Update app.py

app.py

@@ -186,7 +186,7 @@ with gr.Blocks(css=custom_css) as interface:
     )
     
     gr.HTML("""
-    <span style="color: purple; font-weight: bold;">Concept Discovery</span> involves identifying interpretable high-level features or concepts within a deep learning model's representation. It aims to understand what a model has learned and how these learned features relate to meaningful attributes in the data.<br>
+    <span style="color: purple; font-weight: bold;">Concept Discovery</span> involves identifying interpretable high-level features or concepts within a deep learning model's representation. It aims to understand what a model has learned and how these learned features relate to meaningful attributes in the data.<br><br>
 
     <span style="color: purple; font-weight: bold;">Deep Feature Factorization (DFF)</span> is a technique that decomposes the deep features learned by a model into disentangled and interpretable components. It typically involves matrix factorization methods applied to activation maps, enabling the identification of semantically meaningful concepts captured by the model.