Fix SARIMA and LSTM deployment issues

app.py

@@ -5,6 +5,7 @@ import numpy as np
 import tensorflow as tf
 import joblib
 from sklearn.metrics import mean_absolute_error, mean_squared_error
+from sklearn.preprocessing import MinMaxScaler
 
 # Load the dataset
 webtraffic_data = pd.read_csv("webtraffic.csv")
@@ -24,8 +25,6 @@ sarima_model = joblib.load("sarima_model.pkl")  # SARIMA model
 lstm_model = tf.keras.models.load_model("lstm_model.keras")  # LSTM model
 
 # Initialize scalers and scale the data for LSTM
-from sklearn.preprocessing import MinMaxScaler
-
 scaler_X = MinMaxScaler(feature_range=(0, 1))
 scaler_y = MinMaxScaler(feature_range=(0, 1))
 
@@ -37,8 +36,8 @@ y_train_scaled = scaler_y.fit_transform(train_data['Sessions'].values.reshape(-1
 X_test_scaled = scaler_X.transform(test_data['Sessions'].values.reshape(-1, 1))
 y_test_scaled = scaler_y.transform(test_data['Sessions'].values.reshape(-1, 1))
 
-# Reshape test data for LSTM
-X_test_lstm = X_test_scaled.reshape((X_test_scaled.shape[0], 1, X_test_scaled.shape[1]))
+# Reshape test data for LSTM (samples, time_steps, features)
+X_test_lstm = X_test_scaled.reshape((X_test_scaled.shape[0], 1, 1))
 
 # Generate predictions for SARIMA
 sarima_predictions = sarima_model.predict(start=len(train_data), end=len(webtraffic_data) - 1)