Create app.py

app.py

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+import gradio as gr
+import spaces
+import torch
+from PIL import Image
+from RealESRGAN import RealESRGAN
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model2 = RealESRGAN(device, scale=2)
+model2.load_weights('weights/RealESRGAN_x2.pth', download=True)
+model4 = RealESRGAN(device, scale=4)
+model4.load_weights('weights/RealESRGAN_x4.pth', download=True)
+model8 = RealESRGAN(device, scale=8)
+model8.load_weights('weights/RealESRGAN_x8.pth', download=True)
+
+
+@spaces.GPU(duration=17)
+def inference(image, size):
+    global model2
+    global model4
+    global model8
+    if image is None:
+        raise gr.Error("Image not uploaded")
+        
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    
+    if size == '2x':
+        try:
+            result = model2.predict(image.convert('RGB'))
+        except torch.cuda.OutOfMemoryError as e:
+            print(e)
+            model2 = RealESRGAN(device, scale=2)
+            model2.load_weights('weights/RealESRGAN_x2.pth', download=False)
+            result = model2.predict(image.convert('RGB'))
+    elif size == '4x':
+        try:
+            result = model4.predict(image.convert('RGB'))
+        except torch.cuda.OutOfMemoryError as e:
+            print(e)
+            model4 = RealESRGAN(device, scale=4)
+            model4.load_weights('weights/RealESRGAN_x4.pth', download=False)
+            result = model2.predict(image.convert('RGB'))
+    else:
+        try:
+            result = model8.predict(image.convert('RGB'))
+        except torch.cuda.OutOfMemoryError as e:
+            print(e)
+            model8 = RealESRGAN(device, scale=8)
+            model8.load_weights('weights/RealESRGAN_x8.pth', download=False)
+            result = model2.predict(image.convert('RGB'))
+            
+    print(f"Image size ({device}): {size} ... OK")
+    return result
+
+
+title = "Face Real ESRGAN UpScale: 2x 4x 8x"
+description = "This is an unofficial demo for Real-ESRGAN. Scales the resolution of a photo. This model shows better results on faces compared to the original version.<br>Telegram BOT: https://t.me/restoration_photo_bot"
+article = "<div style='text-align: center;'>Twitter <a href='https://twitter.com/DoEvent' target='_blank'>Max Skobeev</a> | <a href='https://huggingface.co/sberbank-ai/Real-ESRGAN' target='_blank'>Model card</a><div>"
+
+
+gr.Interface(inference,
+    [gr.Image(type="pil"), 
+    gr.Radio(["2x", "4x", "8x"], 
+    type="value",
+    value="2x",
+    label="Resolution model")], 
+    gr.Image(type="pil", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[["groot.jpeg", "2x"]],
+    flagging_mode="never",
+    cache_mode="lazy",
+    delete_cache=(44000, 44000),
+    ).queue(api_open=True).launch(show_error=True, show_api=True, mcp_server=True)