Update app.py

app.py

@@ -4,6 +4,7 @@ import numpy as np
 import gradio as gr
 from PIL import Image
 from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
 from resnet import ResNet18
 import gradio as gr
 
@@ -14,6 +15,8 @@ inv_normalize = transforms.Normalize(
     mean=[-0.50/0.23, -0.50/0.23, -0.50/0.23],
     std=[1/0.23, 1/0.23, 1/0.23]
 )
+classes = ('plane', 'car', 'bird', 'cat', 'deer',
+           'dog', 'frog', 'horse', 'ship', 'truck')
 
 def inference(input_img, transparency, target_layer_number):
     transform = transforms.ToTensor()
@@ -33,5 +36,5 @@ def inference(input_img, transparency, target_layer_number):
     visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True, image_weight=transparency)
     return classes[prediction[0].item()], visualization
 
-demo = gr.Interface(inference, [gr.Image(shape=(32, 32)), gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-5, -1, value = -2, step=1, label="Which Layer?")], ["text", gr.Image(shape=(32, 32)).style(width=128, height=128)])
+demo = gr.Interface(inference, [gr.Image(shape=(32, 32), label="Input Image"), gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-5, -1, value = -2, step=1, label="Which Layer?")], ["text", gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)])
 demo.launch()