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: indigo
 colorTo: pink
 sdk: gradio
-sdk_version: 3.0.5
+sdk_version: 4.36.1
 app_file: app.py
 pinned: false
 ---

app.py

@@ -2,11 +2,7 @@
 
 from __future__ import annotations
 
-import argparse
-import functools
 import io
-import os
-import pathlib
 import tarfile
 
 import gradio as gr
@@ -14,8 +10,8 @@ import numpy as np
 import PIL.Image
 from huggingface_hub import hf_hub_download
 
-TITLE = 'TADNE (This Anime Does Not Exist) Image Selector'
-DESCRIPTION = '''The original TADNE site is https://thisanimedoesnotexist.ai/.
+TITLE = "TADNE (This Anime Does Not Exist) Image Selector"
+DESCRIPTION = """The original TADNE site is https://thisanimedoesnotexist.ai/.
 
 You can view images generated by the TADNE model with seed 0-99999.
 You can filter images based on predictions by the [DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) model.
@@ -32,50 +28,35 @@ Related Apps:
 - [TADNE Interpolation](https://huggingface.co/spaces/hysts/TADNE-interpolation)
 - [TADNE Image Search with DeepDanbooru](https://huggingface.co/spaces/hysts/TADNE-image-search-with-DeepDanbooru)
 - [DeepDanbooru](https://huggingface.co/spaces/hysts/DeepDanbooru)
-'''
-ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.tadne-image-selector" 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_tag_dict() -> dict[str, int]:
-    path = hf_hub_download('hysts/DeepDanbooru',
-                           'tags.txt',
-                           use_auth_token=TOKEN)
+    path = hf_hub_download("public-data/DeepDanbooru", "tags.txt")
     with open(path) as f:
         tags = [line.strip() for line in f.readlines()]
     return {tag: i for i, tag in enumerate(tags)}
 
 
 def load_deepdanbooru_predictions(dirname: str) -> np.ndarray:
-    path = hf_hub_download('hysts/TADNE-sample-images',
-                           f'prediction_results/deepdanbooru/{dirname}.npy',
-                           repo_type='dataset',
-                           use_auth_token=TOKEN)
+    path = hf_hub_download(
+        "hysts/TADNE-sample-images",
+        f"prediction_results/deepdanbooru/{dirname}.npy",
+        repo_type="dataset",
+    )
     return np.load(path)
 
 
+image_size = 128
+min_seed = 0
+max_seed = 99999
+dirname = "0-99999"
+tarball_path = hf_hub_download("hysts/TADNE-sample-images", f"{image_size}/{dirname}.tar", repo_type="dataset")
+
+deepdanbooru_tag_dict = load_deepdanbooru_tag_dict()
+deepdanbooru_predictions = load_deepdanbooru_predictions(dirname)
+
+
 def run(
     general_tags: list[str],
     hair_color_tags: list[str],
@@ -83,31 +64,28 @@ def run(
     eye_color_tags: list[str],
     image_color_tags: list[str],
     other_tags: list[str],
-    additional_tags: str,
+    additional_tags_str: str,
     score_threshold: float,
     start_index: int,
     nrows: int,
     ncols: int,
-    image_size: int,
-    min_seed: int,
-    max_seed: int,
-    dirname: str,
-    tarball_path: pathlib.Path,
-    deepdanbooru_tag_dict: dict[str, int],
-    deepdanbooru_predictions: np.ndarray,
-) -> tuple[int, np.ndarray, np.ndarray]:
-    hair_color_tags = [f'{color}_hair' for color in hair_color_tags]
-    eye_color_tags = [f'{color}_eyes' for color in eye_color_tags]
-    additional_tags = additional_tags.split(',')
-
-    tags = general_tags + hair_color_tags + hair_style_tags + \
-        eye_color_tags + image_color_tags + other_tags + additional_tags
+) -> tuple[int, np.ndarray, np.ndarray, str]:
+    hair_color_tags = [f"{color}_hair" for color in hair_color_tags]
+    eye_color_tags = [f"{color}_eyes" for color in eye_color_tags]
+    additional_tags = additional_tags_str.split(",")
+
+    tags = (
+        general_tags
+        + hair_color_tags
+        + hair_style_tags
+        + eye_color_tags
+        + image_color_tags
+        + other_tags
+        + additional_tags
+    )
     missing_tags = [tag for tag in tags if tag not in deepdanbooru_tag_dict]
 
-    tag_indices = [
-        deepdanbooru_tag_dict[tag] for tag in tags
-        if tag in deepdanbooru_tag_dict
-    ]
+    tag_indices = [deepdanbooru_tag_dict[tag] for tag in tags if tag in deepdanbooru_tag_dict]
 
     conditions = deepdanbooru_predictions[:, tag_indices] > score_threshold
     image_indices = np.arange(len(deepdanbooru_predictions))
@@ -126,141 +104,118 @@ def run(
                 continue
             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 len(image_indices), res, seeds, ','.join(missing_tags)
-
-
-def main():
-    args = parse_args()
-
-    image_size = 128
-    min_seed = 0
-    max_seed = 99999
-    dirname = '0-99999'
-    tarball_path = download_image_tarball(image_size, dirname)
+    return len(image_indices), res, seeds, ",".join(missing_tags)
 
