Create app.py

app.py

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+import os
+import gradio as gr
+from pathlib import Path
+	
+	
+os.system("git clone https://github.com/YangtaoWANG95/TokenCut.git")
+os.chdir("TokenCut")
+
+os.system("wget https://raw.githubusercontent.com/YangtaoWANG95/TokenCut/master/examples/VOC07_000064.jpg -O parrot.jpg")
+	
+def inference(img):
+  os.system("python main_tokencut.py --image_path "+img+" --visualize all --resize 480")
+  filename = Path(img).stem
+  return "./outputs/TokenCut-vit_small16_k/"+filename+"_TokenCut_attn.jpg","./outputs/TokenCut-vit_small16_k/"+filename+"_TokenCut_pred.jpg"
+ 
+title="TokenCut"
+description="Gradio demo for TokenCut: Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut. To use it, simply upload your image or click on one of the examples to load them. We resize the smaller edge of the image to 480 accelerate inference time. Read more at the links below"
+
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2202.11539' target='_blank'>Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut</a> | <a href='https://github.com/YangtaoWANG95/TokenCut' target='_blank'>Github Repo</a></p>"
+
+examples=[['parrot.jpg']]
+gr.Interface(inference,gr.inputs.Image(type="filepath"),[gr.outputs.Image(type="file",label="TokenCut_attn"),gr.outputs.Image(type="file",label="TokenCut_predication")],title=title,description=description,article=article,examples=examples).launch(enable_queue=True)