Update app.py

app.py

@@ -23,7 +23,7 @@ def inference(img, model):
 
 title = "Half Wavelet Attention on M-Net+ for Low-light Image Enhancement"
 description = "Gradio demo for HWMNet. HWMNet has competitive performance results on two real-world low-light datasets in terms of quantitative metrics and visual quality. See the paper and project page for detailed results below. Here, we provide a demo for low-light image enhancement. To use it, simply upload your image, or click one of the examples to load them. We present 2 pretrained models, which is trained on LOL and MIT-Adobe FiveK dataset, respectively. The images in LOL dataset are darker than MIT-Adobe FiveK, so if you have the extremely dark images you could consider it. On the contrary, the MIT-Adobe FiveK's model is suitable for minor adjustment of the images' hue."
-article = "<p style='text-align: center'><a href='https://' target='_blank'>Half Wavelet Attention on M-Net+ for Low-light Image Enhancement</a> | <a href='https://github.com/FanChiMao/HWMNet' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=52Hz_HWMNet_lowlight_enhancement' alt='visitor badge'></center>"
+article = "<p style='text-align: center'><a href='https://ieeexplore.ieee.org/document/9897503' target='_blank'>Half Wavelet Attention on M-Net+ for Low-light Image Enhancement</a> | <a href='https://github.com/FanChiMao/HWMNet' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=52Hz_HWMNet_lowlight_enhancement' alt='visitor badge'></center>"
 
 examples = [['low-light.png', 'LOL'], ['low-light_2.png', 'MIT-5K']]
 gr.Interface(