update intro

app.py

@@ -52,7 +52,7 @@ def inference(img):
 
 
 title = "Melanoma Detection using Adversarial Training and Deep Transfer Learning"
-description = "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."
+description = codecs.open("description.html", 'r', "utf-8").read()
 article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2004.06824' target='_blank'>Melanoma Detection using Adversarial Training and Deep Transfer Learning</a> | <a href='https://github.com/hasibzunair/adversarial-lesions' target='_blank'>Github</a></p>"
 
 

description.html

@@ -11,11 +11,15 @@
     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>
+    Code is publicly available in <a href='https://github.com/hasibzunair/adversarial-lesions' target='_blank'>Github</a>.<br><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.
+    under heavy imbalance conditions, but also detect the target class (e.g. COVID-19) with high confidence. <br><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.
 </body>
 </html>