Create app.py

app.py

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+import gradio as gr
+import requests
+from PIL import Image
+import os
+import torch
+import numpy as np
+from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution
+
+
+processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
+model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
+
+def enhance(image):
+    # prepare image for the model
+    inputs = processor(image, return_tensors="pt")
+
+    # forward pass
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # postprocess
+    output = outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+    output = np.moveaxis(output, source=0, destination=-1)
+    output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
+    
+    return Image.fromarray(output)
+
+title = "Demo: Swin2SR for Image Super-Resolution 🚀🚀🔥"
+description = ''' 
+**This demo expects low-quality and low-resolution JPEG compressed images.**
+**Demo notebook can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Swin2SR/Perform_image_super_resolution_with_Swin2SR.ipynb).
+'''
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2209.11345' target='_blank'>Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration</a> | <a href='https://huggingface.co/docs/transformers/main/model_doc/swin2sr' target='_blank'>HuggingFace docs</a></p>"
+
+examples = [['00003.jpg'], ['0855.jpg'], ['ali_eye.jpg'], ['butterfly.jpg'], ['chain-eye.jpg'], ['gojou-eyes.jpg'], ['shanghai.jpg'], ['vagabond.jpg']]
+
+gr.Interface(
+    enhance, 
+    gr.inputs.Image(type="pil", label="Input").style(height=260),
+    gr.inputs.Image(type="pil", label="Ouput").style(height=240),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    ).launch(enable_queue=True, share= True)