changed text font and colour

app.py

@@ -122,11 +122,11 @@ body {
 # Then in the Gradio interface:
 
 with gr.Blocks(css=custom_css) as interface:
-    gr.Markdown("""
-        ## <span id="neural-vista-title">NeuralVista</span>
-        A powerful tool designed to help you visualize  models in action.
-
-    """)
+   
+    gr.HTML("""
+    <span style="color: #E6E6FA; font-weight: bold;" id="neural-vista-title">NeuralVista</span>
+    A powerful tool designed to help you <span style="color: #E6E6FA; font-weight: bold;" id="neural-vista-title">visualize</span> models in action.
+""")
     
     # Default sample
     default_sample = "Sample 1"
@@ -179,9 +179,11 @@ with gr.Blocks(css=custom_css) as interface:
     )
     
     gr.HTML("""
-    <span style="color: purple;">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.
+    <span style="color: #E6E6FA; 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.
 
-    <span style="color: purple;">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.
+    <span style="color: #E6E6FA; font-weight: bold;">Deep Feature Factorization (DFF)</span> is a technique that decomposes the deep features learned by a model into <span style="color: #E6E6FA; font-weight: bold;" id="neural-vista-title">disentangled and interpretable components</span>. 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.
 """)