Update app.py

app.py

@@ -1,52 +1,52 @@
-import gradio as gr
-import torch
-import torch.nn as nn
-import torchvision
-import torch.backends.cudnn as cudnn
-import torch.optim
-import os
-import sys
-import argparse
-import dataloader
-import model
-import numpy as np
-from torchvision import transforms
-from PIL import Image
-import glob
-
- 
-def lowlight(image):
-	os.environ['CUDA_VISIBLE_DEVICES']=''
-	data_lowlight = Image.open(image)
-	
-	data_lowlight = (np.asarray(data_lowlight)/255.0)
-	data_lowlight = torch.from_numpy(data_lowlight).float()
-	data_lowlight = data_lowlight.permute(2,0,1)
-	data_lowlight = data_lowlight.cpu().unsqueeze(0)
-
-	DCE_net = model.enhance_net_nopool().cpu()
-	DCE_net.load_state_dict(torch.load('Epoch99.pth', map_location=torch.device('cpu')))
-	
-	_,enhanced_image,_ = DCE_net(data_lowlight)
-
-	torchvision.utils.save_image(enhanced_image, f'01.png')
-
-	return '01.png'
-
-
-title = "Low-Light Image Enhancement using Zero-DCE"
-description = "Gradio Demo for Low-Light Enhancement using Zero-DCE. The model improves the quality of images that have poor contrast, low brightness, and suboptimal exposure. To use it, simply upload your image, or click one of the examples to load them. Check out the original paper and the GitHub repo at the links below. "
-article = "<p style='text-align: center'><a href='http://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_Zero-Reference_Deep_Curve_Estimation_for_Low-Light_Image_Enhancement_CVPR_2020_paper.pdf' target='_blank'>Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement</a> | <a href='https://github.com/Li-Chongyi/Zero-DCE' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=52Hz_CMFNet_deblurring' alt='visitor badge'></center>"
-
-examples = [['01.jpg'], ['02.jpg'], ['03.jpg'], ['04.png'], ['05.jpg'],]
-gr.Interface(
-    lowlight,
-    [gr.inputs.Image(type="file", label="Input")],
-    [gr.outputs.Image(type="file", label="Output")],
-    title=title,
-    description=description,
-    article=article,
-    allow_flagging=False,
-    allow_screenshot=False,
-    examples=examples
-).launch(debug=True)
+import gradio as gr
+import torch
+import torch.nn as nn
+import torchvision
+import torch.backends.cudnn as cudnn
+import torch.optim
+import os
+import sys
+import argparse
+import dataloader
+import model
+import numpy as np
+from torchvision import transforms
+from PIL import Image
+import glob
+
+
+def lowlight(image):
+	os.environ['CUDA_VISIBLE_DEVICES']=''
+	data_lowlight = Image.open(image)
+	
+	data_lowlight = (np.asarray(data_lowlight)/255.0)
+	data_lowlight = torch.from_numpy(data_lowlight).float()
+	data_lowlight = data_lowlight.permute(2,0,1)
+	data_lowlight = data_lowlight.cpu().unsqueeze(0)
+
+	DCE_net = model.enhance_net_nopool().cpu()
+	DCE_net.load_state_dict(torch.load('Epoch99.pth', map_location=torch.device('cpu')))
+	
+	_,enhanced_image,_ = DCE_net(data_lowlight)
+
+	torchvision.utils.save_image(enhanced_image, f'01.png')
+
+	return '01.png'
+
+
+title = "Low-Light Image Enhancement using Zero-DCE"
+description = "Gradio Demo for Low-Light Enhancement using Zero-DCE. The model improves the quality of images that have poor contrast, low brightness, and suboptimal exposure. To use it, simply upload your image, or click one of the examples to load them. Check out the original paper and the GitHub repo at the links below. "
+article = "<p style='text-align: center'><a href='http://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_Zero-Reference_Deep_Curve_Estimation_for_Low-Light_Image_Enhancement_CVPR_2020_paper.pdf' target='_blank'>Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement</a> | <a href='https://github.com/Li-Chongyi/Zero-DCE' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=52Hz_CMFNet_deblurring' alt='visitor badge'></center>"
+
+examples = [['01.jpg'], ['02.jpg'], ['03.jpg'], ['04.png'], ['05.jpg'],]
+gr.Interface(
+    lowlight,
+    [gr.inputs.Image(type="file", label="Input")],
+    [gr.outputs.Image(type="file", label="Output")],
+    title=title,
+    description=description,
+    article=article,
+    allow_flagging=False,
+    allow_screenshot=False,
+    examples=examples
+).launch(debug=True)