Create app.py

app.py

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+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")
+iface.launch()