Update app.py

app.py

@@ -456,6 +456,16 @@ with gr.Blocks(css="""
         text-align: center;
     }
 
+    .explanation-box {
+        font-size: 16px;
+        font-style: italic;
+        color: #4a4a4a;
+        padding: 15px;
+        background-color: #f0f0f0;
+        border-radius: 10px;
+        margin-bottom: 20px;
+    }
+
 """) as demo:
 
     # Contact Section
@@ -469,11 +479,18 @@ with gr.Blocks(css="""
             </a>
         </div>
     """)
-    
+
     # Tab for Beam Prediction Task
     with gr.Tab("Beam Prediction Task"):
         gr.Markdown("### Beam Prediction Task")
-        
+
+        # Explanation section with creative spacing and minimal design
+        gr.Markdown("""
+        <div class="explanation-box">
+            In this task, you'll predict the strongest mmWave beam from a predefined codebook based on Sub-6 GHz channels. Adjust the data percentage and task complexity to observe how LWM performs on different settings.
+        </div>
+        """)
+
         with gr.Row():
             with gr.Column():
                 data_percentage_slider = gr.Slider(label="Data Percentage for Training", minimum=10, maximum=100, step=10, value=10)
@@ -491,12 +508,16 @@ with gr.Blocks(css="""
     with gr.Tab("LoS/NLoS Classification Task"):
         gr.Markdown("### LoS/NLoS Classification Task")
 
+        # Explanation section with creative spacing
+        gr.Markdown("""
+        <div class="explanation-box">
+            Use this task to classify whether a channel is LoS (Line-of-Sight) or NLoS (Non-Line-of-Sight). You can either upload your own dataset or use the default dataset to explore how LWM embeddings compare to raw channels.
+        </div>
+        """)
+
         # Radio button for user choice: predefined data or upload dataset
         choice_radio = gr.Radio(choices=["Use Default Dataset", "Upload Dataset"], label="Choose how to proceed", value="Use Default Dataset")
         
-        # Dropdown for selecting percentage for predefined data
-        #percentage_dropdown_los = gr.Dropdown(choices=[f"{value:.3f}" for value in percentage_values_los], value=f"{percentage_values_los[0]:.3f}", label="Percentage of Data for Training")
-        #percentage_dropdown_los = gr.Dropdown(choices=list(range(20)), value=0, label="Percentage of Data for Training")
         percentage_slider_los = gr.Slider(minimum=float(percentage_values_los[0]), 
                                   maximum=float(percentage_values_los[-1]), 
                                   step=float(percentage_values_los[1] - percentage_values_los[0]), 
@@ -526,6 +547,7 @@ with gr.Blocks(css="""
         percentage_slider_los.change(fn=handle_user_choice, inputs=[choice_radio, percentage_slider_los, file_input], 
                                        outputs=[raw_img_los, embeddings_img_los, output_textbox])
 
+
 # Launch the app
 if __name__ == "__main__":
     demo.launch()