app.py

import os
import gradio as gr
from PIL import Image


os.system(
    'wget https://github.com/FanChiMao/SUNet/releases/download/0.0/AWGN_denoising_SUNet.pth -P experiments/pretrained_models')
##

def inference(img):
    os.system('mkdir test')
    #basewidth = 512
    #wpercent = (basewidth / float(img.size[0]))
    #hsize = int((float(img.size[1]) * float(wpercent)))
    #img = img.resize((basewidth, hsize), Image.ANTIALIAS)
    img.save("test/1.png", "PNG")
    os.system(
        'python main_test_SUNet.py --input_dir test --weights experiments/pretrained_models/AWGN_denoising_SUNet.pth')
    return 'result/1.png'


title = "SUNet: Swin Transformer with UNet for Image Denoising"
description = "Gradio demo for SUNet. SUNet has competitive performance results in terms of quantitative metrics and visual quality. See the paper and project page for detailed results below. Here, we provide a demo for AWGN image denoising. To use it, simply upload your image, or click one of the examples to load them. Reference from: https://huggingface.co/akhaliq"
article = "<p style='text-align: center'><a href='https://' target='_blank'>SUNet: Swin Transformer with UNet for Image Denoising</a> | <a href='https://github.com/FanChiMao/SUNet' target='_blank'>Github Repo</a></p>"

examples = [['set5/baby.png'], ['set5/bird.png'],['set5/butterfly.png'],['set5/head.png'],['set5/woman.png']]
gr.Interface(
    inference,
    [gr.inputs.Image(type="pil", label="Input")],
    gr.outputs.Image(type="file", label="Output"),
    title=title,
    description=description,
    article=article,
    allow_flagging=False,
    allow_screenshot=False,
    examples=examples
).launch(debug=True)