Update app.py

app.py

@@ -9,7 +9,6 @@ from PIL import Image
 import numpy as np
 os.mkdir("data")
 os.mkdir("dataout")
-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")
@@ -30,9 +29,9 @@ def infer(img):
 inputs = gr.inputs.Image(type='pil', label="Original Image")
 outputs = gr.outputs.Image(type="file",label="output")
 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. Masks are generated by <a href='https://huggingface.co/spaces/akhaliq/U-2-Net' target='_blank'>U^2net</a>"
+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. Masks are generated by U^2net"
 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 = [
-  ['person.jpeg']
+  ['person512.png']
 ]
 gr.Interface(infer, inputs, outputs, title=title, description=description, article=article, examples=examples, enable_queue=True).launch()