--- a
+++ b/test.py
@@ -0,0 +1,33 @@
+import numpy as np
+from keras.models import load_model
+from sklearn.preprocessing import StandardScaler
+import pickle
+
+# Load the saved model
+model = load_model('my_model.h5')
+
+# Load the scaler object used during training
+scaler = StandardScaler()
+scaler_file = 'scaler.pkl'
+with open(scaler_file, 'rb') as f:
+    scaler = pickle.load(f)
+
+# Load the data for prediction as a numpy array
+my_list0 = [33,1,2,4,5,4,3,2,2,4,3,2,2,4,3,4,2,2,3,1,2,3,4]
+my_list1 = [17,1,3,1,5,3,4,2,2,2,2,4,2,3,1,3,7,8,6,2,1,7,2]
+my_list2 = [64,2,6,8,7,7,7,6,7,7,7,8,7,7,9,6,5,7,2,4,3,1,4]
+
+# Convert the list to a numpy array with the desired shape
+new_data = np.array([my_list2])
+
+# Standardize the new data using the loaded scaler
+new_data_scaled = scaler.transform(new_data)
+
+# Make predictions
+predictions = model.predict(new_data_scaled)
+
+# Convert the predictions to class labels
+predicted_classes = np.argmax(predictions, axis=1)
+
+# Print the predicted class
+print("Predicted class:", predicted_classes[0], type(predicted_classes[0]))