Update app.py

app.py

@@ -561,7 +561,7 @@ with gr.Blocks(css="""
         # Add a conclusion section at the bottom
         gr.Markdown("""
             <div class="explanation-box">
-                <b>Conclusions<b>: LWM embeddings offer such high generalization that with just a limited number of training samples, we can get high performances.
+                The LWM embeddings demonstrate remarkable generalization capabilities, enabling impressive performance even with minimal training samples. This highlights their ability to effectively handle diverse tasks with limited data.
             </div>
         """)
 
@@ -623,7 +623,7 @@ with gr.Blocks(css="""
         # Add a conclusion section at the bottom
         gr.Markdown("""
             <div class="explanation-box">
-                <b>Conclusions<b>: LWM CLS embeddings, although very small (raw channels size / 32), offer a rich and holistic knowledge about channels, making them suitable for a task like LoS/NLoS classfication, specifically with very limited data.  
+                Despite their compact size (1/32 of the raw channels), LWM CLS embeddings capture rich, holistic information about the channels. This makes them exceptionally well-suited for tasks like LoS/NLoS classification, especially when working with very limited data.
             </div>
         """)