Update

.pre-commit-config.yaml

@@ -1,36 +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']
-      additional_dependencies: ['types-python-slugify']
-- 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

@@ -1,18 +1,30 @@
 {
-    "python.linting.enabled": true,
-    "python.linting.flake8Enabled": true,
-    "python.linting.pylintEnabled": false,
-    "python.linting.lintOnSave": true,
-    "python.formatting.provider": "yapf",
-    "python.formatting.yapfArgs": [
-        "--style={based_on_style: pep8, indent_width: 4, blank_line_before_nested_class_or_def: false, spaces_before_comment: 2, split_before_logical_operator: true}"
-    ],
+    "editor.formatOnSave": true,
+    "files.insertFinalNewline": false,
     "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
         "editor.formatOnType": true,
         "editor.codeActionsOnSave": {
-            "source.organizeImports": true
+            "source.organizeImports": "explicit"
         }
     },
-    "editor.formatOnSave": true,
-    "files.insertFinalNewline": true
+    "[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: gray
 colorTo: purple
 sdk: gradio
-sdk_version: 3.37.0
+sdk_version: 4.19.2
 app_file: app.py
 pinned: false
 ---

app.py

@@ -13,31 +13,26 @@ import numpy as np
 import PIL.Image
 import tensorflow as tf
 
-DESCRIPTION = '# [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru)'
+DESCRIPTION = "# [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru)"
 
 
 def load_sample_image_paths() -> list[pathlib.Path]:
-    image_dir = pathlib.Path('images')
+    image_dir = pathlib.Path("images")
     if not image_dir.exists():
-        path = huggingface_hub.hf_hub_download(
-            'public-data/sample-images-TADNE',
-            'images.tar.gz',
-            repo_type='dataset')
+        path = huggingface_hub.hf_hub_download("public-data/sample-images-TADNE", "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() -> tf.keras.Model:
-    path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru',
-                                           'model-resnet_custom_v3.h5')
+    path = huggingface_hub.hf_hub_download("public-data/DeepDanbooru", "model-resnet_custom_v3.h5")
     model = tf.keras.models.load_model(path)
     return model
 
 
 def load_labels() -> list[str]:
-    path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru',
-                                           'tags.txt')
+    path = huggingface_hub.hf_hub_download("public-data/DeepDanbooru", "tags.txt")
     with open(path) as f:
         labels = [line.strip() for line in f.readlines()]
     return labels
@@ -47,18 +42,13 @@ model = load_model()
 labels = load_labels()
 
 
-def predict(
-        image: PIL.Image.Image, score_threshold: float
-) -> tuple[dict[str, float], dict[str, float], str]:
+def predict(image: PIL.Image.Image, score_threshold: float) -> tuple[dict[str, float], dict[str, float], str]:
     _, height, width, _ = model.input_shape
     image = np.asarray(image)
-    image = tf.image.resize(image,
-                            size=(height, width),
-                            method=tf.image.ResizeMethod.AREA,
-                            preserve_aspect_ratio=True)
+    image = tf.image.resize(image, size=(height, width), method=tf.image.ResizeMethod.AREA, preserve_aspect_ratio=True)
     image = image.numpy()
     image = dd.image.transform_and_pad_image(image, width, height)
-    image = image / 255.
+    image = image / 255.0
     probs = model.predict(image[None, ...])[0]
     probs = probs.astype(float)
 
@@ -72,45 +62,42 @@ def predict(
         if prob < score_threshold:
             break
         result_threshold[label] = prob
-    result_text = ', '.join(result_all.keys())
+    result_text = ", ".join(result_all.keys())
     return result_threshold, result_all, result_text
 
 
 image_paths = load_sample_image_paths()
 examples = [[path.as_posix(), 0.5] for path in image_paths]
 
-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')
-            score_threshold = gr.Slider(label='Score threshold',
-                                        minimum=0,
-                                        maximum=1,
-                                        step=0.05,
-                                        value=0.5)
-            run_button = gr.Button('Run')
+            image = gr.Image(label="Input", type="pil")
+            score_threshold = gr.Slider(label="Score threshold", minimum=0, maximum=1, step=0.05, value=0.5)
+            run_button = gr.Button("Run")
         with gr.Column():
             with gr.Tabs():
-                with gr.Tab(label='Output'):
-                    result = gr.Label(label='Output', show_label=False)
-                with gr.Tab(label='JSON'):
-                    result_json = gr.JSON(label='JSON output',
-                                          show_label=False)
-                with gr.Tab(label='Text'):
-                    result_text = gr.Text(label='Text output',
-                                          show_label=False,
-                                          lines=5)
-    gr.Examples(examples=examples,
-                inputs=[image, score_threshold],
-                outputs=[result, result_json, result_text],
-                fn=predict,
-                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
+                with gr.Tab(label="Output"):
+                    result = gr.Label(label="Output", show_label=False)
+                with gr.Tab(label="JSON"):
+                    result_json = gr.JSON(label="JSON output", show_label=False)
+                with gr.Tab(label="Text"):
+                    result_text = gr.Text(label="Text output", show_label=False, lines=5)
+    gr.Examples(
+        examples=examples,
+        inputs=[image, score_threshold],
+        outputs=[result, result_json, result_text],
+        fn=predict,
+        cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
+    )
 
     run_button.click(
         fn=predict,
         inputs=[image, score_threshold],
         outputs=[result, result_json, result_text],
-        api_name='predict',
+        api_name="predict",
     )
-demo.queue().launch()
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()

requirements.txt

@@ -1,4 +1,3 @@
 git+https://github.com/KichangKim/DeepDanbooru@v3-20200915-sgd-e30#egg=deepdanbooru
-pillow==10.0.0
-pydantic==1.10.11
+pillow==10.2.0
 tensorflow==2.13.0

style.css

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