Update app.py to adapt to dataset structure

app.py

@@ -9,14 +9,17 @@ import joblib
 # Load the dataset
 webtraffic_data = pd.read_csv("webtraffic.csv")
 
-# Ensure the 'Datetime' column is in datetime format
-webtraffic_data['Datetime'] = pd.to_datetime(webtraffic_data['Datetime'])
+# Rename 'Hour Index' for easier use
+webtraffic_data.rename(columns={"Hour Index": "Datetime"}, inplace=True)
+
+# Create a datetime-like index for visualization purposes
+webtraffic_data['Datetime'] = pd.to_datetime(webtraffic_data['Datetime'], unit='h', origin='unix')
 
 # Load the pre-trained models
 sarima_model = joblib.load("sarima_model.pkl")  # Load SARIMA model
 lstm_model = tf.keras.models.load_model("lstm_model.keras")  # Load LSTM model
 
-# Load the scaler for LSTM if used during training (optional)
+# Load the scaler for LSTM (if used during training)
 scaler = joblib.load("scaler.pkl")
 
 # Function to generate predictions and plots