Update app.py

app.py

@@ -1,9 +1,9 @@
-import pandas as pd
-import numpy as np
-import joblib
-import gradio as gr
 import os
 import tempfile
+import pandas as pd
+import pickle
+import gradio as gr
+from datetime import datetime
 
 # Set a custom directory for Gradio's temporary files
 os.environ["GRADIO_TEMP"] = tempfile.mkdtemp()
@@ -17,134 +17,131 @@ device_models = {
     "Fridge": "fridge_model.pkl"
 }
 
-# Define required numeric features for each device
+# Define the device-specific features
 device_features = {
+    "Fridge": ["date_numeric", "time_numeric", "fridge_temperature", "label"],
     "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"]
+    "Thermostat": ["date_numeric", "time_numeric", "current_temp", "thermostat_status", "label"],
+    "Weather": ["date_numeric", "time_numeric", "temperature", "pressure", "humidity", "label"]
 }
 
-# Class labels for attack types (assuming same for all devices; adjust if needed)
+# Define class labels for attack types
 class_labels = {
     0: "Normal",
     1: "Backdoor",
     2: "DDoS",
     3: "Injection",
-    4: "Password Attack",
+    4: "Password",
     5: "Ransomware",
     6: "Scanning",
-    7: "XSS",
+    7: "XSS"
 }
 
-def convert_datetime_features(log_data):
-    """Convert date and time into numeric values."""
-    try:
-        log_data['date'] = pd.to_datetime(log_data['date'], format='%d-%m-%y', errors='coerce')
-        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
-    except Exception as e:
-        return f"Error processing date/time: {str(e)}", None
-    
-    return None, log_data
-
-def detect_intrusion(device, file):
-    """Process log file and predict attack type based on selected device."""
-    # Load the selected device's model
-    try:
-        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
+def convert_datetime_features(df):
+    """Convert date and time to numeric features."""
     try:
-        log_data = pd.read_csv(file.name)
+        # Convert date to datetime and then to Unix timestamp
+        df['date'] = pd.to_datetime(df['date'], format='%d-%b-%y')
+        df['date_numeric'] = df['date'].astype('int64') // 10**9
+        
+        # Convert time to seconds since midnight
+        df['time'] = pd.to_datetime(df['time'], format='%H:%M:%S')
+        df['time_numeric'] = df['time'].dt.hour * 3600 + df['time'].dt.minute * 60 + df['time'].dt.second
     except Exception as e:
-        return f"Error reading file: {str(e)}", None, None
-
-    # Convert date and time features
-    error, log_data = convert_datetime_features(log_data)
-    if error:
-        return error, None, None
-
-    # 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 for {device}: {', '.join(missing_features)}", None, None
+        raise ValueError(f"Error converting date/time: {str(e)}")
+    return df
 
-    # Preprocess device-specific features
+def detect_intrusion(file, device_type="Fridge"):
     try:
-        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":
+        # Read the input CSV file
+        if isinstance(file, str):
+            log_data = pd.read_csv(file)
+        else:
+            log_data = pd.read_csv(file.name)
+
+        # Validate device type
+        if device_type not in device_features:
+            return f"Unsupported device type: {device_type}"
+
+        # Convert date and time to numeric features
+        log_data = convert_datetime_features(log_data)
+
+        # Preprocess features based on device type
+        if device_type == "Fridge":
+            log_data['fridge_temperature'] = pd.to_numeric(log_data['fridge_temperature'], errors='coerce')
+        elif device_type == "Garage Door":
+            log_data['door_state'] = log_data['door_state'].map({'closed': 0, 'open': 1})
+            log_data['sphone_signal'] = log_data['sphone_signal'].astype(int)
+        elif device_type == "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 = model.predict(feature_values)
-    except Exception as e:
-        return f"Error during prediction for {device}: {str(e)}", None, None
-
-    # Map predictions to attack types
-    log_data['Prediction'] = [class_labels.get(pred, 'Unknown Attack') for pred in predictions]
-
-    # Format date for output
-    log_data['date'] = log_data['date'].dt.strftime('%Y-%m-%d')
+        elif device_type == "Thermostat":
+            log_data['current_temp'] = pd.to_numeric(log_data['current_temp'], errors='coerce')
+            log_data['thermostat_status'] = log_data['thermostat_status'].astype(int)
+        elif device_type == "Weather":
+            for col in ['temperature', 'pressure', 'humidity']:
+                log_data[col] = pd.to_numeric(log_data[col], errors='coerce')
+
+        # Select relevant features
+        required_features = device_features[device_type]
+        missing_features = [f for f in required_features if f not in log_data.columns]
+        if missing_features:
+            return f"Missing features for {device_type}: {', '.join(missing_features)}"
+
+        # Load the pre-trained model
+        try:
+            with open(device_models[device_type], "rb") as f:
+                model = pickle.load(f)
+        except FileNotFoundError:
+            return f"Model file for {device_type} not found: {device_models[device_type]}"
+        except KeyError:
+            return f"No model defined for device type: {device_type}"
+
+        # Prepare features for prediction (exclude label if present)
+        features = [f for f in required_features if f != "label"]
+        X = log_data[features]
+
+        # Make predictions
+        predictions = model.predict(X)
+
+        # Map predictions to class labels
+        log_data['Prediction'] = [class_labels.get(pred, "Unknown") for pred in predictions]
+
+        # Format date for output
+        log_data['date'] = log_data['date'].dt.strftime('%Y-%m-%d')
+
+        # Select output columns
+        output_data = log_data[['date', 'time', 'Prediction']]
+
+        # Save results to CSV
+        output_file = f"intrusion_results_{device_type.lower().replace(' ', '_')}.csv"
+        output_data.to_csv(output_file, index=False)
+
+        return output_data, output_file
 
-    # Select final output columns
-    output_df = log_data[['date', 'time', 'Prediction']]
+    except Exception as e:
+        return f"Error processing file: {str(e)}"
 
-    # Save the output to a CSV file for download
-    output_file = f"intrusion_results_{device.lower().replace(' ', '_')}.csv"
-    output_df.to_csv(output_file, index=False)
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# IoT Intrusion Detection System")
+    gr.Markdown("Upload a CSV file and select the device type to detect intrusions.")
 
-    return None, output_df, output_file
+    with gr.Row():
+        file_input = gr.File(label="Upload CSV File")
+        device_type = gr.Dropdown(choices=list(device_features.keys()), label="Device Type", value="Fridge")
+    
+    submit_button = gr.Button("Detect Intrusions")
+    
+    output_table = gr.Dataframe(label="Detection Results")
+    output_file = gr.File(label="Download Results CSV")
 
-# 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=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", visible=False),
-        gr.Dataframe(label="Intrusion Detection Results"),
-        gr.File(label="Download Predictions CSV")
-    ],
-    title="IoT Intrusion Detection System",
-    description=(
-        """
-        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
-        """
+    submit_button.click(
+        fn=detect_intrusion,
+        inputs=[file_input, device_type],
+        outputs=[output_table, output_file]
     )
-)
 
-iface.launch()
+# Launch the interface
+demo.launch()