Update app.py

app.py

@@ -52,6 +52,8 @@ def inference(input_img, see_misclassified=False,num_misclassified_imgs=0,see_gr
     softmax = torch.nn.Softmax(dim=0)
     o = softmax(outputs.flatten())
     confidences = {classes[i]: float(o[i]) for i in range(10)}
+    sorted_confidences = dict(sorted(confidences.items(), key=lambda x:x[1], reverse=True))
+    
     _, prediction = torch.max(outputs, 1)
 
     # gradcam
@@ -69,8 +71,8 @@ def inference(input_img, see_misclassified=False,num_misclassified_imgs=0,see_gr
       visualization = org_img
 
     # top n classes only
-    confidences = {k: confidences[k] for k in list(confidences)[:top_classes]}
-    return confidences, visualization
+    sorted_confidences = {k: sorted_confidences[k] for k in list(sorted_confidences)[:top_classes]}
+    return sorted_confidences, visualization
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"