app.py

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

# Remove the torch.hub download and instead ensure 'bear.jpg' is in your directory
# Place bear.jpg and anime.png in your project directory manually

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

# Download model weights if they don't exist
if not os.path.exists("RealESRGAN_x4plus.pth"):
    run_cmd("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .")

run_cmd("pip install basicsr")

if not os.path.exists("RealESRGAN_x4plus_anime_6B.pth"):
    os.system("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 = f"/tmp/input_image{_id}/"
    OUTPUT_DIR = f"/tmp/output_image{_id}/"
    
    # Create directories safely
    os.makedirs(INPUT_DIR, exist_ok=True)
    os.makedirs(OUTPUT_DIR, exist_ok=True)
    
    # Resize image
    basewidth = 256
    wpercent = (basewidth/float(img.size[0]))
    hsize = int((float(img.size[1])*float(wpercent)))
    img = img.resize((basewidth,hsize), Image.LANCZOS)
    
    input_path = os.path.join(INPUT_DIR, "1.jpg")
    img.save(input_path, "JPEG")
    
    if mode == "base":
        model_name = "RealESRGAN_x4plus"
    else:
        model_name = "RealESRGAN_x4plus_anime_6B"
    
    command = f"python inference_realesrgan.py -n {model_name} -i {INPUT_DIR} -o {OUTPUT_DIR}"
    run_cmd(command)
    
    output_path = os.path.join(OUTPUT_DIR, "1_out.jpg")
    
    # Cleanup temporary directories
    try:
        if os.path.exists(INPUT_DIR):
            os.system(f"rm -rf {INPUT_DIR}")
        if os.path.exists(OUTPUT_DIR):
            os.system(f"rm -rf {OUTPUT_DIR}")
    except Exception as e:
        print(f"Cleanup error: {e}")
    
    return output_path

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>"

# Create interface
interface = gr.Interface(
    inference,
    [
        gr.inputs.Image(type="pil", label="Input"),
        gr.inputs.Radio(["base", "anime"], type="value", default="base", label="model type")
    ],
    gr.outputs.Image(type="file", label="Output"),
    title=title,
    description=description,
    article=article,
    examples=[
        ['bear.jpg', 'base'],
        ['anime.png', 'anime']
    ]
)

# Launch the interface
interface.launch()