Upload app.py

app.py

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+import os
+os.system("pip install gradio==2.8.0b3")
+from PIL import Image
+import torch
+import gradio as gr
+
+
+
+model2 = torch.hub.load(
+    "AK391/animegan2-pytorch:main",
+    "generator",
+    pretrained=True,
+    device="cuda",
+    progress=False
+)
+
+
+model1 = torch.hub.load("AK391/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1",  device="cuda")
+face2paint = torch.hub.load(
+    'AK391/animegan2-pytorch:main', 'face2paint', 
+    size=512, device="cuda",side_by_side=False
+)
+def inference(img, ver):
+    if ver == 'version 2 (🔺 robustness,🔻 stylization)':
+        out = face2paint(model2, img)
+    else:
+        out = face2paint(model1, img)
+    return out
+  
+title = "AnimeGANv2"
+description = "Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below."
+article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
+examples=[['groot.jpeg','version 2 (🔺 robustness,🔻 stylization)'],['bill.png','version 1 (🔺 stylization, 🔻 robustness)'],['tony.png','version 1 (🔺 stylization, 🔻 robustness)'],['elon.png','version 2 (🔺 robustness,🔻 stylization)'],['IU.png','version 1 (🔺 stylization, 🔻 robustness)'],['billie.png','version 2 (🔺 robustness,🔻 stylization)'],['will.png','version 2 (🔺 robustness,🔻 stylization)'],['beyonce.png','version 1 (🔺 stylization, 🔻 robustness)'],['gongyoo.jpeg','version 1 (🔺 stylization, 🔻 robustness)']]
+gr.Interface(inference, [gr.inputs.Image(type="pil"),gr.inputs.Radio(['version 1 (🔺 stylization, 🔻 robustness)','version 2 (🔺 robustness,🔻 stylization)'], type="value", default='version 2 (🔺 robustness,🔻 stylization)', label='version')
+], gr.outputs.Image(type="pil"),title=title,description=description,article=article,examples=examples,allow_flagging=False,allow_screenshot=False).launch(enable_queue=True,cache_examples=True)