change the test images from test set, forct K to int

app.py

@@ -35,11 +35,11 @@ import os
 def get_sample_images(num_images):
     sample_images = []
     for i in range(num_images):
-      train_feature = X_train[i]
-      train_feature_2d =train_feature.reshape(28,28)
+      test_feature = X_test[i]
+      test_feature_2d =test_feature.reshape(28,28)
     
       # Make it unsigned integers:
-      data = train_feature_2d.astype(np.uint8)
+      data = test_feature_2d.astype(np.uint8)
 
       outdir =  "images_folder"
       img_path = os.path.join(outdir, 'local_%05d.png' % (i,))
@@ -47,7 +47,7 @@ def get_sample_images(num_images):
          os.mkdir(outdir)
       cv2.imwrite(img_path, data)
 
-      sample_images.append([img_path,int(np.random.choice([7,9,11,13]))])   # ["image path", "K"]
+      sample_images.append([img_path,int(np.random.choice([7,9,11,13,15,24]))])   # ["image path", "K"]
     return sample_images
 
 # EXTRA: adapted from https://github.com/ageron/handson-ml2/blob/master/03_classification.ipynb