#!/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 import piexif 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 plaintext_to_html(text): text = "

" + "
\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "

" return text def predict(image: PIL.Image.Image, score_threshold: float, model: tf.keras.Model, labels: list[str]) -> dict[str, float]: rawimage = image _, 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 b = dict(sorted(res.items(),key=lambda item:item[1], reverse=True)) a = ', '.join(list(b.keys())).replace('_',' ').replace('(','\(').replace(')','\)') items = rawimage.info geninfo = '' if "exif" in rawimage.info: exif = piexif.load(rawimage.info["exif"]) exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'') try: exif_comment = piexif.helper.UserComment.load(exif_comment) except ValueError: exif_comment = exif_comment.decode('utf8', errors="ignore") items['exif comment'] = exif_comment geninfo = exif_comment for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif', 'loop', 'background', 'timestamp', 'duration']: items.pop(field, None) geninfo = items.get('parameters', geninfo) info = '' for key, text in items.items(): info += f"""

{plaintext_to_html(str(key))}

{plaintext_to_html(str(text))}

""".strip()+"\n" if len(info) == 0: message = "" info = f"

{message}

" return (a,res,geninfo,info) def main(): args = parse_args() 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 String'), gr.outputs.Label(label='Output Labels'), gr.outputs.HTML(), gr.outputs.HTML() ], examples=[ ['miku.jpg',0.5], ['miku2.jpg',0.5] ], 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()