Update app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import joblib
+import pickle
+import os
+
+# --- 1. CONFIGURATION AND FILE LOADING ---
+
+# Define file paths (assuming you'll upload your improved model)
+MODEL_PATH = 'improved_intrusion_detection_model.h5'
+SCALER_PATH = 'standard_scaler.pkl'
+FEATURE_NAMES_PATH = 'feature_names.pkl'
+
+# Define the 41 original raw features expected from the user input
+# NOTE: This list needs to be manually defined based on the KDD dataset structure.
+# The 'feature_names.pkl' you provided contains the FINAL 119 feature names.
+RAW_41_FEATURES = [
+    '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'
+]
+
+# Identify categorical columns from the raw features
+CATEGORICAL_COLS = ['protocol_type', 'service', 'flag']
+NUMERICAL_COLS = [col for col in RAW_41_FEATURES if col not in CATEGORICAL_COLS]
+
+
+try:
+    # Load Model (assuming it's in the directory)
+    model = tf.keras.models.load_model(MODEL_PATH)
+
+    # Load Preprocessing Objects
+    scaler = joblib.load(SCALER_PATH)
+    
+    # Load final 119 feature names list
+    # The feature_names.pkl file contains the FINAL 119 column names, including OHE columns.
+    with open(FEATURE_NAMES_PATH, 'rb') as f:
+        FINAL_119_COLUMNS = pickle.load(f).tolist()
+
+    # --- Derived Configuration ---
+    # The final columns must match the scaler's feature count
+    if scaler.n_features_in_ != len(FINAL_119_COLUMNS):
+        raise ValueError(f"Scaler expects {scaler.n_features_in_} features, but feature_names.pkl has {len(FINAL_119_COLUMNS)}. Check file consistency.")
+    
+except (FileNotFoundError, ValueError) as e:
+    print(f"FATAL ERROR: Failed to load required file or file inconsistent: {e}")
+    print("Please ensure your improved model (.h5) and all .pkl files are in the same folder.")
+    raise
+
+# --- 2. PREDICTION FUNCTION ---
+
+def predict_attack(*raw_input_values):
+    """
+    Processes the 41 raw user inputs, converts them to 119 scaled features, and predicts.
+    """
+    if len(raw_input_values) != len(RAW_41_FEATURES):
+        return f'<h1 style="color:red; font-size:24px;">Input Error: Expected {len(RAW_41_FEATURES)} features, received {len(raw_input_values)}.</h1>'
+    
+    # 1. Create a raw DataFrame from the user input
+    raw_df = pd.DataFrame([raw_input_values], columns=RAW_41_FEATURES)
+    
+    # Ensure numerical columns are numeric type
+    for col in NUMERICAL_COLS:
+        raw_df[col] = pd.to_numeric(raw_df[col], errors='coerce').fillna(0.0) 
+
+    
+    # 2. One-Hot Encoding
+    # Use pandas get_dummies on the categorical columns
+    df_encoded = pd.get_dummies(raw_df, columns=CATEGORICAL_COLS, dtype=float)
+
+    # 3. Align and Reorder Features to match the 119 FINAL_119_COLUMNS list
+    # This crucial step ensures the exact order and column presence (filling missing with 0)
+    X_processed = df_encoded.reindex(columns=FINAL_119_COLUMNS, fill_value=0)
+    
+    # Convert to NumPy array
+    X_array = X_processed.values.astype(np.float32)
+
+    # 4. Standard Scaling (on the entire 119-feature vector)
+    X_scaled = scaler.transform(X_array)
+    
+    # 5. Reshape for CNN (1, 119, 1)
+    X_cnn = X_scaled.reshape((1, X_scaled.shape[1], 1))
+
+    # 6. Predict
+    prediction = model.predict(X_cnn, verbose=0)
+    
+    # Determine result (binary classification threshold 0.5)
+    probability = prediction[0][0]
+    
+    if probability > 0.5:
+        # Detected as Attack
+        result = f"🚨 ATTACK DETECTED! (Probability: {probability*100:.2f}%)"
+        color = "red"
+    else:
+        # Detected as Normal
+        result = f"✅ Normal Traffic (Probability: {(1 - probability)*100:.2f}%)"
+        color = "green"
+        
+    return f'<h1 style="color:{color}; font-size:24px;">{result}</h1>'
+
+# --- 3. GRADIO INTERFACE SETUP ---
+
+# Use placeholders for the categorical choices since we don't have the categorical map file
+# This assumes the user will input valid strings like 'tcp', 'http', 'SF'.
+# For a robust deployed app, you should load the unique categorical values.
+# For demonstration, we'll use simple Textboxes or common examples.
+input_components = []
+for name in RAW_41_FEATURES:
+    if name in NUMERICAL_COLS:
+        input_components.append(gr.Number(label=name, value=0.0))
+    elif name == 'protocol_type':
+        input_components.append(gr.Dropdown(label=name, choices=['tcp', 'udp', 'icmp'], value='tcp'))
+    elif name == 'flag':
+        input_components.append(gr.Dropdown(label=name, choices=['SF', 'S0', 'REJ', 'RSTR', 'OTH'], value='SF'))
+    elif name == 'service':
+        # Service has 70+ values; using Textbox is best unless all choices are loaded
+        input_components.append(gr.Textbox(label=name, value='http'))
+    else:
+        input_components.append(gr.Textbox(label=name, value='0'))
+
+# Example Neptune DoS attack vector: [0, tcp, private, S0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 10, 1, 1, 0, 0, 0.04, 0.06, 0, 255, 10, 0.04, 0.06, 0, 0, 1, 1, 0, 0]
+example_attack_data = [
+    0.0, 'tcp', 'private', 'S0', 0.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+    255, 10, 1.0, 1.0, 0.0, 0.0, 0.04, 0.06, 0.0, 255, 10, 0.04, 0.06, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0
+]
+
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_attack,
+    inputs=input_components,
+    outputs=gr.HTML(label="Prediction Result"),
+    title="KDD Intrusion Detection System (CNN)",
+    description="Enter the 41 raw features of a network connection. The model predicts if the traffic is 'normal' or an 'attack'.",
+    examples=[example_attack_data]
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch(share=False)