app.py

import gradio as gr
from PIL import Image
import torch
import torchvision.transforms as transforms
from models.network_swinir import SwinIR as net

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load pretrained model
model = net(img_size=64, in_nc=3, out_nc=3, nf=64, n_resblocks=8).to(device)
model.load_state_dict(torch.load('001_classicalSR_DF2K_s64w8_SwinIR-M_x8.pth', map_location=device))
model.eval()

def process_img(input_image: Image.Image):
    # Resize to low resolution
    input_image = input_image.resize((input_image.width // 4, input_image.height // 4))

    # Transform to tensor
    transform = transforms.ToTensor()
    input_tensor = transform(input_image).unsqueeze(0).to(device)

    # Use the model to upscale image
    with torch.no_grad():
        output_tensor = model(input_tensor)

    # Transform the output tensor to image
    output_image = transforms.ToPILImage()(output_tensor.squeeze().cpu())

    return output_image

iface = gr.Interface(
    fn=process_img, 
    inputs=gr.inputs.Image(type="pil"), 
    outputs="image", 
    title="SwinIR upscaling"
)
iface.launch()