app.py

import os
import random
os.system("pip install gradio==2.9b23")
import gradio as gr
from PIL import Image
import torch
from subprocess import call

# Install necessary packages
os.system("pip install gradio==2.9b23")
os.system("pip install basicsr")
os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .")
os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P .")

torch.hub.download_url_to_file('http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution_files/100075_lowres.jpg', 'bear.jpg')

def run_cmd(command):
    try:
        print(command)
        call(command, shell=True)
    except KeyboardInterrupt:
        print("Process interrupted")
        sys.exit(1)

def inference(img, mode):
    _id = random.randint(1, 10000)
    INPUT_DIR = "/tmp/input_image" + str(_id) + "/"
    OUTPUT_DIR = "/tmp/output_image" + str(_id) + "/"
    run_cmd("rm -rf " + INPUT_DIR)
    run_cmd("rm -rf " + OUTPUT_DIR)
    run_cmd("mkdir " + INPUT_DIR)
    run_cmd("mkdir " + OUTPUT_DIR)
    
    basewidth = 256
    wpercent = (basewidth / float(img.size[0]))
    hsize = int((float(img.size[1]) * float(wpercent)))
    img = img.resize((basewidth, hsize), Image.LANCZOS)
    img.save(INPUT_DIR + "1.jpg", "JPEG")
    
    if mode == "base":
        run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus -i " + INPUT_DIR + " -o " + OUTPUT_DIR)
    else:
        run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i " + INPUT_DIR + " -o " + OUTPUT_DIR)
    
    return os.path.join(OUTPUT_DIR, "1_out.jpg")

def main():
    with gr.Blocks() as demo:
        gr.Markdown("# Real-ESRGAN")
        gr.Markdown(
            "Gradio demo for Real-ESRGAN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once."
            "\n\n"
            "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/xinntao/Real-ESRGAN'>Github Repo</a></p>"
        )
        
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(type="pil", label="Input")
                model_type = gr.Radio(["base", "anime"], type="value", default="base", label="Model type")
                examples = gr.Examples(examples=[['bear.jpg', 'base'], ['anime.png', 'anime']], inputs=[input_image, model_type])
                submit_btn = gr.Button("Submit")
            
            with gr.Column():
                output_image = gr.Image(type="file", label="Output")

        submit_btn.click(fn=inference, inputs=[input_image, model_type], outputs=output_image)
    
    demo.launch()