Update

.pre-commit-config.yaml

@@ -0,0 +1,35 @@
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.2.0
+  hooks:
+  - id: check-executables-have-shebangs
+  - id: check-json
+  - id: check-merge-conflict
+  - id: check-shebang-scripts-are-executable
+  - id: check-toml
+  - id: check-yaml
+  - id: double-quote-string-fixer
+  - id: end-of-file-fixer
+  - id: mixed-line-ending
+    args: ['--fix=lf']
+  - id: requirements-txt-fixer
+  - id: trailing-whitespace
+- repo: https://github.com/myint/docformatter
+  rev: v1.4
+  hooks:
+  - id: docformatter
+    args: ['--in-place']
+- repo: https://github.com/pycqa/isort
+  rev: 5.12.0
+  hooks:
+    - id: isort
+- repo: https://github.com/pre-commit/mirrors-mypy
+  rev: v0.991
+  hooks:
+    - id: mypy
+      args: ['--ignore-missing-imports']
+- repo: https://github.com/google/yapf
+  rev: v0.32.0
+  hooks:
+  - id: yapf
+    args: ['--parallel', '--in-place']

.style.yapf

@@ -0,0 +1,5 @@
+[style]
+based_on_style = pep8
+blank_line_before_nested_class_or_def = false
+spaces_before_comment = 2
+split_before_logical_operator = true

README.md

@@ -4,7 +4,7 @@ emoji: 😻
 colorFrom: indigo
 colorTo: green
 sdk: gradio
-sdk_version: 3.0.5
+sdk_version: 3.19.1
 app_file: app.py
 pinned: false
 ---

app.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import argparse
 import functools
 import os
 import pathlib
@@ -24,23 +23,8 @@ from utils import generate_label
 
 TITLE = 'CelebAMask-HQ Face Parsing'
 DESCRIPTION = 'This is an unofficial demo for the model provided in https://github.com/switchablenorms/CelebAMask-HQ.'
-ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.celebamask-hq-face-parsing" alt="visitor badge"/></center>'
 
-TOKEN = os.environ['TOKEN']
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    return parser.parse_args()
+HF_TOKEN = os.getenv('HF_TOKEN')
 
 
 @torch.inference_mode()
@@ -62,7 +46,7 @@ def predict(image: PIL.Image.Image, model: nn.Module, transform: Callable,
 def load_model(device: torch.device) -> nn.Module:
     path = hf_hub_download('hysts/CelebAMask-HQ-Face-Parsing',
                            'models/model.pth',
-                           use_auth_token=TOKEN)
+                           use_auth_token=HF_TOKEN)
     state_dict = torch.load(path, map_location='cpu')
     model = unet()
     model.load_state_dict(state_dict)
@@ -71,46 +55,30 @@ def load_model(device: torch.device) -> nn.Module:
     return model
 
 
-def main():
-    args = parse_args()
-    device = torch.device(args.device)
-
-    model = load_model(device)
-    transform = T.Compose([
-        T.Resize((512, 512), interpolation=PIL.Image.NEAREST),
-        T.ToTensor(),
-        T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-    ])
-
-    func = functools.partial(predict,
-                             model=model,
-                             transform=transform,
-                             device=device)
-    func = functools.update_wrapper(func, predict)
-
-    image_dir = pathlib.Path('images')
-    examples = [[path.as_posix()] for path in sorted(image_dir.glob('*.jpg'))]
-
-    gr.Interface(
-        func,
-        gr.inputs.Image(type='pil', label='Input'),
-        [
-            gr.outputs.Image(type='numpy', label='Predicted Labels'),
-            gr.outputs.Image(type='numpy', label='Masked'),
-        ],
-        examples=examples,
-        title=TITLE,
-        description=DESCRIPTION,
-        article=ARTICLE,
-        theme=args.theme,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+model = load_model(device)
+transform = T.Compose([
+    T.Resize((512, 512), interpolation=PIL.Image.NEAREST),
+    T.ToTensor(),
+    T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+])
+
+func = functools.partial(predict,
+                         model=model,
+                         transform=transform,
+                         device=device)
+
+image_dir = pathlib.Path('images')
+examples = [[path.as_posix()] for path in sorted(image_dir.glob('*.jpg'))]
+
+gr.Interface(
+    fn=func,
+    inputs=gr.Image(label='Input', type='pil'),
+    outputs=[
+        gr.Image(label='Predicted Labels', type='numpy'),
+        gr.Image(label='Masked', type='numpy'),
+    ],
+    examples=examples,
+    title=TITLE,
+    description=DESCRIPTION,
+).queue().launch(show_api=False)