#!/usr/bin/env python

from __future__ import annotations

import argparse
import functools
import json
import os
import pathlib
import tarfile
from typing import Callable

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

TOKEN = os.environ['TOKEN']

MODEL_REPO = 'hysts/danbooru-pretrained'
MODEL_FILENAME = 'resnet50-13306192.pth'
LABEL_FILENAME = 'class_names_6000.json'


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 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(device: torch.device) -> torch.nn.Module:
    path = huggingface_hub.hf_hub_download(MODEL_REPO,
                                           MODEL_FILENAME,
                                           use_auth_token=TOKEN)
    state_dict = torch.load(path)
    model = torch.hub.load('RF5/danbooru-pretrained',
                           'resnet50',
                           pretrained=False)
    model.load_state_dict(state_dict)
    model.to(device)
    model.eval()
    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 = json.load(f)
    return labels


@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.tolist(), labels):
        if prob < score_threshold:
            continue
        res[label] = prob
    return res


def main():
    gr.close_all()

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

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

    model = load_model(device)
    labels = load_labels()

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

    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()