    scaler = joblib.load(SCALER_PATH)
+    final_features = joblib.load(FEATURES_PATH)
+    label_encoder = joblib.load(LABEL_ENCODER_PATH)
+    print("Model and preprocessors loaded successfully.")
+except Exception as e:
+    print(f"Error loading model artifacts: {e}")
+    # Exit if essential files are missing
+    exit()
+
+def preprocess_and_predict(*raw_input_features):
+    """
+    Takes raw inputs, preprocesses them exactly like the training data, 
+    and returns the prediction.
+    """
+    
+    # 1. Convert tuple of inputs to a single list/Series
+    input_data = pd.Series(raw_input_features, index=raw_input_features_names)
+    
+    # Reshape for single sample processing
+    df_raw = pd.DataFrame([input_data])
+    
+    # 2. One-Hot Encode Categorical Features
+    df_encoded = pd.get_dummies(df_raw, columns=CATEGORICAL_COLS)
+    
+    # 3. Align columns with training data and fill missing features with 0
+    # This is CRUCIAL for deployment correctness.
+    df_encoded = df_encoded.reindex(columns=final_features, fill_value=0)
+    
+    # 4. Scale Numerical Features
+    X_scaled = scaler.transform(df_encoded)
+    
+    # 5. Reshape for CNN Input: (1 sample, 122 features, 1 channel)
+    X_cnn = X_scaled.reshape(X_scaled.shape[0], X_scaled.shape[1], 1)
+    
+    # 6. Predict Probability
+    y_pred_proba = model.predict(X_cnn, verbose=0)[0][0]
+    
+    # 7. Apply Fixed Threshold and Decode Label
+    if y_pred_proba >= FINAL_THRESHOLD:
+        prediction_int = 1
+    else:
+        prediction_int = 0
+    
+    # Decode 0 or 1 back to 'normal' or 'attack'
+    final_label = label_encoder.inverse_transform([prediction_int])[0]
+    
+    return final_label, f"Confidence: {y_pred_proba:.4f}"
+
+
+# --- Gradio Interface Setup ---
+
+# Create a list of the 41 feature names (excluding 'label') for the UI
+raw_input_features_names = [
+    'duration', 'protocol_type', 'service', 'flag', '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'
+]
+
+# Create Gradio inputs corresponding to the feature types
+inputs = [
+    gr.Number(label=name, value=0) if name not in CATEGORICAL_COLS else 
+    gr.Textbox(label=name, value='tcp') # Default example for categorical
+    for name in raw_input_features_names
+]
+
+iface = gr.Interface(
+    fn=preprocess_and_predict,
+    inputs=inputs,
+    outputs=[gr.Label(label="Prediction"), gr.Textbox(label="Details")],
+    title="CNN Network Intrusion Detector (KDD)",
+    description="Enter the 41 raw network traffic features to classify the connection as 'normal' or 'attack'. Optimized with 0.7 threshold.",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()