Updated default values

app.py

@@ -24,10 +24,6 @@ dataloader_args = dict(shuffle=True, batch_size=128, num_workers=4, pin_memory=T
 
 test_loader = dataset.get_test_data_loader(**dataloader_args)
 
-inv_normalize = transforms.Normalize(
-    mean=[-0.48215841/0.24348513, -0.44653091/0.26158784, -0.49139968/0.24703223],
-    std=[1/0.24348513, 1/0.26158784, 1/0.24703223]
-)
 classes = ('plane', 'car', 'bird', 'cat', 'deer',
            'dog', 'frog', 'horse', 'ship', 'truck')
 
@@ -100,16 +96,16 @@ def inference(input_img, is_grad_cam=True, transparency = 0.5, target_layer_numb
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
-examples = [["cat.jpg", True, 0.5, -1, 3], 
-            ["dog.jpg", True, 0.5, -1, 3], 
-            ["bird.jpg", True, 0.5, -1, 3], 
-            ["car.jpg", True, 0.5, -1, 3], 
-            ["deer.jpg", True, 0.5, -1, 3], 
-            ["frog.jpg", True, 0.5, -1, 3], 
-            ["horse.jpg", True, 0.5, -1, 3], 
-            ["plane.jpg", True, 0.5, -1, 3], 
-            ["ship.jpg", True, 0.5, -1, 3], 
-            ["truck.jpg", True, 0.5, -1, 3]]
+examples = [["cat.jpg", True, 0.5, -1, 3, True, 10], 
+            ["dog.jpg", True, 0.5, -1, 3, True, 10], 
+            ["bird.jpg", True, 0.5, -1, 3, True, 10], 
+            ["car.jpg", True, 0.5, -1, 3, True, 10], 
+            ["deer.jpg", True, 0.5, -1, 3, True, 10], 
+            ["frog.jpg", True, 0.5, -1, 3, True, 10], 
+            ["horse.jpg", True, 0.5, -1, 3, True, 10], 
+            ["plane.jpg", True, 0.5, -1, 3, True, 10], 
+            ["ship.jpg", True, 0.5, -1, 3, True, 10], 
+            ["truck.jpg", True, 0.5, -1, 3, True, 10]]
 demo = gr.Interface(
     inference, 
     inputs = [