Update app.py

app.py

@@ -16,7 +16,7 @@ import torchvision.transforms as T
 from custom_resnet import LitResnet
 
 classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
-
+top_classes_g = 3
 wrong_img = pd.read_csv('misclassified_images.csv')
 wrong_img_no = len(wrong_img)
 
@@ -57,9 +57,9 @@ def image_classifier(input_image, top_classes=3, show_cam=True, target_layers=[2
     _, label = torch.max(output, 1)
     
     #confidences.sort(key=lambda x: x[1], reverse=True)
-    confidences = OrderedDict(confidences[:top_classes])
+    #confidences = OrderedDict(confidences[:top_classes])
     #label = torch.argmax(output).item()
-
+    top_classes_g = top_classes
     outputs = list()
     if show_cam:
         for layer in target_layers:
@@ -82,7 +82,7 @@ demo1 = gr.Interface(
         gr.Slider(0, 1, value=0.5, label="Transparency", step=0.1,
                   info="Set Transparency of CAMs")
     ],
-    outputs=[gr.Gallery(label="Output Images", columns=2, rows=2), gr.Label(label='Top Classes')],
+    outputs=[gr.Gallery(label="Output Images", columns=2, rows=2), gr.Label(label='Top Classes',num_top_classes=top_classes_g)],
     examples=examples
 )