text update

app.py

@@ -179,11 +179,12 @@ with gr.Blocks(css=custom_css) as interface:
     )
     
     gr.Markdown("""
-        ## Concept Discovery 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.
+         ## <span id="neural-vista-title">Concept Discovery</span>
+         Concept Discovery 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.
 
-        ## Deep Feature Factorization (DFF) 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.
+         Deep Feature Factorization (DFF) 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.
 
-        ## Together, these methods enhance model interpretability and provide insights into the decision-making process of neural networks.
+         Together, these methods enhance model interpretability and provide insights into the decision-making process of neural networks.
 
     """)
     with gr.Row(elem_classes="custom-row"):