app.py

import os
import random
import gradio as gr
from PIL import Image
import torch
from random import randint
import sys
import psutil
import subprocess

def run_cmd(command):
    try:
        print(f"Running command: {command}")
        # Run the command and capture both output and error
        result = subprocess.run(command, shell=True, text=True, capture_output=True)

        # Print stdout and stderr
        if result.stdout:
            print("Output:\n", result.stdout)
        
        if result.stderr:
            print("Error:\n", result.stderr)
        
        # Check for command success
        if result.returncode != 0:
            print(f"Command failed with return code {result.returncode}")

    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 .")



def inference(img,mode):
    _id = randint(1, 10000)
    INPUT_DIR = "./tmp/input_image" + str(_id) + "/"
    OUTPUT_DIR = "./tmp/output_image" + str(_id) + "/"

    run_cmd("ls -l ./tmp/*/*")

    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()