#!/usr/bin/env python

from __future__ import annotations

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

DESCRIPTION = '# [RF5/danbooru-pretrained](https://github.com/RF5/danbooru-pretrained)'

MODEL_REPO = 'public-data/danbooru-pretrained'


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')
        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, 'resnet50-13306192.pth')
    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, 'class_names_6000.json')
    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


image_paths = load_sample_image_paths()
examples = [[path.as_posix(), 0.4] for path in image_paths]

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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]),
])

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

with gr.Blocks(css='style.css') as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Row():
        with gr.Column():
            image = gr.Image(label='Input', type='pil')
            threshold = gr.Slider(label='Score Threshold',
                                  minimum=0,
                                  maximum=1,
                                  step=0.05,
                                  value=0.4)
            run_button = gr.Button('Run')
        with gr.Column():
            result = gr.Label(label='Output')

    inputs = [image, threshold]
    gr.Examples(examples=examples,
                inputs=inputs,
                outputs=result,
                fn=fn,
                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
    run_button.click(fn=fn, inputs=inputs, outputs=result, api_name='predict')
demo.queue(max_size=15).launch()