Add files

.gitmodules

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+[submodule "DeepDanbooru"]
+	path = DeepDanbooru
+	url = https://github.com/KichangKim/DeepDanbooru

DeepDanbooru

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+Subproject commit 92ba0b56be5eed0037e3f067bb9867f5ac691647

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import pathlib
+import subprocess
+import sys
+import urllib
+import zipfile
+from typing import Callable
+
+# workaround for https://github.com/gradio-app/gradio/issues/483
+command = 'pip install -U gradio==2.7.0'
+subprocess.call(command.split())
+
+command = 'pip install -r DeepDanbooru/requirements.txt'
+subprocess.call(command.split())
+sys.path.insert(0, 'DeepDanbooru')
+
+import deepdanbooru as dd
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import PIL.Image
+import tensorflow as tf
+
+TOKEN = os.environ['TOKEN']
+
+ZIP_PATH = 'data.zip'
+TAG_PATH = 'tags.txt'
+MODEL_PATH = 'model-resnet_custom_v3.h5'
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--score-slider-step', type=float, default=0.05)
+    parser.add_argument('--score-threshold', type=float, default=0.5)
+    parser.add_argument('--theme', type=str, default='dark-grass')
+    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')
+    parser.add_argument('--allow-screenshot', action='store_true')
+    return parser.parse_args()
+
+
+def download_sample_images() -> list[pathlib.Path]:
+    image_dir = pathlib.Path('samples')
+    image_dir.mkdir(exist_ok=True)
+
+    dataset_repo = 'hysts/sample-images-TADNE'
+    n_images = 36
+    paths = []
+    for index in range(n_images):
+        path = huggingface_hub.hf_hub_download(dataset_repo,
+                                               f'{index:02d}.jpg',
+                                               repo_type='dataset',
+                                               cache_dir=image_dir.as_posix(),
+                                               use_auth_token=TOKEN)
+        paths.append(pathlib.Path(path))
+    return paths
+
+
+def download_model_data() -> None:
+    url = 'https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20200915-sgd-e30/deepdanbooru-v3-20200915-sgd-e30.zip'
+    urllib.request.urlretrieve(url, ZIP_PATH)
+    with zipfile.ZipFile(ZIP_PATH) as f:
+        f.extract(TAG_PATH)
+        f.extract(MODEL_PATH)
+
+
+def predict(image: PIL.Image.Image, score_threshold: float, model,
+            labels: list[str]) -> dict[str, float]:
+    _, 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 = image.numpy()
+    image = dd.image.transform_and_pad_image(image, width, height)
+    image = image / 255.
+    probs = model.predict(image[None, ...])[0]
+    probs = probs.astype(float)
+    res = dict()
+    for prob, label in zip(probs, labels):
+        if prob < score_threshold:
+            continue
+        res[label] = prob
+    return res
+
+
+def main():
+    gr.close_all()
+
+    args = parse_args()
+
+    image_paths = download_sample_images()
+    examples = [[path.as_posix(), args.score_threshold]
+                for path in image_paths]
+
+    zip_path = pathlib.Path(ZIP_PATH)
+    if not zip_path.exists():
+        download_model_data()
+
+    model = tf.keras.models.load_model(MODEL_PATH)
+
+    with open(TAG_PATH) as f:
+        labels = [line.strip() for line in f.readlines()]
+
+    func = functools.partial(predict, model=model, labels=labels)
+    func = functools.update_wrapper(func, predict)
+
+    repo_url = 'https://github.com/KichangKim/DeepDanbooru'
+    title = 'KichangKim/DeepDanbooru'
+    description = f'A demo for {repo_url}'
+    article = None
+
+    gr.Interface(
+        func,
+        [
+            gr.inputs.Image(type='pil', label='Input'),
+            gr.inputs.Slider(0,
+                             1,
+                             step=args.score_slider_step,
+                             default=args.score_threshold,
+                             label='Score Threshold'),
+        ],
+        gr.outputs.Label(label='Output'),
+        theme=args.theme,
+        title=title,
+        description=description,
+        article=article,
+        examples=examples,
+        allow_screenshot=args.allow_screenshot,
+        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()

requirements.txt

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+pillow>=9.0.0
+tensorflow>=2.7.0