Update app.py

app.py

@@ -107,38 +107,38 @@ def server(input, output, session: Session):
             id = "image_" + str(i)
             opacity = ui.input_slider(id, "Opacity", 0, 1.0, 0.5)
             
-            def create_plot_function(name, opacity):
-                @render.plot
-                def plot_predicitons():
-                    fig, ax = plt.subplots()
+            # def create_plot_function(name, opacity):
+            #     @render.plot
+            #     def plot_predicitons():
+            #         fig, ax = plt.subplots()
         
-                    ax = plt.Axes(fig, [0., 0., 1., 1.])
-                    ax.set_axis_off()
-                    fig.add_axes(ax)
+            #         ax = plt.Axes(fig, [0., 0., 1., 1.])
+            #         ax.set_axis_off()
+            #         fig.add_axes(ax)
                     
-                    v = Visualizer(r["image"][:, :, ::-1],
-                                   scale=1, instance_mode=ColorMode.SEGMENTATION, font_size_scale=1)
+            #         v = Visualizer(r["image"][:, :, ::-1],
+            #                        scale=1, instance_mode=ColorMode.SEGMENTATION, font_size_scale=1)
         
-                    colours = []
-                    for cls in r["instances"].pred_classes:
-                        if cls == 0:
-                            colours.append([1,0,0])
-                        elif cls == 1:
-                            colours.append([1,1,0])
-                        elif cls == 2:
-                            colours.append([0,0,0])
+            #         colours = []
+            #         for cls in r["instances"].pred_classes:
+            #             if cls == 0:
+            #                 colours.append([1,0,0])
+            #             elif cls == 1:
+            #                 colours.append([1,1,0])
+            #             elif cls == 2:
+            #                 colours.append([0,0,0])
         
-                    out = v.overlay_instances(masks = r["instances"].pred_masks.to("cpu"),
-                                              assigned_colors = colours, 
-                                              alpha = opacity)
-                    ax.imshow(cv2.cvtColor(out.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))
+            #         out = v.overlay_instances(masks = r["instances"].pred_masks.to("cpu"),
+            #                                   assigned_colors = colours, 
+            #                                   alpha = opacity)
+            #         ax.imshow(cv2.cvtColor(out.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))
                     
-                    return fig
+            #         return fig
                 
-                plot_prediction.__name__ = name
-                return plot_prediction
+            #     plot_prediction.__name__ = name
+            #     return plot_prediction
             
-            plot_function = create_plot_function("plot_" + str(i), input[id]())
+            # plot_function = create_plot_function("plot_" + str(i), input[id]())
                 
             output.append(
                 ui.div(