Update app.py

app.py

@@ -39,18 +39,41 @@ class_labels = {
 }
 
 def convert_datetime_features(df):
-    """Convert date and time to numeric features."""
+    """Convert date and time to numeric features with flexible format handling."""
     try:
-        # 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
+        # Copy dataframe to avoid modifying original
+        df = df.copy()
         
+        # Try multiple date formats
+        date_formats = ['%d-%b-%y', '%d-%m-%y', '%Y-%m-%d']
+        df['date_numeric'] = pd.Series(index=df.index, dtype='float64')
+        
+        for fmt in date_formats:
+            try:
+                # Attempt to parse dates with the current format
+                valid_dates = pd.to_datetime(df['date'], format=fmt, errors='coerce')
+                if valid_dates.notna().any():
+                    df['date_numeric'] = valid_dates.astype('int64') // 10**9
+                    break
+            except Exception:
+                continue
+        
+        # Check for unparsed dates
+        if df['date_numeric'].isna().any():
+            invalid_rows = df[df['date_numeric'].isna()]['date'].to_list()
+            raise ValueError(f"Invalid date formats in rows: {invalid_rows[:5]}")
+
         # Convert time to seconds since midnight
-        df['time'] = pd.to_datetime(df['time'], format='%H:%M:%S')
+        df['time'] = pd.to_datetime(df['time'], format='%H:%M:%S', errors='coerce')
+        if df['time'].isna().any():
+            invalid_times = df[df['time'].isna()]['time'].to_list()
+            raise ValueError(f"Invalid time formats in rows: {invalid_times[:5]}")
         df['time_numeric'] = df['time'].dt.hour * 3600 + df['time'].dt.minute * 60 + df['time'].dt.second
+
+        return df
+    
     except Exception as e:
         raise ValueError(f"Error converting date/time: {str(e)}")
-    return df
 
 def detect_intrusion(file, device_type="Fridge"):
     try:
@@ -62,7 +85,8 @@ def detect_intrusion(file, device_type="Fridge"):
 
         # Validate device type
         if device_type not in device_features:
-            return f"Unsupported device type: {device_type}"
+            error_df = pd.DataFrame({"Error": [f"Unsupported device type: {device_type}"]})
+            return error_df, None
 
         # Convert date and time to numeric features
         log_data = convert_datetime_features(log_data)
@@ -87,16 +111,19 @@ def detect_intrusion(file, device_type="Fridge"):
         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)}"
+            error_df = pd.DataFrame({"Error": [f"Missing features for {device_type}: {', '.join(missing_features)}"]})
+            return error_df, None
 
         # 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]}"
+            error_df = pd.DataFrame({"Error": [f"Model file for {device_type} not found: {device_models[device_type]}"]})
+            return error_df, None
         except KeyError:
-            return f"No model defined for device type: {device_type}"
+            error_df = pd.DataFrame({"Error": [f"No model defined for device type: {device_type}"]})
+            return error_df, None
 
         # Prepare features for prediction (exclude label if present)
         features = [f for f in required_features if f != "label"]
@@ -121,7 +148,8 @@ def detect_intrusion(file, device_type="Fridge"):
         return output_data, output_file
 
     except Exception as e:
-        return f"Error processing file: {str(e)}"
+        error_df = pd.DataFrame({"Error": [f"Error processing file: {str(e)}"]})
+        return error_df, None
 
 # Create Gradio interface
 with gr.Blocks() as demo: