app.py

import os
os.system("pip install gradio==2.9b23")
#https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13985
#os.system("python -m pip install torch==2.0.1 torchvision==0.15.2")
import random
import gradio as gr
from PIL import Image
import torch
from random import randint
import sys
from subprocess import call
import psutil



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

run_cmd("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .")
run_cmd("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P .")

run_cmd("pip install basicsr")
run_cmd("pip freeze")



def inference(img,mode):
    _id = 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 -p " + INPUT_DIR)
    run_cmd("mkdir -p " + OUTPUT_DIR)

    img.save(INPUT_DIR + "1.png", "PNG")
    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.png")



        
title = "Real-ESRGAN"
description = "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"
article = "<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>"

gr.Interface(
    inference, 
    [gr.inputs.Image(type="pil", label="Input"),gr.inputs.Radio(["base","anime"], type="value", default="anime", label="model type")], 
    gr.outputs.Image(type="file", label="Output"),
    title=title,
    description=description,
    article=article
).launch()