-    deepdanbooru_tag_dict = load_deepdanbooru_tag_dict()
-    deepdanbooru_predictions = load_deepdanbooru_predictions(dirname)
 
-    func = functools.partial(
-        run,
-        image_size=image_size,
-        min_seed=min_seed,
-        max_seed=max_seed,
-        dirname=dirname,
-        tarball_path=tarball_path,
-        deepdanbooru_tag_dict=deepdanbooru_tag_dict,
-        deepdanbooru_predictions=deepdanbooru_predictions,
-    )
-    func = functools.update_wrapper(func, run)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.CheckboxGroup([
-                '1girl',
-                '1boy',
-                'multiple_girls',
-                'multiple_boys',
-                'looking_at_viewer',
+demo = gr.Interface(
+    fn=run,
+    inputs=[
+        gr.CheckboxGroup(
+            label="General",
+            choices=[
+                "1girl",
+                "1boy",
+                "multiple_girls",
+                "multiple_boys",
+                "looking_at_viewer",
             ],
-                                    label='General'),
-            gr.inputs.CheckboxGroup([
-                'aqua',
-                'black',
-                'blonde',
-                'blue',
-                'brown',
-                'green',
-                'grey',
-                'orange',
-                'pink',
-                'purple',
-                'red',
-                'silver',
-                'white',
+        ),
+        gr.CheckboxGroup(
+            label="Hair Color",
+            choices=[
+                "aqua",
+                "black",
+                "blonde",
+                "blue",
+                "brown",
+                "green",
+                "grey",
+                "orange",
+                "pink",
+                "purple",
+                "red",
+                "silver",
+                "white",
             ],
-                                    label='Hair Color'),
-            gr.inputs.CheckboxGroup([
-                'bangs',
-                'curly_hair',
-                'long_hair',
-                'medium_hair',
-                'messy_hair',
-                'ponytail',
-                'short_hair',
-                'straight_hair',
-                'twintails',
+        ),
+        gr.CheckboxGroup(
+            label="Hair Style",
+            choices=[
+                "bangs",
+                "curly_hair",
+                "long_hair",
+                "medium_hair",
+                "messy_hair",
+                "ponytail",
+                "short_hair",
+                "straight_hair",
+                "twintails",
             ],
-                                    label='Hair Style'),
-            gr.inputs.CheckboxGroup([
-                'aqua',
-                'black',
-                'blue',
-                'brown',
-                'green',
-                'grey',
-                'orange',
-                'pink',
-                'purple',
-                'red',
-                'white',
-                'yellow',
+        ),
+        gr.CheckboxGroup(
+            label="Eye Color",
+            choices=[
+                "aqua",
+                "black",
+                "blue",
+                "brown",
+                "green",
+                "grey",
+                "orange",
+                "pink",
+                "purple",
+                "red",
+                "white",
+                "yellow",
             ],
-                                    label='Eye Color'),
-            gr.inputs.CheckboxGroup([
-                'greyscale',
-                'monochrome',
+        ),
+        gr.CheckboxGroup(
+            label="Image Color",
+            choices=[
+                "greyscale",
+                "monochrome",
             ],
-                                    label='Image Color'),
-            gr.inputs.CheckboxGroup([
-                'animal_ears',
-                'closed_eyes',
-                'full_body',
-                'hat',
-                'smile',
+        ),
+        gr.CheckboxGroup(
+            label="Others",
+            choices=[
+                "animal_ears",
+                "closed_eyes",
+                "full_body",
+                "hat",
+                "smile",
             ],
-                                    label='Others'),
-            gr.inputs.Textbox(label='Additional Tags'),
-            gr.inputs.Slider(0,
-                             1,
-                             step=0.1,
-                             default=0.5,
-                             label='DeepDanbooru Score Threshold'),
-            gr.inputs.Number(default=0, label='Start Index'),
-            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.Textbox(type='number', label='Number of Found Images'),
-            gr.outputs.Image(type='numpy', label='Output'),
-            gr.outputs.Dataframe(type='numpy', label='Seed'),
-            gr.outputs.Textbox(type='str', label='Missing Tags'),
-        ],
-        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()
+        ),
+        gr.Textbox(label="Additional Tags"),
+        gr.Slider(label="DeepDanbooru Score Threshold", minimum=0, maximum=1, step=0.1, value=0.5),
+        gr.Number(label="Start Index", value=0),
+        gr.Slider(label="Number of Rows", minimum=0, maximum=10, step=1, value=2),
+        gr.Slider(label="Number of Columns", minimum=0, maximum=10, step=1, value=5),
+    ],
+    outputs=[
+        gr.Textbox(label="Number of Found Images"),
+        gr.Image(label="Output"),
+        gr.Dataframe(label="Seed"),
+        gr.Textbox(label="Missing Tags"),
+    ],
+    title=TITLE,
+    description=DESCRIPTION,
+)
+
+
+if __name__ == "__main__":
+    demo.queue().launch()

requirements.txt

@@ -1,2 +1,2 @@
-numpy==1.22.3
-Pillow==9.0.1
+numpy==1.26.4
+Pillow==10.3.0