test

app.py

@@ -37,20 +37,22 @@ def image_mod(image, noise_level, denoiser):
     return torch_to_pil(noisy), torch_to_pil(estimated)
 
 
-input_image = gr.Image(label='Input Image')
+input_image = gr.Image(label='Input Image', examples=[])
 output_images = gr.Image(label='Denoised Image')
 noise_image = gr.Image(label='Noisy Image')
 input_image_output = gr.Image(label='Input Image')
 
 noise_levels = gr.Dropdown(choices=[0.1, 0.2, 0.3, 0.4, 0.5], value=0.1, label='Noise Level')
 
-denoiser = gr.Dropdown(choices=['DnCNN', 'DRUNet', 'BM3D', 'MedianFilter'], value=0.1, label='DRUNet')
+denoiser = gr.Dropdown(choices=['DnCNN', 'DRUNet', 'BM3D', 'MedianFilter'], value='DnCNN', label='Denoiser')
 
 demo = gr.Interface(
     image_mod,
     inputs=[input_image, noise_levels, denoiser],
+    examples=[['https://deepinv.github.io/deepinv/_static/deepinv_logolarge.png', 0.1, 'DnCNN']],
     outputs=[noise_image, output_images],
     title="Image Denoising with DeepInverse",
+    description="Denoise an image using a variety of denoisers and noise levels using the deepinverse library (https://deepinv.github.io/). We only include lightweight models like DnCNN and MedianFilter as this example is intended to be run on a CPU.",
 )
 
 demo.launch()