#!/usr/bin/env python

from __future__ import annotations

import argparse
import functools
import os
import pathlib
import subprocess
import sys

# 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

sys.path.insert(0, 'bizarre-pose-estimator')

from _util.twodee_v0 import I as ImageWrapper

TOKEN = os.environ['TOKEN']

MODEL_REPO = 'hysts/bizarre-pose-estimator-models'
MODEL_PATH = 'tagger.pth'
LABEL_PATH = 'tags.txt'


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=0.5)
    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


@torch.inference_mode()
def predict(image: PIL.Image.Image, score_threshold: float,
            device: torch.device, model: torch.nn.Module,
            labels: list[str]) -> dict[str, float]:
    data = ImageWrapper(image).resize_square(256).alpha_bg(
        c='w').convert('RGB').tensor()
    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_model(device: torch.device) -> torch.nn.Module:
    model_path = huggingface_hub.hf_hub_download(MODEL_REPO,
                                                 MODEL_PATH,
                                                 use_auth_token=TOKEN)
    state_dict = torch.load(model_path)

    model = torchvision.models.resnet50(num_classes=1062)
    model.load_state_dict(state_dict)
    model.to(device)
    model.eval()

    return model


def load_labels() -> list[str]:
    label_path = huggingface_hub.hf_hub_download(MODEL_REPO,
                                                 LABEL_PATH,
                                                 use_auth_token=TOKEN)
    with open(label_path) as f:
        labels = [line.strip() for line in f.readlines()]
    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]

    model = load_model(device)
    labels = load_labels()

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

    repo_url = 'https://github.com/ShuhongChen/bizarre-pose-estimator'
    title = 'ShuhongChen/bizarre-pose-estimator (tagger)'
    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()