.

app.py

@@ -4,13 +4,17 @@ import gradio as gr
 
 model2 = torch.hub.load(
     "AK391/animegan2-pytorch:main",
-    "generator", pretrained=True, device="cpu", progress=False)
-model1 = torch.hub.load(
-    "AK391/animegan2-pytorch:main",
-    "generator", pretrained="face_paint_512_v1", device="cpu")
+    "generator",
+    pretrained=True,
+    device="cpu",
+    progress=False
+)
+model1 = torch.hub.load("AK391/animegan2-pytorch:main",
+                        "generator", pretrained="face_paint_512_v1",  device="cpu")
 face2paint = torch.hub.load(
     'AK391/animegan2-pytorch:main', 'face2paint',
-    size=512, device="cpu", side_by_side=False)
+    size=512, device="cpu", side_by_side=False
+)
 
 def inference(img, ver):
     if ver == 'version 2 (🔺 robustness,🔻 stylization)':
@@ -22,5 +26,7 @@ def inference(img, ver):
 title = "Face Anime For You"
 description = "Online Demo for AnimeGanv2 Face Portrait v2. To use it, simply upload your image, or click one of the examples to load them. Please use a cropped portrait picture for best results similar to the examples below."
 article = "<p style='text-align: center'>❤ from Bruce</p>"
-examples = [['IU.png', 'version 2 (🔺 robustness,🔻 stylization)'], ['elon.png', 'version 2 (🔺 robustness,🔻 stylization)']]
-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, enable_queue=True, examples=examples, allow_flagging=False).launch()
+examples = [['elon.png', 'version 2 (🔺 robustness,🔻 stylization)'],
+            ['IU.png', 'version 2 (🔺 robustness,🔻 stylization)']]
+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, enable_queue=True, examples=examples, allow_flagging=False).launch()