add text desc

app.py

@@ -185,7 +185,14 @@ with gr.Blocks(css=custom_css) as interface:
         outputs=sample_display,
     )
     
+    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.
+
+        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.
+
+    """)
     with gr.Row(elem_classes="custom-row"):
         dff_gallery = gr.Gallery(
             label="Deep Feature Factorization",