Update app.py

app.py

@@ -8,15 +8,25 @@ import tempfile
 # Set a custom directory for Gradio's temporary files
 os.environ["GRADIO_TEMP"] = tempfile.mkdtemp()
 
-# Load the saved Random Forest model
-rf_model = joblib.load('rf_model.pkl')  # Ensure the correct model path
+# Dictionary of IoT devices and their corresponding model files
+device_models = {
+    "Garage Door": "garage_door_model.pkl",
+    "GPS Tracker": "gps_tracker_model.pkl",
+    "Weather": "weather_model.pkl",
+    "Thermostat": "thermostat_model.pkl",
+    "Fridge": "fridge_model.pkl"
+}
 
-# Define required numeric features
-numeric_features = [
-    "date_numeric", "time_numeric", "door_state", "sphone_signal", "label"
-]
+# Define required numeric features for each device
+device_features = {
+    "Garage Door": ["date_numeric", "time_numeric", "door_state", "sphone_signal", "label"],
+    "GPS Tracker": ["date_numeric", "time_numeric", "latitude", "longitude", "label"],
+    "Weather": ["date_numeric", "time_numeric", "temperature", "humidity", "label"],
+    "Thermostat": ["date_numeric", "time_numeric", "temp_set", "temp_actual", "label"],
+    "Fridge": ["date_numeric", "time_numeric", "temp_inside", "door_open", "label"]
+}
 
-# Class labels for attack types
+# Class labels for attack types (assuming same for all devices; adjust if needed)
 class_labels = {
     0: "Normal",
     1: "Backdoor",
@@ -35,35 +45,62 @@ def convert_datetime_features(log_data):
         log_data['date_numeric'] = log_data['date'].astype(np.int64) // 10**9  
 
         time_parsed = pd.to_datetime(log_data['time'], format='%H:%M:%S', errors='coerce')
-        log_data['time_numeric'] = (time_parsed.dt.hour * 3600) + (time_parsed.dt.minute * 60) + time_parsed.dt.second
+        log_data['time_numeric'] = (time_parsed.dt.hour * 3600) + (time_parsed.dt.minute * 60) + time_parsed.dt SECOND
     except Exception as e:
-        return f"Error processing date/time: {str(e)}"
+        return f"Error processing date/time: {str(e)}", None
     
-    return log_data
+    return None, log_data
 
-def detect_intrusion(file):
-    """Process log file and predict attack type."""
+def detect_intrusion(device, file):
+    """Process log file and predict attack type based on selected device."""
+    # Load the selected device's model
+    try:
+        rf_model = joblib.load(device_models[device])
+    except Exception as e:
+        return f"Error loading model for {device}: {str(e)}", None, None
+
+    # Read the uploaded file
     try:
         log_data = pd.read_csv(file.name)
     except Exception as e:
-        return f"Error reading file: {str(e)}"
+        return f"Error reading file: {str(e)}", None, None
 
-    log_data = convert_datetime_features(log_data)
+    # Convert date and time features
+    error, log_data = convert_datetime_features(log_data)
+    if error:
+        return error, None, None
 
-    missing_features = [feature for feature in numeric_features if feature not in log_data.columns]
+    # Get the required features for the selected device
+    required_features = device_features[device]
+    missing_features = [feature for feature in required_features if feature not in log_data.columns]
     if missing_features:
-        return f"Missing features in file: {', '.join(missing_features)}"
+        return f"Missing features for {device}: {', '.join(missing_features)}", None, None
 
+    # Preprocess device-specific features
     try:
-        log_data['door_state'] = log_data['door_state'].astype(str).str.strip().replace({'closed': 0, 'open': 1})
-        log_data['sphone_signal'] = pd.to_numeric(log_data['sphone_signal'], errors='coerce')
-
-        feature_values = log_data[numeric_features].astype(float).values
+        if device == "Garage Door":
+            log_data['door_state'] = log_data['door_state'].astype(str).str.strip().replace({'closed': 0, 'open': 1})
+            log_data['sphone_signal'] = pd.to_numeric(log_data['sphone_signal'], errors='coerce')
+        elif device == "GPS Tracker":
+            log_data['latitude'] = pd.to_numeric(log_data['latitude'], errors='coerce')
+            log_data['longitude'] = pd.to_numeric(log_data['longitude'], errors='coerce')
+        elif device == "Weather":
+            log_data['temperature'] = pd.to_numeric(log_data['temperature'], errors='coerce')
+            log_data['humidity'] = pd.to_numeric(log_data['humidity'], errors='coerce')
+        elif device == "Thermostat":
+            log_data['temp_set'] = pd.to_numeric(log_data['temp_set'], errors='coerce')
+            log_data['temp_actual'] = pd.to_numeric(log_data['temp_actual'], errors='coerce')
+        elif device == "Fridge":
+            log_data['temp_inside'] = pd.to_numeric(log_data['temp_inside'], errors='coerce')
+            log_data['door_open'] = log_data['door_open'].astype(str).str.strip().replace({'closed': 0, 'open': 1})
+
+        # Prepare feature values for prediction
+        feature_values = log_data[required_features].astype(float).values
         predictions = rf_model.predict(feature_values)
     except Exception as e:
-        return f"Error during prediction: {str(e)}"
+        return f"Error during prediction for {device}: {str(e)}", None, None
 
-    # Map predictions to specific attack types
+    # Map predictions to attack types
     log_data['Prediction'] = [class_labels.get(pred, 'Unknown Attack') for pred in predictions]
 
     # Format date for output
@@ -73,23 +110,39 @@ def detect_intrusion(file):
     output_df = log_data[['date', 'time', 'Prediction']]
 
     # Save the output to a CSV file for download
-    output_file = "intrusion_results.csv"
+    output_file = f"intrusion_results_{device.lower().replace(' ', '_')}.csv"
     output_df.to_csv(output_file, index=False)
 
-    return output_df, output_file
+    return None, output_df, output_file
 
 # Create Gradio interface
+def gradio_interface(device, file):
+    error, df, output_file = detect_intrusion(device, file)
+    if error:
+        return error, None, None
+    return df, df, output_file
+
 iface = gr.Interface(
-    fn=detect_intrusion,
-    inputs=[gr.File(label="Upload Log File (CSV format)")],
-    outputs=[gr.Dataframe(label="Intrusion Detection Results"), gr.File(label="Download Predictions CSV")],
-    title="Intrusion Detection System",
+    fn=gradio_interface,
+    inputs=[
+        gr.Dropdown(choices=list(device_models.keys()), label="Select IoT Device", value="Garage Door"),
+        gr.File(label="Upload Log File (CSV format)")
+    ],
+    outputs=[
+        gr.Textbox(label="Status/Error Message"),
+        gr.Dataframe(label="Intrusion Detection Results"),
+        gr.File(label="Download Predictions CSV")
+    ],
+    title="IoT Intrusion Detection System",
     description=(
         """
-        Upload a CSV log file with the following features:
-        date,time,door_state,sphone_signal,label
-        Example:
-        26-04-19,13:59:20,1,-85,normal
+        Select an IoT device and upload a CSV log file with the appropriate features for that device.
+        Example features per device:
+        - Garage Door: date,time,door_state,sphone_signal,label (e.g., 26-04-19,13:59:20,1,-85,normal)
+        - GPS Tracker: date,time,latitude,longitude,label
+        - Weather: date,time,temperature,humidity,label
+        - Thermostat: date,time,temp_set,temp_actual,label
+        - Fridge: date,time,temp_inside,door_open,label
         """
     )
 )