Update

.pre-commit-config.yaml

@@ -0,0 +1,60 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    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: v1.10.0
+    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: 24.4.2
+    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: 1.8.5
+    hooks:
+      - id: nbqa-black
+      - id: nbqa-pyupgrade
+        args: ["--py37-plus"]
+      - id: nbqa-isort
+        args: ["--float-to-top"]

README.md

@@ -4,7 +4,7 @@ emoji: 🌖
 colorFrom: red
 colorTo: yellow
 sdk: gradio
-sdk_version: 3.0.5
+sdk_version: 4.36.1
 app_file: app.py
 pinned: false
 ---

app.py

@@ -2,10 +2,7 @@
 
 from __future__ import annotations
 
-import argparse
-import functools
 import io
-import os
 import pathlib
 import tarfile
 
@@ -17,8 +14,8 @@ import PIL.Image
 import tensorflow as tf
 from huggingface_hub import hf_hub_download
 
-TITLE = 'TADNE Image Search with DeepDanbooru'
-DESCRIPTION = '''The original TADNE site is https://thisanimedoesnotexist.ai/.
+TITLE = "TADNE Image Search with DeepDanbooru"
+DESCRIPTION = """The original TADNE site is https://thisanimedoesnotexist.ai/.
 
 This app shows images similar to the query image from images generated
 by the TADNE model with seed 0-99999.
@@ -38,59 +35,30 @@ Related Apps:
 - [TADNE Image Selector](https://huggingface.co/spaces/hysts/TADNE-image-selector)
 - [TADNE Interpolation](https://huggingface.co/spaces/hysts/TADNE-interpolation)
 - [DeepDanbooru](https://huggingface.co/spaces/hysts/DeepDanbooru)
-'''
-ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.tadne-image-search-with-deepdanbooru" alt="visitor badge"/></center>'
-
-TOKEN = os.environ['TOKEN']
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    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()
-
-
-def download_image_tarball(size: int, dirname: str) -> pathlib.Path:
-    path = hf_hub_download('hysts/TADNE-sample-images',
-                           f'{size}/{dirname}.tar',
-                           repo_type='dataset',
-                           use_auth_token=TOKEN)
-    return path
+"""
 
 
 def load_deepdanbooru_predictions(dirname: str) -> np.ndarray:
     path = hf_hub_download(
-        'hysts/TADNE-sample-images',
-        f'prediction_results/deepdanbooru/intermediate_features/{dirname}.npy',
-        repo_type='dataset',
-        use_auth_token=TOKEN)
+        "hysts/TADNE-sample-images",
+        f"prediction_results/deepdanbooru/intermediate_features/{dirname}.npy",
+        repo_type="dataset",
+    )
     return np.load(path)
 
 
 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',
-                                               use_auth_token=TOKEN)
+        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 create_model() -> tf.keras.Model:
-    path = huggingface_hub.hf_hub_download('hysts/DeepDanbooru',
-                                           'model-resnet_custom_v3.h5',
-                                           use_auth_token=TOKEN)
+    path = huggingface_hub.hf_hub_download("public-data/DeepDanbooru", "model-resnet_custom_v3.h5")
     model = tf.keras.models.load_model(path)
     model = tf.keras.Model(model.input, model.layers[-4].output)
     layer = tf.keras.layers.GlobalAveragePooling2D()
@@ -98,16 +66,21 @@ def create_model() -> tf.keras.Model:
     return model
 
