Update app.py

app.py

@@ -9,9 +9,7 @@ from PIL import Image
 import numpy as np
 os.mkdir("data")
 os.mkdir("dataout")
-# Images
-torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2018/08/12/16/59/ara-3601194_1280.jpg', 'parrot.jpg')
-torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/10/21/14/46/fox-1758183_1280.jpg', 'fox.jpg')
+torch.hub.download_url_to_file('https://images.pexels.com/photos/103123/pexels-photo-103123.jpeg', 'person.jpeg')
 model = hub.Module(name='U2Net')
 def infer(img):
   img.save("./data/data.png")
@@ -31,11 +29,10 @@ def infer(img):
   return "./dataout/data_mask.png"
 inputs = gr.inputs.Image(type='pil', label="Original Image")
 outputs = gr.outputs.Image(type="file",label="output")
-title = "U^2-Net"
-description = "demo for U^2-Net. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2005.09007'>U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection</a> | <a href='https://github.com/xuebinqin/U-2-Net'>Github Repo</a></p>"
+title = "LaMa Image Inpainting"
+description = "Gradio demo for LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.07161' target='_blank'>Resolution-robust Large Mask Inpainting with Fourier Convolutions</a> | <a href='https://github.com/saic-mdal/lama' target='_blank'>Github Repo</a></p>"
 examples = [
-  ['fox.jpg'],
-  ['parrot.jpg']
+  ['person.jpeg']
 ]
 gr.Interface(infer, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()