Update app.py

app.py

@@ -8,12 +8,25 @@ model = tf.keras.models.load_model("denis_mnist_cnn_model.h5")
 
 # Preprocessing function for images
 def preprocess_image(image):
+    # Convert PIL image to a tensor
+    image = tf.convert_to_tensor(image)
+    
+    # Check if the image has a single channel (grayscale) and reshape if needed
+    if image.shape[-1] != 1:
+        # Convert to grayscale if the image is not in grayscale format (e.g., RGB)
+        image = tf.image.rgb_to_grayscale(image)
+    
     # Resize the image to 28x28 as expected by the model
-    image = np.array(image)
     image = tf.image.resize(image, (28, 28))  # Resize to 28x28
+
+    # Convert grayscale to RGB (3 channels)
     image = tf.image.grayscale_to_rgb(image)  # Convert grayscale to RGB (3 channels)
-    image = image / 255.0  # Normalize pixel values to [0, 1]
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    
+    # Normalize pixel values to [0, 1]
+    image = image / 255.0
+
+    # Add batch dimension (model expects batch of images)
+    image = tf.expand_dims(image, axis=0)
     return image
 
 # Function to make predictions