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.gitattributes

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 *.model filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+images

README.md

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 ---
 title: DeepDanbooru String
-emoji: 📊
-colorFrom: pink
-colorTo: indigo
+emoji: 💬
+colorFrom: blue
+colorTo: red
 sdk: gradio
-sdk_version: 3.5
+sdk_version: 3.0.5
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Configuration
+
+`title`: _string_
+Display title for the Space
+
+`emoji`: _string_
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_
+Can be either `gradio`, `streamlit`, or `static`
+
+`sdk_version` : _string_
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_
+Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_
+Whether the Space stays on top of your list.

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import pathlib
+import tarfile
+
+import deepdanbooru as dd
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import PIL.Image
+import tensorflow as tf
+
+TITLE = 'NoCrypt/DeepDanbooru_string'
+DESCRIPTION = 'Cloned from: https://huggingface.co/spaces/hysts/DeepDanbooru'
+
+TOKEN = os.environ['TOKEN']
+MODEL_REPO = 'NoCrypt/DeepDanbooru_string'
+MODEL_FILENAME = 'model-resnet_custom_v3.h5'
+LABEL_FILENAME = 'tags.txt'
+
+
+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')
+    return parser.parse_args()
+
+
+def load_sample_image_paths() -> list[pathlib.Path]:
+    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)
+        with tarfile.open(path) as f:
+            f.extractall()
+    return sorted(image_dir.glob('*'))
+
+
+def load_model() -> tf.keras.Model:
+    path = huggingface_hub.hf_hub_download(MODEL_REPO,
+                                           MODEL_FILENAME,
+                                           use_auth_token=TOKEN)
+    model = tf.keras.models.load_model(path)
+    return model
+
+
+def load_labels() -> list[str]:
+    path = huggingface_hub.hf_hub_download(MODEL_REPO,
+                                           LABEL_FILENAME,
+                                           use_auth_token=TOKEN)
+    with open(path) as f:
+        labels = [line.strip() for line in f.readlines()]
+    return labels
+
+
+def predict(image: PIL.Image.Image, score_threshold: float,
+            model: tf.keras.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.tolist(), labels):
+        if prob < score_threshold:
+            continue
+        res[label] = prob
+    return res
+
+
+def main():
+    args = parse_args()
+
+    image_paths = load_sample_image_paths()
+    examples = [[path.as_posix(), args.score_threshold]
+                for path in image_paths]
+
+    model = load_model()
+    labels = load_labels()
+
+    func = functools.partial(predict, model=model, labels=labels)
+    func = functools.update_wrapper(func, predict)
+
+    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.Textbox(label='Output'),
+        examples=examples,
+        title=TITLE,
+        description=DESCRIPTION,
+        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()

requirements.txt

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+pillow>=9.0.0
+tensorflow>=2.7.0
+git+https://github.com/KichangKim/DeepDanbooru@v3-20200915-sgd-e30#egg=deepdanbooru