Update app.py

app.py

@@ -14,4 +14,68 @@ def greet(name):
     return "Hello " + name + "!!"
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+iface.launch()
+
+
+
+
+
+
+
+
+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)