Update app.py

app.py

@@ -7,14 +7,14 @@ from tensorflow.keras.datasets import mnist
 import cv2
 
 # I/O image dimensions for display
-DIMS = (100,100)
+DIMS = (100, 100)
 # Load the trained model
 model = load_model('mnist_model.h5')
 
 # Load MNIST examples
 (x_train, y_train), (x_test, y_test) = mnist.load_data()
 mnist_examples = [[x_test[i]] for i in range(10)]  # Select first 10 examples and format as nested list
-# resize the examples 100 by 100
+# Resize the examples to 100 by 100
 mnist_examples = [[cv2.resize(x_test[i], DIMS)] for i in range(10)]
 
 # Function to preprocess the image
@@ -73,14 +73,7 @@ def gradio_mask(image, steps, increment):
 
 class GradioInterface:
     def __init__(self):
-        self.preloaded_examples = self.preload_examples()
-
-    def preload_examples(self):
-        preloaded = {}
-        for model_name, example_dir in Config.EXAMPLES.items():
-            examples = [os.path.join(example_dir, img) for img in os.listdir(example_dir)]
-            preloaded[model_name] = examples
-        return preloaded
+        self.preloaded_examples = mnist_examples
 
     def create_interface(self):
         app_styles = """