#!/usr/bin/env python

from __future__ import annotations

import os
import pathlib
import shlex
import subprocess

import gradio as gr
import torch

from app_colorization import create_demo as create_demo_colorization
from app_superresolution import create_demo as create_demo_superresolution

DESCRIPTION = '# [DDNM-HQ](https://github.com/wyhuai/DDNM/tree/main/hq_demo)'

if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
    DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
if not torch.cuda.is_available():
    DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'

if torch.cuda.is_available():
    MODEL_DIR = pathlib.Path('DDNM/hq_demo/data/pretrained')
    if not MODEL_DIR.exists():
        MODEL_DIR.mkdir()
        subprocess.run(shlex.split(
            'wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_classifier.pt'
        ),
                       cwd=MODEL_DIR.as_posix())
        subprocess.run(shlex.split(
            'wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion.pt'
        ),
                       cwd=MODEL_DIR.as_posix())

with gr.Blocks(css='style.css') as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Tabs():
        with gr.TabItem(label='Super-resolution'):
            create_demo_superresolution()
        with gr.TabItem(label='Colorization'):
            create_demo_colorization()
demo.queue(api_open=False, max_size=5).launch()