#!/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()