.

app.py

@@ -60,11 +60,9 @@ cdl_color_map = [{'value': 1, 'label': 'Natural vegetation', 'rgb': (233,255,190
 
 def apply_color_map(rgb, color_map=cdl_color_map):
     
-    
     rgb_mapped = rgb.copy()
     
     for map_tmp in cdl_color_map:
-        
         for i in range(3):
             rgb_mapped[i] = np.where((rgb[0] == map_tmp['value']) & (rgb[1] == map_tmp['value']) & (rgb[2] == map_tmp['value']), map_tmp['rgb'][i], rgb_mapped[i])
     
@@ -158,7 +156,6 @@ def inference_segmentor(model, imgs, custom_test_pipeline=None):
     else:
         # img_metas = scatter(data['img_metas'],'cpu')
         # data['img_metas'] = [i.data[0] for i in data['img_metas']]
-        
         img_metas = data['img_metas'].data[0]
         img = data['img']
         data = {'img': img, 'img_metas':img_metas}
@@ -274,8 +271,6 @@ with gr.Blocks() as demo:
                 inp2=gr.Image(image_mode='RGB', scale=10, label='T2')
                 inp3=gr.Image(image_mode='RGB', scale=10, label='T3')
 
-    btn.click(fn=func, inputs=inp, outputs=[inp1, inp2, inp3, out])
-
     with gr.Row():
         with gr.Column():
             gr.BarPlot(simple,
@@ -291,6 +286,8 @@ with gr.Blocks() as demo:
             out = gr.Image(image_mode='RGB', scale=10, label='Model prediction')
             # gr.Image(value='Legend.png', image_mode='RGB', scale=2, show_label=False)
   
+    btn.click(fn=func, inputs=inp, outputs=[inp1, inp2, inp3, out])
+    
     with gr.Row():
         gr.Markdown(value='### Model prediction legend')
         gr.Image(value='Legend.png', image_mode='RGB', show_label=False)