Update app.py

app.py

@@ -93,6 +93,37 @@ def server(input, output, session: Session):
         # Update reactive value
         analysis_results.set(results)
 
+    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():
@@ -105,13 +136,15 @@ def server(input, output, session: Session):
         for i, r in enumerate(results):
 
             id = "image_" + str(i)
-            opacity = ui.input_slider(id, "Opacity", 0, 1.0, 0.5),
+            opacity = ui.input_slider(id, "Opacity", 0, 1.0, 0.5)
+
+            output(create_plot(i, r, input[id]()), id="plot_" + str(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(0))),
+                        ui.column(4, ui.output_plot("plot_" + str(i))),
                     ),
                     opacity,
                     ui.h5(r['filename'], style="margin-top: 15px;"),
@@ -125,35 +158,6 @@ def server(input, output, session: Session):
                     class_="card p-3"
                 )
             )
-
-            @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 = input[id]())
-
-                ax.imshow(cv2.cvtColor(out.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))
-        
-                return fig
                 
         return ui.div(ui_output)