object alignment

app.py

@@ -65,9 +65,12 @@ body {
     background-position: center;
     background-size: cover;
     background-attachment: fixed;
-    height: 100%;  /* Ensure body height is 100% of the viewport */
-    margin: 0;
     overflow-y: auto;  /* Allow vertical scrolling */
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    height: 100vh;
+    margin: 0;
 }
 
 .custom-row {
@@ -182,11 +185,11 @@ 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.
+        ## 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"):