 
-def predict(image: PIL.Image.Image, model: tf.keras.Model) -> np.ndarray:
+image_size = 128
+dirname = "0-99999"
+tarball_path = hf_hub_download("hysts/TADNE-sample-images", f"{image_size}/{dirname}.tar", repo_type="dataset")
+deepdanbooru_predictions = load_deepdanbooru_predictions(dirname)
+
+model = create_model()
+
+
+def predict(image: PIL.Image.Image) -> np.ndarray:
     _, 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
     features = model.predict(image[None, ...])[0]
     features = features.astype(float)
     return features
@@ -117,14 +90,9 @@ def run(
     image: PIL.Image.Image,
     nrows: int,
     ncols: int,
-    image_size: int,
-    dirname: str,
-    tarball_path: pathlib.Path,
-    deepdanbooru_predictions: np.ndarray,
-    model: tf.keras.Model,
 ) -> tuple[np.ndarray, np.ndarray]:
-    features = predict(image, model)
-    distances = ((deepdanbooru_predictions - features)**2).sum(axis=1)
+    features = predict(image)
+    distances = ((deepdanbooru_predictions - features) ** 2).sum(axis=1)
 
     image_indices = np.argsort(distances)
 
@@ -134,69 +102,42 @@ def run(
         for index in range(nrows * ncols):
             image_index = image_indices[index]
             seeds.append(image_index)
-            member = tar_file.getmember(f'{dirname}/{image_index:07d}.jpg')
-            with tar_file.extractfile(member) as f:
+            member = tar_file.getmember(f"{dirname}/{image_index:07d}.jpg")
+            with tar_file.extractfile(member) as f:  # type: ignore
                 data = io.BytesIO(f.read())
             image = PIL.Image.open(data)
             image = np.asarray(image)
             images.append(image)
-    res = np.asarray(images).reshape(nrows, ncols, image_size, image_size,
-                                     3).transpose(0, 2, 1, 3, 4).reshape(
-                                         nrows * image_size,
-                                         ncols * image_size, 3)
+    res = (
+        np.asarray(images)
+        .reshape(nrows, ncols, image_size, image_size, 3)
+        .transpose(0, 2, 1, 3, 4)
+        .reshape(nrows * image_size, ncols * image_size, 3)
+    )
     seeds = np.asarray(seeds).reshape(nrows, ncols)
 
     return res, seeds
 
 
-def main():
-    args = parse_args()
-
-    image_size = 128
-    dirname = '0-99999'
-    tarball_path = download_image_tarball(image_size, dirname)
-    deepdanbooru_predictions = load_deepdanbooru_predictions(dirname)
-
-    model = create_model()
-
-    image_paths = load_sample_image_paths()
-    examples = [[path.as_posix(), 2, 5] for path in image_paths]
-
-    func = functools.partial(
-        run,
-        image_size=image_size,
-        dirname=dirname,
-        tarball_path=tarball_path,
-        deepdanbooru_predictions=deepdanbooru_predictions,
-        model=model,
-    )
-    func = functools.update_wrapper(func, run)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='pil', label='Input'),
-            gr.inputs.Slider(1, 10, step=1, default=2, label='Number of Rows'),
-            gr.inputs.Slider(
-                1, 10, step=1, default=5, label='Number of Columns'),
-        ],
-        [
-            gr.outputs.Image(type='numpy', label='Output'),
-            gr.outputs.Dataframe(type='numpy', label='Seed'),
-        ],
-        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()
+image_paths = load_sample_image_paths()
+examples = [[path.as_posix(), 2, 5] for path in image_paths]
+
+demo = gr.Interface(
+    fn=run,
+    inputs=[
+        gr.Image(label="Input", type="pil"),
+        gr.Slider(label="Number of Rows", minimum=1, maximum=10, step=1, value=2),
+        gr.Slider(label="Number of Columns", minimum=1, maximum=10, step=1, value=2),
+    ],
+    outputs=[
+        gr.Image(label="Output"),
+        gr.Dataframe(label="Seed"),
+    ],
+    examples=examples,
+    title=TITLE,
+    description=DESCRIPTION,
+)
+
+
+if __name__ == "__main__":
+    demo.queue().launch()

requirements.txt

@@ -1,3 +1,3 @@
-pillow==9.1.0
-tensorflow==2.8.0
 git+https://github.com/KichangKim/DeepDanbooru@v3-20200915-sgd-e30#egg=deepdanbooru
+pillow==10.3.0
+tensorflow==2.8.0