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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()