update app

description.html

@@ -11,13 +11,13 @@
     class samples and over-represented samples in a bid to generate under-represented class samples 
     using unpaired image-to-image translation. These synthetic images are then used as additional 
     training data in the task of detecting abnormalities in binary classification use-cases. 
-    Code is publicly available in <a href='https://github.com/hasibzunair/adversarial-lesions' target='_blank'>Github</a>.</br></br>
+    Code is publicly available in <a href='https://github.com/hasibzunair/adversarial-lesions'>Github</a>.</br>
 
     This method was also effective for COVID-19 detection from chest radiography images which led to 
     <a href="https://github.com/hasibzunair/synthetic-covid-cxr-dataset">Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis</a>. 
     The synthetic images not only improved performance of various deep learning architectures when used as additional training data
-    under heavy imbalance conditions, but also detect the target class (e.g. COVID-19) with high confidence. </br></br>
-
+    under heavy imbalance conditions, but also detect the target class (e.g. COVID-19) with high confidence. </br>
+    
     This model predicts if the given image has benign or malignant symptoms. 
     To use it, simply upload a skin lesion image, or click one of the examples to load them. 
     Read more at the links below.