Update app.py

app.py

@@ -83,7 +83,7 @@ inference_model = LitResnet.load_from_checkpoint("cifar10_customresnet_20_epoch.
 classes = ('plane', 'car', 'bird', 'cat', 'deer',
            'dog', 'frog', 'horse', 'ship', 'truck')
 
-def inference(input_img, see_misclassified,num_misclassified_imgs,see_gradcam,num_gradcam_imgs,transparency = 0.85, target_layer_number = -1,top_classes=3):
+def inference(input_img, see_misclassified=False,num_misclassified_imgs=0,see_gradcam=False,num_gradcam_imgs=0,transparency = 0.85, target_layer_number = -1,top_classes=3):
 
     # if see_misclassified: # show misclassified images
     #   org_img   =  cv2.imread('/content/drive/MyDrive/AI/ERA_course/session12/example_images/img_eg_0.jpg')
@@ -127,7 +127,9 @@ def inference(input_img, see_misclassified,num_misclassified_imgs,see_gradcam,nu
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
-examples = [["img_eg_0.jpg", False,0,False,0.5, -1,3], ["img_eg_1.jpg", False,0,False,0.5, -1,3],["img_eg_2.jpg", False,0,False,0.5, -1,3],["img_eg_3.jpg", False,0,False,0.5, -1,3],["img_eg_4.jpg", False,0,False,0.5, -1,3],["img_eg_5.jpg", False,0,False,0.5, -1,3],["img_eg_6.jpg", False,0,False,0.5, -1,3],["img_eg_7.jpg", False,0,False,0.5, -1,3],["img_eg_8.jpg", False,0,False,0.5, -1,3]]
+#examples = [["img_eg_0.jpg", False,0,False,0.5, -1,3], ["img_eg_1.jpg", False,0,False,0.5, -1,3],["img_eg_2.jpg", False,0,False,0.5, -1,3],["img_eg_3.jpg", False,0,False,0.5, -1,3],["img_eg_4.jpg", False,0,False,0.5, -1,3],["img_eg_5.jpg", False,0,False,0.5, -1,3],["img_eg_6.jpg", False,0,False,0.5, -1,3],["img_eg_7.jpg", False,0,False,0.5, -1,3],["img_eg_8.jpg", False,0,False,0.5, -1,3]]
+examples = [["img_eg_0.jpg"], ["img_eg_1.jpg"],["img_eg_2.jpg"],["img_eg_3.jpg"],["img_eg_4.jpg"],["img_eg_5.jpg"],["img_eg_6.jpg"],["img_eg_7.jpg"],["img_eg_8.jpg"],["img_eg_9.jpg"]]
+
 
 demo = gr.Interface(
     inference,