Update

.pre-commit-config.yaml

@@ -1,35 +1,60 @@
 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']
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    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: 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: v1.7.5
+    hooks:
+      - id: docformatter
+        args: ["--in-place"]
+  - repo: https://github.com/pycqa/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+        args: ["--profile", "black"]
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.8.0
+    hooks:
+      - id: mypy
+        args: ["--ignore-missing-imports"]
+        additional_dependencies:
+          [
+            "types-python-slugify",
+            "types-requests",
+            "types-PyYAML",
+            "types-pytz",
+          ]
+  - repo: https://github.com/psf/black
+    rev: 24.2.0
+    hooks:
+      - id: black
+        language_version: python3.10
+        args: ["--line-length", "119"]
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.7.1
+    hooks:
+      - id: nbstripout
+        args:
+          [
+            "--extra-keys",
+            "metadata.interpreter metadata.kernelspec cell.metadata.pycharm",
+          ]
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.7.1
+    hooks:
+      - id: nbqa-black
+      - id: nbqa-pyupgrade
+        args: ["--py37-plus"]
+      - id: nbqa-isort
+        args: ["--float-to-top"]

.style.yapf

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

.vscode/settings.json

@@ -0,0 +1,30 @@
+{
+    "editor.formatOnSave": true,
+    "files.insertFinalNewline": false,
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.formatOnType": true,
+        "editor.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+        }
+    },
+    "[jupyter]": {
+        "files.insertFinalNewline": false
+    },
+    "black-formatter.args": [
+        "--line-length=119"
+    ],
+    "isort.args": ["--profile", "black"],
+    "flake8.args": [
+        "--max-line-length=119"
+    ],
+    "ruff.lint.args": [
+        "--line-length=119"
+    ],
+    "notebook.output.scrolling": true,
+    "notebook.formatOnCellExecution": true,
+    "notebook.formatOnSave.enabled": true,
+        "notebook.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+        }
+}

README.md

@@ -4,7 +4,7 @@ emoji: 🐠
 colorFrom: green
 colorTo: blue
 sdk: gradio
-sdk_version: 3.36.1
+sdk_version: 4.19.2
 app_file: app.py
 pinned: false
 ---

app.py

@@ -15,29 +15,25 @@ import PIL.Image
 import torch
 import torchvision.transforms as T
 
-DESCRIPTION = '# [RF5/danbooru-pretrained](https://github.com/RF5/danbooru-pretrained)'
+DESCRIPTION = "# [RF5/danbooru-pretrained](https://github.com/RF5/danbooru-pretrained)"
 
-MODEL_REPO = 'public-data/danbooru-pretrained'
+MODEL_REPO = "public-data/danbooru-pretrained"
 
 
 def load_sample_image_paths() -> list[pathlib.Path]:
-    image_dir = pathlib.Path('images')
+    image_dir = pathlib.Path("images")
     if not image_dir.exists():
-        dataset_repo = 'hysts/sample-images-TADNE'
-        path = huggingface_hub.hf_hub_download(dataset_repo,
-                                               'images.tar.gz',
-                                               repo_type='dataset')
+        dataset_repo = "hysts/sample-images-TADNE"
+        path = huggingface_hub.hf_hub_download(dataset_repo, "images.tar.gz", repo_type="dataset")
         with tarfile.open(path) as f:
             f.extractall()
-    return sorted(image_dir.glob('*'))
+    return sorted(image_dir.glob("*"))
 
 
 def load_model(device: torch.device) -> torch.nn.Module:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO, 'resnet50-13306192.pth')
+    path = huggingface_hub.hf_hub_download(MODEL_REPO, "resnet50-13306192.pth")
     state_dict = torch.load(path)
-    model = torch.hub.load('RF5/danbooru-pretrained',
-                           'resnet50',
-                           pretrained=False)
+    model = torch.hub.load("RF5/danbooru-pretrained", "resnet50", pretrained=False)
     model.load_state_dict(state_dict)
     model.to(device)
     model.eval()
@@ -45,16 +41,21 @@ def load_model(device: torch.device) -> torch.nn.Module:
 
 
 def load_labels() -> list[str]:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO, 'class_names_6000.json')
+    path = huggingface_hub.hf_hub_download(MODEL_REPO, "class_names_6000.json")
     with open(path) as f:
         labels = json.load(f)
     return labels
 
 
 @torch.inference_mode()
-def predict(image: PIL.Image.Image, score_threshold: float,
-            transform: Callable, device: torch.device, model: torch.nn.Module,
-            labels: list[str]) -> dict[str, float]:
+def predict(
+    image: PIL.Image.Image,
+    score_threshold: float,
+    transform: Callable,
+    device: torch.device,
+    model: torch.nn.Module,
+    labels: list[str],
+) -> dict[str, float]:
     data = transform(image)
     data = data.to(device).unsqueeze(0)
     preds = model(data)[0]
@@ -72,41 +73,44 @@ def predict(image: PIL.Image.Image, score_threshold: float,
 image_paths = load_sample_image_paths()
 examples = [[path.as_posix(), 0.4] for path in image_paths]
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 model = load_model(device)
 labels = load_labels()
 
-transform = T.Compose([
-    T.Resize(360),
-    T.ToTensor(),
-    T.Normalize(mean=[0.7137, 0.6628, 0.6519], std=[0.2970, 0.3017, 0.2979]),
-])
+transform = T.Compose(
+    [
+        T.Resize(360),
+        T.ToTensor(),
+        T.Normalize(mean=[0.7137, 0.6628, 0.6519], std=[0.2970, 0.3017, 0.2979]),
+    ]
+)
 
-fn = functools.partial(predict,
-                       transform=transform,
-                       device=device,
-                       model=model,
-                       labels=labels)
+fn = functools.partial(predict, transform=transform, device=device, model=model, labels=labels)
 
-with gr.Blocks(css='style.css') as demo:
+with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Row():
         with gr.Column():
-            image = gr.Image(label='Input', type='pil')
-            threshold = gr.Slider(label='Score Threshold',
-                                  minimum=0,
-                                  maximum=1,
-                                  step=0.05,
-                                  value=0.4)
-            run_button = gr.Button('Run')
+            image = gr.Image(label="Input", type="pil")
+            threshold = gr.Slider(label="Score Threshold", minimum=0, maximum=1, step=0.05, value=0.4)
+            run_button = gr.Button()
         with gr.Column():
-            result = gr.Label(label='Output')
+            result = gr.Label(label="Output")
 
     inputs = [image, threshold]
-    gr.Examples(examples=examples,
-                inputs=inputs,
-                outputs=result,
-                fn=fn,
-                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
-    run_button.click(fn=fn, inputs=inputs, outputs=result, api_name='predict')
-demo.queue(max_size=15).launch()
+    gr.Examples(
+        examples=examples,
+        inputs=inputs,
+        outputs=result,
+        fn=fn,
+        cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
+    )
+    run_button.click(
+        fn=fn,
+        inputs=inputs,
+        outputs=result,
+        api_name="predict",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=15).launch()

requirements.txt

@@ -1,2 +1,2 @@
-torch==1.13.1
-torchvision==0.14.1
+torch==2.0.1
+torchvision==0.15.2

style.css

@@ -1,3 +1,11 @@
 h1 {
   text-align: center;
+  display: block;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: #fff;
+  background: #1565c0;
+  border-radius: 100vh;
 }