Update app.py

app.py

@@ -78,7 +78,8 @@ def inference_visualization(input_img, transparency = 0.5, target_layer_number =
 # def gradio_callback(view_gradcam, num_gradcam_images, layer_name, opacity,
 #                     view_misclassified, num_misclassified_images,
 #                         input_img,submit):
-def gradio_callback(view_grad_cam, input_img, transparency = 0.5, target_layer_number = -1):
+def gradio_callback(view_grad_cam, num_gradcam_images, view_misclassified, num_misclassified_images,
+                    input_img, transparency = 0.5, target_layer_number = -1):
     
     confidence = inference_confidences(input_img, transparency = 0.5, target_layer_number = -1)
     visualization = inference_visualization(input_img, transparency = 0.5, target_layer_number = -1)
@@ -87,7 +88,7 @@ def gradio_callback(view_grad_cam, input_img, transparency = 0.5, target_layer_n
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "Gradio interface to infer on ResNet18 model, and get GradCAM results"
-examples = [["Yes","cat.jpg", 0.5, -1], ["Yes","dog.jpg", 0.5, -1]]
+examples = [["Yes",5,"Yes",5,"cat.jpg", 0.5, -1], ["Yes",5,"Yes",5,"dog.jpg", 0.5, -1]]
 
 demo = gr.Interface(
     # inference, 
@@ -110,6 +111,9 @@ demo = gr.Interface(
         # gr.Image(shape=(32, 32), label="Input Image")
         # gr.Radio(["Yes", "No"], label="View GradCAM images?"),
         gr.Radio(["Yes", "No"], label="Location", info="GradCAM images?"),
+        gr.Number(label="Number of GradCAM images to view", default=5, max=10),
+        gr.Radio(["Yes", "No"], label="View misclassified images?"),
+        gr.Number(label="Number of misclassified images to view", default=5, min=1, max=10),
         gr.Image(shape=(32, 32), label="Input Image"), 
         gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), 
         gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?")