Update app.py

app.py

@@ -66,10 +66,12 @@ with gr.Blocks() as demo:
         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))
+        # assuming image_array is your input image array of shape (552, 3)
+        resized_array = np.resize(img, (28, 28)) # resize the array to (28, 28)
+        input_array = np.expand_dims(resized_array, axis=0) # add an extra dimension to represent the batch size
+
         # Make a prediction using the model
-        prediction = probability_model.predict(img_normal)
+        prediction = probability_model.predict(img_array)
 
         # Postprocess the prediction and return it
         return np.argmax(predictions[0])