app.py

import os
import gradio as gr
import util
from run_cmd import run_cmd
from random import randint
from PIL import Image
import tempfile

temp_path = tempfile.gettempdir()

is_colab = util.is_google_colab()

run_cmd("pip install pngquant")

def inference(img, size, type):
    _id = randint(1, 10000)
    INPUT_DIR = os.path.join(temp_path, f"input_image{str(_id)}")
    OUTPUT_DIR = os.path.join(temp_path, f"output_image{str(_id)}")
    img_in_path = os.path.join(INPUT_DIR, "1.jpg")
    img_out_path = os.path.join(OUTPUT_DIR, f"1_{size}.png")
    run_cmd(f"rm -rf {INPUT_DIR}")
    run_cmd(f"rm -rf {OUTPUT_DIR}")
    run_cmd(f"mkdir {INPUT_DIR}")
    run_cmd(f"mkdir {OUTPUT_DIR}")
    img.save(img_in_path, "PNG")

    if type == "Manga":
        run_cmd(f"python inference_manga_v2.py {img_in_path} {img_out_path}")
    else:
        run_cmd(f"python inference.py {img_in_path} {img_out_path} {type}")

    img_out = Image.open(img_out_path)

    if size == "x2":
        img_out = img_out.resize((img_out.width // 2, img_out.height // 2), resample=Image.BICUBIC)

    #img_out.save(img_out_path, optimize=True) # Add more optimizations
    #img_out = Image.open(img_out_path)

    # Remove input and output image
    run_cmd(f"rm -f {img_in_path}")
    #run_cmd(f"rm -f {img_out_path}")

    out_file.update(value=img_out_path, visible=True)

    return img_out, gr.File.update(value=img_out_path, visible=True)

css = '''
    .file-preview {
        overflow: hidden !important;
        margin: 5px 0 !important;
        padding: 0 10px !important;
    }

    .file-preview div div:nth-child(2) {
        flex-grow: 1 !important;
    }

    .file-preview div div:nth-child(3) {
        text-align: right !important;
    }
'''

title = "ESRGAN Upscaling With Custom Models"
description = "This space uses old ESRGAN architecture to upscale images, using models made by the community."
article = "<p><a href='https://upscale.wiki/wiki/Model_Database'>Model Database</a></p>"

with gr.Blocks(title=title, css=css) as demo:
    gr.Markdown(
    f"""
    # {title}
    {description}
    """)

    with gr.Box():
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(type="pil", label="Input")
                upscale_size = gr.Radio(["x4", "x2"], label="Upscale by:", value="x4")
                upscale_type = gr.Radio(["Manga", "Anime", "General"], label="Select the type of picture you want to upscale:", value="Manga")

                with gr.Row():
                    upscale_btn = gr.Button(value="Upscale", variant="primary")

            with gr.Column():
                output_image = gr.Image(type="filepath", interactive=False, label="Upscaled image", )
                
                with gr.Row():
                    out_file = gr.File(interactive=False, show_label=False, visible=False)

    gr.HTML(value=article)
        
    upscale_btn.click(inference, inputs=[input_image, upscale_size, upscale_type], outputs=[output_image, out_file])

demo.queue()
demo.launch(debug=is_colab, share=is_colab, inline=is_colab)