Update Requirements

app.py

@@ -14,7 +14,7 @@ with gr.Blocks() as demo:
         )
 
         description = "<p style='font-size: 14px; margin: 5px; font-weight: w300; text-align: center'> <a href='' style='text-decoration:none' target='_blank'>Krithika Rangarajan<sup>*</sup>, </a> <a href='https://github.com/Pranjal2041' style='text-decoration:none' target='_blank'>Pranjal Aggarwal<sup>*</sup>, </a> <a href='' style='text-decoration:none' target='_blank'>Dhruv Kumar Gupta, </a> <a href='' style='text-decoration:none' target='_blank'>Rohan Dhanakshirur, </a> <a href='' style='text-decoration:none' target='_blank'>Akhil Baby, </a> <a href='' style='text-decoration:none' target='_blank'>Chandan Pal, </a> <a href='' style='text-decoration:none' target='_blank'>Arun Kumar Gupta, </a> <a href='' style='text-decoration:none' target='_blank'>Smriti Hari, </a> <a href='' style='text-decoration:none' target='_blank'>Subhashis Banerjee, </a> <a href='' style='text-decoration:none' target='_blank'>Chetan Arora, </a> </p>" \
-            + "<p style='font-size: 16px; margin: 5px; font-weight: w600; text-align: center'>  <a href='https://link.springer.com/article/10.1007/s00330-023-09717-7' target='_blank'>Publication</a> | <a href='https://github.com/Pranjal2041/DenseMammogram' target='_blank'>Website</a> | <a href='https://github.com/Pranjal2041/DenseMammogram' target='_blank'>Github Repo</a></p>" \
+            + "<p style='font-size: 16px; margin: 5px; font-weight: w600; text-align: center'>  <a href='https://link.springer.com/article/10.1007/s00330-023-09717-7' target='_blank'>Publication</a> | <a href='https://pranjal2041.github.io/DenseMammogram/' target='_blank'>Website</a> | <a href='https://github.com/Pranjal2041/DenseMammogram' target='_blank'>Github Repo</a></p>" \
             + "<p style='text-align: center; margin: 5px; font-size: 14px; font-weight: w300;'>  \
                 Deep learning suffers from some problems similar to human radiologists, such as poor sensitivity to detection of isodense, obscure masses or cancers in dense breasts. Traditional radiology teaching can be incorporated into the deep learning approach to tackle these problems in the network. Our method suggests collaborative network design, and incorporates core radiology principles resulting in SOTA results. You can use this demo to run inference by providing bilateral mammogram images. To get started, you can try one of the preset examples. \
                 </p>" \

requirements.txt

@@ -9,3 +9,4 @@ pandas
 matplotlib
 numpy
 easydict==1.9
+opencv-python