app.py

import gradio as gr
import os

# Cloning and setting up the model
os.system("git clone https://github.com/megvii-research/NAFNet")
os.system("mv NAFNet/* ./")
os.system("mv *.pth experiments/pretrained_models/")
os.system("python3 setup.py develop --no_cuda_ext --user")

# Inference function
def inference(image, task):
    if not os.path.exists('tmp'):
        os.makedirs('tmp')
    image.save("tmp/lq_image.png", "PNG")
    
    if task == 'Denoising':
        os.system("python basicsr/demo.py -opt options/test/SIDD/NAFNet-width64.yml --input_path ./tmp/lq_image.png --output_path ./tmp/image.png")
    elif task == 'Deblurring':
        os.system("python basicsr/demo.py -opt options/test/REDS/NAFNet-width64.yml --input_path ./tmp/lq_image.png --output_path ./tmp/image.png")
    
    return "tmp/image.png"

# Title and description
title = "NAFNet"
description = """Gradio demo for <b>NAFNet: Nonlinear Activation Free Network for Image Restoration</b>.
NAFNet achieves state-of-the-art performance on three tasks: image denoising, image deblurring, and stereo image super-resolution (SR).
See the paper and project page for detailed results below. Here, we provide a demo for image denoise and deblur.
To use it, simply upload your image, or click one of the examples to load them. Inference needs some time since this demo uses CPU."""

article = """<p style='text-align: center'>
<a href='https://arxiv.org/abs/2204.04676' target='_blank'>Simple Baselines for Image Restoration</a> |
<a href='https://arxiv.org/abs/2204.08714' target='_blank'>NAFSSR: Stereo Image Super-Resolution Using NAFNet</a> |
<a href='https://github.com/megvii-research/NAFNet' target='_blank'>Github Repo</a>
</p>"""

examples = [['demo/noisy.png', 'Denoising'], ['demo/blurry.jpg', 'Deblurring']]

# Updated Gradio Interface
iface = gr.Interface(
    fn=inference,  # Updated function syntax
    inputs=[
        gr.Image(type="pil", label="Input Image"),  # Replaced gr.inputs.Image
        gr.Radio(["Denoising", "Deblurring"], value="Denoising", label="Task")  # Replaced gr.inputs.Radio
    ],
    outputs=gr.Image(type="file", label="Output Image"),  # Replaced gr.outputs.Image
    title=title,
    description=description,
    article=article,
    examples=examples
)

# Launch interface
iface.launch(debug=True)