#!/usr/bin/env python

from __future__ import annotations

import argparse
import functools
import json
import os
import pathlib
import subprocess
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())

import gradio as gr
import huggingface_hub
import PIL.Image
import torch
import torchvision.transforms as T

TOKEN = os.environ['TOKEN']


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', type=str, default='cpu')
    parser.add_argument('--score-slider-step', type=float, default=0.05)
    parser.add_argument('--score-threshold', type=float, default=0.4)
    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


@torch.inference_mode()
def predict(image: PIL.Image.Image, score_threshold: float,
            transform: Callable, device: torch.device, model: torch.nn.Module,
            labels: list[str]) -> dict[str, float]:
    data = transform(image)
    data = data.to(device).unsqueeze(0)
    preds = model(data)[0]
    preds = torch.sigmoid(preds)
    preds = preds.cpu().numpy().astype(float)

    res = dict()
    for prob, label in zip(preds, labels):
        if prob < score_threshold:
            continue
        res[label] = prob
    return res


def load_labels() -> list[str]:
    label_path = pathlib.Path('class_names_6000.json')
    label_url = 'https://raw.githubusercontent.com/RF5/danbooru-pretrained/master/config/class_names_6000.json'
    if not label_path.exists():
        torch.hub.download_url_to_file(label_url, label_path.as_posix())
    with open(label_path) as f:
        labels = json.load(f)
    return labels


def main():
    gr.close_all()

    args = parse_args()
    device = torch.device(args.device)

    image_paths = download_sample_images()
    examples = [[path.as_posix(), args.score_threshold]
                for path in image_paths]

    if device.type == 'cpu':
        model_path = pathlib.Path('resnet50-13306192.pth')
        model_url = 'https://github.com/RF5/danbooru-pretrained/releases/download/v0.1/resnet50-13306192.pth'
        if not model_path.exists():
            torch.hub.download_url_to_file(model_url, model_path.as_posix())
        model = torch.hub.load('RF5/danbooru-pretrained',
                               'resnet50',
                               pretrained=False)
        state_dict = torch.load(model_path, map_location=device)
        model.load_state_dict(state_dict)
    else:
        model = torch.hub.load('RF5/danbooru-pretrained', 'resnet50')
    model.to(device)
    model.eval()

    transform = T.Compose([
        T.Resize(360),
        T.ToTensor(),
        T.Normalize(mean=[0.7137, 0.6628, 0.6519],
                    std=[0.2970, 0.3017, 0.2979]),
    ])

    labels = load_labels()

    func = functools.partial(predict,
                             transform=transform,
                             device=device,
                             model=model,
                             labels=labels)
    func = functools.update_wrapper(func, predict)

    repo_url = 'https://github.com/RF5/danbooru-pretrained'
    title = 'RF5/danbooru-pretrained'
    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()