updated code for top classes in inference code

app.py

@@ -32,7 +32,12 @@ def inference(input_img, transparency = 0.5, target_layer_number = -1, num_top_c
     rgb_img = np.transpose(img, (1, 2, 0))
     rgb_img = rgb_img.numpy()
     visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
-    return confidences, visualization
+
+    # Sort confidences dictionary in descending order of values and take top num_top_classes
+    sorted_confidences = {k: v for k, v in sorted(confidences.items(), key=lambda item: item[1], reverse=True)}
+    top_classes = list(sorted_confidences.keys())[:num_top_classes]
+
+    return top_classes, visualization
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
@@ -46,7 +51,7 @@ demo = gr.Interface(
               gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), 
               gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?"),
               gr.Slider(0, 10, value = 1, step=1, label="Number of Top Classes")], 
-    outputs = [gr.Label(num_top_classes=5), gr.Image(shape=(32, 32), label="Output", style={"width": "128px", "height": "128px"})],
+    outputs = [gr.Label(num_top_classes=10), gr.Image(shape=(32, 32), label="Output", style={"width": "128px", "height": "128px"})],
     title = title,
     description = description,
     examples = examples,