Minor bug fix

app.py

@@ -27,7 +27,7 @@ test_loader = dataset.get_test_data_loader(**dataloader_args)
 classes = ('plane', 'car', 'bird', 'cat', 'deer',
            'dog', 'frog', 'horse', 'ship', 'truck')
 
-cache_dict = {"missclassified_images": None, "is_missclassified_images": None, "num_missclassified_images": None}
+cache_dict = {"misclassified_images": None, "is_misclassified_images": None, "num_misclassified_images": None}
 
 def resize_image_pil(image, new_width, new_height):
 
@@ -51,7 +51,7 @@ def resize_image_pil(image, new_width, new_height):
     return resized
 
 def inference(input_img, is_grad_cam=True, transparency = 0.5, target_layer_number = -1,
-              top_predictions=3, is_missclassified_images=True, num_missclassified_images=10):
+              top_predictions=3, is_misclassified_images=True, num_misclassified_images=10):
     input_img = resize_image_pil(input_img, 32, 32)
     
     input_img = np.array(input_img)
@@ -86,22 +86,22 @@ def inference(input_img, is_grad_cam=True, transparency = 0.5, target_layer_numb
     # Pick the top n predictions
     top_n_confidences = dict(list(sorted_confidences.items())[:top_predictions])
 
-    if (is_missclassified_images != cache_dict["num_missclassified_images"] or
-         num_missclassified_images != cache_dict["num_missclassified_images"]):
-        cache_dict["num_missclassified_images"] = is_missclassified_images
-        cache_dict["num_missclassified_images"] = num_missclassified_images
-        if is_missclassified_images:
+    if (is_misclassified_images != cache_dict["num_misclassified_images"] or
+         num_misclassified_images != cache_dict["num_misclassified_images"]):
+        cache_dict["num_misclassified_images"] = is_misclassified_images
+        cache_dict["num_misclassified_images"] = num_misclassified_images
+        if is_misclassified_images:
             # Get the misclassified data from test dataset
             misclassified_data = get_misclassified_data(model, device, test_loader)
             # Plot the misclassified data
-            misclassified_images = display_cifar_misclassified_data(misclassified_data, number_of_samples=num_missclassified_images)
-            cache_dict["missclassified_images"] = misclassified_images
+            misclassified_images = display_cifar_misclassified_data(misclassified_data, number_of_samples=num_misclassified_images)
+            cache_dict["misclassified_images"] = misclassified_images
         else:
-            missclassified_images = None
+            misclassified_images = None
     else:
-        missclassified_images = cache_dict["missclassified_images"]
+        misclassified_images = cache_dict["misclassified_images"]
     
-    return classes[prediction[0].item()], visualization, top_n_confidences, missclassified_images
+    return classes[prediction[0].item()], visualization, top_n_confidences, misclassified_images
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
@@ -124,13 +124,13 @@ demo = gr.Interface(
         gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?"),
         gr.Slider(2, 10, value=3, step=1, label="Number of Top Classes"),
         gr.Checkbox(label="Show Misclassified Images"),
-        gr.Slider(5, 40, value=10, step=5, label="Number of Missclassified Images")
+        gr.Slider(5, 40, value=10, step=5, label="Number of Misclassified Images")
         ], 
     outputs = [
         "text", 
         gr.Image(width=256, height=256, label="Output"),
         gr.Label(label="Top Classes"),
-        gr.Plot(label="Missclassified Images")
+        gr.Plot(label="Misclassified Images")
         ],
     title = title,
     description = description,