Update app.py

app.py

@@ -19,7 +19,7 @@ classes = ('plane', 'car', 'bird', 'cat', 'deer',
 model = LITResNet(classes)
 model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False)
 
-def inference(input_img, show_gradcam, num_gradcam, layer_num, opacity, show_misclassified, num_misclassified, num_top_classes):
+def inference(input_img, input_img_label, show_gradcam, num_gradcam, layer_num, opacity, show_misclassified, num_misclassified, num_top_classes):
     input_img = np.array(Image.fromarray(np.array(input_img)).resize((32,32)))
     org_img = input_img
 
@@ -32,7 +32,7 @@ def inference(input_img, show_gradcam, num_gradcam, layer_num, opacity, show_mis
     confidences = {classes[i]: float(o[i]) for i in range(10)}
 
     _, prediction = torch.max(outputs, 1)
-    is_misclassified = (prediction != labels.index(input_img_label))
+    is_misclassified = (prediction != classes.index(input_img_label))
 
     if show_gradcam:
         target_layers = [model.layer2[layer_num]]
@@ -58,9 +58,9 @@ title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, get GradCAM results, and view misclassified images"
 
 examples = [
-    ["plane.jpeg", True, 1, -1, 0.5, False, 0, 3],
-    ["car.jpg", True, 2, -2, 0.7, True, 1, 5],
-    ["bird.jpg", False, 0, -1, 0.5, False, 0, 3],
+    ["plane.jpg", "plane", True, 1, -1, 0.5, False, 0, 3],
+    ["car.jpg", "car", True, 2, -2, 0.7, True, 1, 5],
+    ["bird.jpg", "bird", False, 0, -1, 0.5, False, 0, 3],
     # Add more examples as needed
 ]
 
@@ -68,6 +68,7 @@ demo = gr.Interface(
     inference,
     inputs=[
         gr.Image(width=256, height=256, label="Input Image"),
+        gr.Dropdown(choices=classes, label="Ground Truth Label"),
         gr.Checkbox(value=True, label="Show GradCAM"),
         gr.Slider(1, 5, value=1, step=1, label="Number of GradCAM Images"),
         gr.Slider(-2, -1, value=-2, step=1, label="Which Layer?"),