Create app.py

app.py

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+import gradio as gr
+from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution
+from PIL import Image
+import torch
+
+# Load model and processor
+processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-lightweight-x4-64")
+model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-lightweight-x4-64")
+
+def upscale_image(image):
+    # Preprocess the input image
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Perform super-resolution
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Post-process the output to get a high-resolution image
+    output_image = processor.postprocess(outputs.logits, target_sizes=[(image.size[1]*4, image.size[0]*4)])[0]
+    return output_image
+
+# Gradio interface
+iface = gr.Interface(
+    fn=upscale_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="Image Super Resolution with Swin2SR",
+    description="Upload an image and enhance its resolution using the Swin2SR model (4x resolution)."
+)
+
+if __name__ == "__main__":
+    iface.launch()