Update app.py

app.py

@@ -6,7 +6,15 @@ import cv2
 
 img_size = 28
 model = from_pretrained_keras("keras-io/keras-reptile")
+examples = [["./example0.JPG"], ["./example1.JPG"]] 
+def inference(original_image):
+    image = tf.keras.utils.img_to_array(original_image)
+    image = image.astype("float32") / 255.0
+    image = np.reshape(image, (28, 28, 1))
+    output = model.predict(image)
+    return output
 
+'''
 def read_image(image):
     image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     image = tf.image.resize(image,(28,28,1))
@@ -24,7 +32,14 @@ examples = [["./example0.JPG"], ["./example1.JPG"]]
 title = "Few shot learning"
 description = "Upload an image or select from examples to classify fashion items."
 
+'''
 
-gr.Interface(display_result, input, outputs="text", examples=examples, allow_flagging=False, analytics_enabled=False,
- title=title, description=description).launch(enable_queue=True)
-gr.launch()
+interface = gr.Interface(
+    fn = inference,
+    title = "Few shot learning with Reptile",
+    description = "Keras Implementation of Reptile for ",
+    inputs = gr.inputs.Image(shape=(28, 28, 1)),
+    outputs = "text",
+    examples = examples,
+    article = "Author: <a href=\"https://huggingface.co/Blazer007\">Animesh Maheshwari</a>. Based on the keras example from <a href=\"\"></a> \n Model Link: https://huggingface.co/keras-io/keras-reptile",
+    ).launch(enable_queue=True, debug = True)