Update app.py

app.py

@@ -68,16 +68,16 @@ def inference(input_img, see_misclassified=False,num_misclassified_imgs=0,see_gr
 
     # top n classes only
     sorted_confidences = {k: sorted_confidences[k] for k in list(sorted_confidences)[:top_classes]}
-    return sorted_confidences, visualization
+    return sorted_confidences, [visualization]
 
-title = "CIFAR10 trained on ResNet18 Model with GradCAM"
-description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
+title = "CIFAR10 trained on Custom ResNet Model with GradCAM"
+description = "A Gradio interface to infer on ResNet model, and get GradCAM results"
 examples = [["examples/cat.jpg"], ["examples/plane.jpg"],["examples/dog.jpg"],["examples/truck.jpg"],["examples/bird.jpg"],["examples/ship.jpg"],["examples/horse.jpg"],["examples/frog.jpg"],["examples/deer.jpg"],["examples/car.jpg"]]
 
 demo = gr.Interface(
     inference,
-    inputs =  [gr.Image(shape=(32, 32), label="Input Image"), gr.Checkbox(label="Misclassified"),gr.Slider(0, 10, value = 0, step=1,label="Total Misclassified Images"),gr.Checkbox(label="Gradcam"),gr.Slider(0, 10, value = 0, step=1,label="Total GradCam Images"),gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?"), gr.Slider(1, 10, value=3, step=1, label="How many top classes?")],
-    outputs = [gr.Label(), gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)],
+    inputs =  [gr.Image(shape=(32, 32), label="Input Image"), gr.Checkbox(label="Misclassified"),gr.Number(value=2,minimum=0,maximum=10,label="Total Misclassified Images"),gr.Checkbox(label="Gradcam"),gr.Number(value=2,minimum=0,maximum=10,label="Total GradCam Images"),gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?"), gr.Slider(1, 10, value=3, step=1, label="How many top classes?")],
+    outputs = [gr.Label(), gr.Gallery(label="Output Images", show_label=False, elem_id="gallery").style(columns=[2], rows=[5], object_fit="contain", height="auto")],
     title = title,
     description = description,
     examples = examples)