Update app.py

app.py

@@ -48,7 +48,40 @@ app_ui = ui.page_fluid(
     )
 )
 
+@module.ui
+def plot_ui():
+    return ui.card(ui.column(4, ui.output_plot("plot_prediction")))
 
+@module.server
+def plot_server(input, output, session, r, opacity):
+    @output
+    @render.plot
+    def plot_prediction():
+        fig, ax = plt.subplots()
+
+        # 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)
+
+        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))
+
+        return fig
 
 def server(input, output, session: Session):
     analysis_results = reactive.Value([])
@@ -93,38 +126,6 @@ def server(input, output, session: Session):
         # Update reactive value
         analysis_results.set(results)
 
-    @render.plot
-    def create_plot(i, r, opacity):
-        @output(id="plot_" + str(i))
-        @render.plot
-        def plot_prediction():
-            fig, ax = plt.subplots()
-    
-            # 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)
-    
-            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))
-    
-            return fig
-        return plot_prediction
-    
     @output
     @render.ui
     def results_container():
@@ -139,13 +140,13 @@ def server(input, output, session: Session):
             id = "image_" + str(i)
             opacity = ui.input_slider(id, "Opacity", 0, 1.0, 0.5)
 
-            output(create_plot(i, r, input[id]()), id="plot_" + str(i))
+            plot_server(f"plot_{i}")
             
             ui_output.append(
                 ui.div(
                     ui.row(
                         ui.column(4, ui.img(src=f"data:image/png;base64,{r['image_base64']}")),
-                        ui.column(4, ui.output_plot("plot_" + str(i))),
+                        plot_ui(f"plot_{i}"),
                     ),
                     opacity,
                     ui.h5(r['filename'], style="margin-top: 15px;"),