Delete app.py

app.py

@@ -1,77 +0,0 @@
-import torch
-from PIL import Image
-from RealESRGAN import RealESRGAN
-import gradio as gr
-
-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)
-
-
-def inference(image, size):
-    global model2
-    global model4
-    global model8
-    if image is None:
-        raise gr.Error("Image not uploaded")
-        
-    width, height = image.size
-    if width >= 5000 or height >= 5000:
-        raise gr.Error("The image is too large.")
-
-    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"]],
-    allow_flagging='never',
-    cache_examples=False,
-    ).queue(api_open=False).launch(show_error=True, show_api=False)
-