Update app.py

app.py

@@ -30,30 +30,29 @@ cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
 cfg.MODEL.WEIGHTS = "model_weights/chatswood_buildings_poc.pth"  
 cfg.MODEL.ROI_HEADS.NUM_CLASSES = 8
 
-def segment_buildings(input_image):
+def segment_buildings(im):
 
-    im = cv2.imread(input_image)
     outputs = predictor(im)
     v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
     out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
     return Image.fromarray(np.uint8(out.get_image())).convert('RGB')
 
-# gradio components -inputs
-gr_image_input = gr.inputs.Image(type="pil")
+# gradio components 
 """
 gr_slider_confidence = gr.inputs.Slider(0,1,.1,.7,
                                         label='Set confidence threshold % for masks')
 """
 # gradio outputs
-gr_image_output = gr.outputs.Image(type="pil") 
+inputs = gr.inputs.Image(type="pil", label="Input Image")
+outputs = gr.outputs.Image(type="pil", label="Output Image")
 
 title = "Building Segmentation"
 description = "An instance segmentation demo for identifying boundaries of buildings in aerial images using DETR (End-to-End Object Detection) model with MaskRCNN-101 backbone"
 
 # Create user interface and launch
 gr.Interface(segment_buildings, 
-                inputs = gr_image_input,
-                outputs = gr_image_output,
+                inputs = inputs,
+                outputs = outputs,
                 title = title,
                 enable_queue = True,
                 description = description).launch()