diff --git a/Network/TesterRandom.py b/Network/TesterRandom.py
index 3a69073..73ceb50 100644
--- a/Network/TesterRandom.py
+++ b/Network/TesterRandom.py
@@ -1,14 +1,13 @@
+import os
+import numpy as np
 import tensorflow as tf
 from tensorflow import keras
-from tensorflow.keras.preprocessing import image
-import numpy as np
-import os
 
 # Load the trained model
 model = keras.models.load_model('trained_model.h5')
 
 # Load the class names
-class_names = ['Empty', 'Food','People']
+class_names = ['Table', 'Done','Order']
 
 # Path to the folder containing test images
 test_images_folder = 'Testing/'
@@ -23,11 +22,11 @@ for folder_name in os.listdir(test_images_folder):
         
         # True class based on folder name
         if folder_name == 'Empty':
-            true_class = 'Empty'
+            true_class = 'Table'
         elif folder_name == 'Food':
-            true_class = 'Food'
+            true_class = 'Done'
         elif folder_name == 'People':
-            true_class = 'People'
+            true_class = 'Order'
         true_class = folder_name
         
         # Iterate over the files in the subfolder
@@ -36,8 +35,8 @@ for folder_name in os.listdir(test_images_folder):
                 i+=1
                 # Load and preprocess the test image
                 image_path = os.path.join(folder_path, filename)
-                test_image = image.load_img(image_path, target_size=(100, 100))
-                test_image = image.img_to_array(test_image)
+                test_image = keras.preprocessingimage.load_img(image_path, target_size=(100, 100))
+                test_image = keras.preprocessingimage.img_to_array(test_image)
                 test_image = np.expand_dims(test_image, axis=0)
                 test_image = test_image / 255.0  # Normalize the image
             
diff --git a/Network/testerVal.py b/Network/testerVal.py
index 0868241..8abc29b 100644
--- a/Network/testerVal.py
+++ b/Network/testerVal.py
@@ -7,7 +7,7 @@ from tensorflow import keras
 model = keras.models.load_model('trained_model.h5')
 
 # Load the class names
-class_names = ['Empty', 'Food','People']
+class_names = ['Table', 'Done','Order']
 
 # Load and preprocess the validation dataset
 data_dir = "Training/"