Update app.py

app.py

@@ -64,9 +64,12 @@ with gr.Blocks() as demo:
         # the image can be passed as a PIL or numpy
         # create the final model for production
         probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
-        
+
+        # Normalize the pixel values
+        img_normal = img / 255.0
+        img_normal = img_normal.reshape((1, 28, 28))
         # Make a prediction using the model
-        prediction = probability_model.predict(img)
+        prediction = probability_model.predict(img_normal)
 
         # Postprocess the prediction and return it
         return np.argmax(predictions[0])