Update app.py

app.py

@@ -17,65 +17,3 @@ iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 iface.launch()
 
 
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-PATH_TO_LABELS = 'data/label_map.pbtxt'   
-category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
-
-def pil_image_as_numpy_array(pilimg):
-    img_array = tf.keras.utils.img_to_array(pilimg)
-    return img_array
-
-def load_model():
-    model_dir = 'saved_model'    
-    detection_model = tf.saved_model.load(str(model_dir))
-    return detection_model
-
-def predict(image_np):
-    image_np = pil_image_as_numpy_array(image_np)
-    image_np = np.expand_dims(image_np, axis=0)
-    results = detection_model(image_np)
-    result = {key: value.numpy() for key, value in results.items()}
-    label_id_offset = 0
-    image_np_with_detections = image_np.copy()
-    viz_utils.visualize_boxes_and_labels_on_image_array(
-        image_np_with_detections[0],
-        result['detection_boxes'][0],
-        (result['detection_classes'][0] + label_id_offset).astype(int),
-        result['detection_scores'][0],
-        category_index,
-        use_normalized_coordinates=True,
-        max_boxes_to_draw=200,
-        min_score_thresh=.6,
-        agnostic_mode=False,
-        line_thickness=2
-    )
-    result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
-    return result_pil_img
-
-
-
-detection_model = load_model()
-
-# Specify paths to example images
-sample_images = [["br_61.jpg"], ["br_61.jpg"],
-                ]
-
-tab1 = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(label='Upload an expressway image', type="pil"),
-    outputs=gr.Image(type="pil"),
-    title='Image Processing',
-    examples=sample_images
-)
-
-
-# Create a Multi Interface with Tabs
-iface = gr.TabbedInterface([tab1], title='Cauliflower and Beetroot Detection via ssd_resnet50_v1_fpn_640x640_coco17_tpu-8')
-
-# Launch the interface
-iface.launch(share=True)