Update app.py

app.py

@@ -179,7 +179,6 @@ with gr.Blocks(css=custom_css) as interface:
     )
     
     gr.Markdown("""
-         ## <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.