Update app.py

app.py

@@ -1,14 +1,13 @@
 import gradio as gr
 import joblib
 import pandas as pd
-from sklearn.ensemble import RandomForestClassifier
 
 # Load the saved Random Forest model
 rf_model = joblib.load('rf_model.pkl')  # Make sure to use the actual path to your saved model
 
 # Define the feature names (excluding the target column 'type')
-feature_names = [
-    "date", "time", "door_state", "sphone_signal", "label"
+numeric_features = [
+    "door_state", "sphone_signal", "label"  # Only numeric features
 ]
 
 class_labels = {
@@ -29,13 +28,13 @@ def detect_intrusion(file):
     except Exception as e:
         return f"Error reading file: {str(e)}"
 
-    # Check if all required feature columns are in the log file
-    missing_features = [feature for feature in feature_names if feature not in log_data.columns]
+    # Check if all required numeric feature columns are in the log file
+    missing_features = [feature for feature in numeric_features if feature not in log_data.columns]
     if missing_features:
         return f"Missing features in file: {', '.join(missing_features)}"
     
-    # Extract the feature values (including 'label', but excluding 'type' which is the target)
-    feature_values = log_data[feature_names].astype(float).values
+    # Extract the numeric feature values (excluding date and time)
+    feature_values = log_data[numeric_features].astype(float).values
 
     # Predict the class (multi-class classification) for each row in the log file
     predictions = rf_model.predict(feature_values)