Update app.py

app.py

@@ -9,13 +9,21 @@ model = tf.keras.models.load_model("denis_mnist_cnn_model.h5")
 def preprocess_image(image):
     # Resize the image to 28x28 as expected by the model
     image = tf.image.resize(image, (28, 28))  # Resize to 28x28
+    print(f"Image shape after resizing: {image.shape}")
     
-    # Convert image to float32 and normalize pixel values to [0, 1]
+    # Convert the image to grayscale (1 channel)
+    image = tf.image.rgb_to_grayscale(image)  # Convert RGB to grayscale
+    
+    print(f"Image shape after converting to grayscale: {image.shape}")
+    
+    # Normalize pixel values to [0, 1]
     image = tf.cast(image, tf.float32) / 255.0
     
     # Add batch dimension (model expects batch of images)
     image = tf.expand_dims(image, axis=0)
+    
     print(f"Image shape after adding batch dimension: {image.shape}")
+    
     return image
 
 # Function to make predictions