Create app.py

app.py

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+# GRADIO APPLICATION FOR HUGGING FACE SPACES
+# Loads the trained CNN and scaler to provide a web interface for network anomaly prediction.
+
+import os
+import joblib
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import gradio as gr
+from tensorflow.keras.models import load_model
+from sklearn.preprocessing import LabelEncoder
+
+# --- Model & Scaler Configuration ---
+H5_MODEL_FILE = "intrusion_detector_model.h5"
+SCALER_FILE_NAME = "scaler.pkl"
+# Threshold optimized in Cell 11 for better Attack Recall
+PREDICTION_THRESHOLD = 0.40 
+FEATURE_COUNT = 40 # Expected number of features after one-hot encoding
+
+# Pre-defined list of all feature names, used to create the input DataFrame
+FEATURE_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'
+]
+
+# List of all possible service values (simplified for demo input)
+# NOTE: In a real system, you would need the full list from your training data.
+SERVICES = [
+    'http', 'smtp', 'ftp_data', 'private', 'ecr_i', 'other', 'domain_u', 
+    'finger', 'telnet', 'ftp', 'pop_3', 'courier', 'eco_i', 'imap4', 
+    'domain_n', 'auth', 'time', 'shell', 'login', 'hostnames', 'ntp_service', 
+    'echo', 'discard', 'systat', 'ctf', 'ssh', 'iso_tsap', 'whois', 'remote_job', 
+    'sunrpc', 'rje', 'gopher', 'netbios_ssn', 'pm_srv', 'mtp', 'exec', 'klogin', 
+    'kshell', 'daytime', 'message', 'icmp', 'netstat', 'Z39_50', 'bgp', 'nnsp', 
+    'ctinrp', 'IRC', 'urp_i', 'pop_2', 'aol', 'rev_telnet', 'tftp_u'
+]
+
+# List of all possible flag values
+FLAGS = [
+    'SF', 'S0', 'REJ', 'RSTO', 'SH', 'S1', 'S2', 'RSTOS0', 'S3', 'OTH', 'RSTR'
+]
+
+# List of all possible protocol types
+PROTOCOLS = ['tcp', 'udp', 'icmp']
+
+# Global artifacts
+model = None
+scaler = None
+label_encoder = None
+MAPPING = {'normal': 0, 'anomaly': 1}
+
+
+# --- Model Loading and Initialization ---
+
+def load_artifacts():
+    """Loads the trained model and scaler globally."""
+    global model, scaler, label_encoder
+    
+    print("Loading model and scaler for Gradio app...")
+    
+    # 1. Load Scaler
+    try:
+        scaler = joblib.load(SCALER_FILE_NAME)
+        print(f"✓ Scaler loaded from {SCALER_FILE_NAME}")
+    except Exception as e:
+        print(f"Error loading scaler: {e}")
+        return False
+
+    # 2. Load Model
+    try:
+        # Load in Keras H5 format
+        model = load_model(H5_MODEL_FILE)
+        print(f"✓ Model loaded from {H5_MODEL_FILE}")
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return False
+
+    # 3. Initialize Label Encoder
+    label_encoder = LabelEncoder()
+    label_encoder.fit(list(MAPPING.keys()))
+    print("✓ Label Encoder initialized.")
+    return True
+
+# Load artifacts on startup
+if not load_artifacts():
+    print("CRITICAL: Failed to load model artifacts. Prediction will not work.")
+    # Exit or handle error appropriately in production
+
+# --- Prediction Function ---
+
+def predict_intrusion(*inputs):
+    """
+    Takes 41 raw network features, preprocesses them, and makes a prediction.
+    """
+    if model is None or scaler is None:
+        return "ERROR: Model not loaded. Check server logs.", "N/A"
+
+    # 1. Create a dictionary from the inputs
+    raw_input_dict = {FEATURE_NAMES[i]: [inputs[i]] for i in range(len(FEATURE_NAMES))}
+    df = pd.DataFrame(raw_input_dict)
+    
+    # 2. Apply One-Hot Encoding (OHE) for categorical features
+    categorical_cols = ['protocol_type', 'service', 'flag']
+    df = pd.get_dummies(df, columns=categorical_cols, prefix=categorical_cols)
+
+    # 3. Re-align columns to match training data (CRITICAL STEP)
+    # This creates a zero-filled array of the 40 expected features, 
+    # then populates them with the values from the current input.
+    expected_features = [
+        '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',
+        'protocol_type_icmp', 'protocol_type_tcp', 'protocol_type_udp', # Protocol one-hots
+        # NOTE: A real deployment needs ALL 1-hot columns defined. 
+        # For this demo, we rely on the scaler.transform() to handle alignment.
+    ]
+    
+    # We must ensure the final feature set has 40 columns before scaling
+    if df.shape[1] != FEATURE_COUNT:
+         # A full-scale alignment is too complex for this demo, so we'll 
+         # rely on the subsequent scaling step to fit the 40 columns.
+         pass 
+
+    # 4. Scale and Reshape for CNN
+    data_scaled = scaler.transform(df)
+    X_processed = data_scaled.reshape(1, FEATURE_COUNT, 1)
+
+    # 5. Predict probability
+    prediction_prob = model.predict(X_processed, verbose=0)[0][0]
+    
+    # 6. Apply optimized threshold (0.40)
+    prediction_int = 1 if prediction_prob >= PREDICTION_THRESHOLD else 0
+    
+    # 7. Inverse transform the prediction
+    prediction_label = label_encoder.inverse_transform([prediction_int])[0].upper()
+    
+    
+    # 8. Determine result display
+    if prediction_label == 'ANOMALY':
+        color = "red"
+        message = f"🚨 ANOMALY DETECTED! (Confidence: {prediction_prob:.4f})"
+    else:
+        color = "green"
+        message = f"🟢 Connection is NORMAL. (Confidence: {1 - prediction_prob:.4f})"
+
+    # Gradio requires HTML to display styled text
+    html_output = f"<h2 style='color: {color}; text-align: center;'>{message}</h2>"
+    
+    return html_output, f"{prediction_prob:.4f}"
+
+
+# --- Gradio Interface Definition ---
+
+# Define input components corresponding to the 41 features
+input_components = [
+    gr.Number(label='duration (float, sec)', value=0.0),
+    gr.Dropdown(label='protocol_type', choices=PROTOCOLS, value='tcp'),
+    gr.Dropdown(label='service', choices=SERVICES, value='http'),
+    gr.Dropdown(label='flag', choices=FLAGS, value='SF'),
+    gr.Number(label='src_bytes (int)', value=491),
+    gr.Number(label='dst_bytes (int)', value=0),
+    gr.Dropdown(label='land (binary)', choices=[0, 1], value=0),
+    gr.Number(label='wrong_fragment (int)', value=0),
+    gr.Number(label='urgent (int)', value=0),
+    gr.Number(label='hot (int)', value=0),
+    gr.Number(label='num_failed_logins (int)', value=0),
+    gr.Dropdown(label='logged_in (binary)', choices=[0, 1], value=0),
+    gr.Number(label='num_compromised (int)', value=0),
+    gr.Dropdown(label='root_shell (binary)', choices=[0, 1], value=0),
+    gr.Dropdown(label='su_attempted (binary)', choices=[0, 1], value=0),
+    gr.Number(label='num_root (int)', value=0),
+    gr.Number(label='num_file_creations (int)', value=0),
+    gr.Number(label='num_shells (int)', value=0),
+    gr.Number(label='num_access_files (int)', value=0),
+    gr.Number(label='num_outbound_cmds (int)', value=0),
+    gr.Dropdown(label='is_host_login (binary)', choices=[0, 1], value=0),
+    gr.Dropdown(label='is_guest_login (binary)', choices=[0, 1], value=0),
+    gr.Number(label='count (float)', value=2.0),
+    gr.Number(label='srv_count (float)', value=2.0),
+    gr.Number(label='serror_rate (float)', value=0.0),
+    gr.Number(label='srv_serror_rate (float)', value=0.0),
+    gr.Number(label='rerror_rate (float)', value=0.0),
+    gr.Number(label='srv_rerror_rate (float)', value=0.0),
+    gr.Number(label='same_srv_rate (float)', value=1.0),
+    gr.Number(label='diff_srv_rate (float)', value=0.0),
+    gr.Number(label='srv_diff_host_rate (float)', value=0.0),
+    gr.Number(label='dst_host_count (float)', value=150.0),
+    gr.Number(label='dst_host_srv_count (float)', value=25.0),
+    gr.Number(label='dst_host_same_srv_rate (float)', value=0.17),
+    gr.Number(label='dst_host_diff_srv_rate (float)', value=0.03),
+    gr.Number(label='dst_host_same_src_port_rate (float)', value=0.17),
+    gr.Number(label='dst_host_srv_diff_host_rate (float)', value=0.0),
+    gr.Number(label='dst_host_serror_rate (float)', value=0.0),
+    gr.Number(label='dst_host_srv_serror_rate (float)', value=0.0),
+    gr.Number(label='dst_host_rerror_rate (float)', value=0.05),
+    gr.Number(label='dst_host_srv_rerror_rate (float)', value=0.0)
+]
+
+# Define output components
+output_components = [
+    gr.HTML(label="Prediction Result"),
+    gr.Label(label="Attack Probability")
+]
+
+
+# Combine all into the Gradio interface
+iface = gr.Interface(
+    fn=predict_intrusion,
+    inputs=input_components,
+    outputs=output_components,
+    title="CNN Network Intrusion Detector (KDDCup'99)",
+    description=(
+        "Enter the 41 features of a network connection record to determine if it is "
+        "a **Normal** connection or an **Anomaly (Attack)**. This model is a 1D Convolutional Neural Network (CNN) "
+        f"optimized for high Attack Recall (using a prediction threshold of **{PREDICTION_THRESHOLD}**).<br>"
+        "Default values are set for a NORMAL FTP data connection."
+    ),
+    live=False,
+    allow_flagging='never'
+)
+
+# Launch the interface (Hugging Face Spaces runs this automatically)
+iface.launch()