Update app.py

app.py

@@ -474,6 +474,17 @@ with gr.Blocks(css="""
         margin-bottom: 20px;
     }
 
+    .console-output {
+        background-color: #ffffff;  /* Ensure it's visible in light mode */
+        color: #000000;  /* Text in dark color for better contrast */
+        padding: 10px;
+        border-radius: 5px;
+    }
+
+    .plot-title {
+        font-weight: bold;
+        color: #2c3e50;
+    }
 """) as demo:
 
     # Contact Section
@@ -490,14 +501,14 @@ with gr.Blocks(css="""
     
     gr.Markdown("""
     <div class="bold-highlight">
-        🚀 Explore the pre-trained **LWM Model** here: 
+        🚀 Explore the pre-trained <b>LWM Model<b> here: 
         <a target="_blank" href="https://huggingface.co/wi-lab/lwm">https://huggingface.co/wi-lab/lwm</a>
     </div>
     """)
 
     # Tab for Beam Prediction Task
     with gr.Tab("Beam Prediction Task"):
-        gr.Markdown("### Beam Prediction Task")
+        #gr.Markdown("### Beam Prediction Task")
 
         # Explanation section with creative spacing and minimal design
         gr.Markdown("""
@@ -516,11 +527,6 @@ with gr.Blocks(css="""
               </ul>
             </div>
         """)
-        #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. These are just inferences on first the LWM model and then the trained downstream model for this task (A residual 1D-CNN model with 500K parameters). The dataset used for this task is a combination of six scenarios from the DeepMIMO dataset that were not included in the LWM pre-training, showing the genralization of our model.
-        #</div>
-        #""")
 
         with gr.Row():
             with gr.Column():
@@ -535,9 +541,16 @@ with gr.Blocks(css="""
         data_percentage_slider.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_dropdown], outputs=[raw_img_bp, embeddings_img_bp])
         task_complexity_dropdown.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_dropdown], outputs=[raw_img_bp, embeddings_img_bp])
 
+        # Add a conclusion section at the bottom
+        gr.Markdown("""
+            <div class="explanation-box">
+                **Conclusions**: By adjusting the data percentage and task complexity, you can observe how the LWM generalizes well to unseen scenarios and handles various complexities in the beam prediction task.
+            </div>
+        """)
+
     # Separate Tab for LoS/NLoS Classification Task
     with gr.Tab("LoS/NLoS Classification Task"):
-        gr.Markdown("### LoS/NLoS Classification Task")
+        #gr.Markdown("### LoS/NLoS Classification Task")
 
         # Explanation section with creative spacing
         gr.Markdown("""
@@ -557,11 +570,6 @@ with gr.Blocks(css="""
               </ul>
             </div>
         """)
-        #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. The default dataset is a combinbation of six scenarios from the DeepMIMO dataset. Your custom dataset in h5py format should contain 'channels' array of size (N,32,32), and 'labels' representing LoS/NLoS channels with 1/0. You can find additional information on how to save your dataset in the h5py format in the above-mentioned model repository. The interesting thing about this task is that we do not train any downstream model for LoS/NLoS classification, but just use a simple approach that predicts the label for a test sample based on the distance to the centroid of training samples corresponding to each label.
-        #</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")
@@ -579,7 +587,7 @@ with gr.Blocks(css="""
         with gr.Row():
             raw_img_los = gr.Image(label="Raw Channels", type="pil", width=300, height=300)
             embeddings_img_los = gr.Image(label="Embeddings", type="pil", width=300, height=300)
-            output_textbox = gr.Textbox(label="Console Output", lines=10)
+            output_textbox = gr.Textbox(label="Console Output", lines=10, css_classes=["console-output"])
 
         # Update the file uploader visibility based on user choice
         def toggle_file_input(choice):
@@ -595,6 +603,13 @@ 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])
 
+        # Add a conclusion section at the bottom
+        gr.Markdown("""
+            <div class="explanation-box">
+                **Conclusions**: With this task, you can evaluate how well LWM embeddings perform on LoS/NLoS classification tasks, and compare it to the performance of raw channels in identifying these features.
+            </div>
+        """)
+
 # Launch the app
 if __name__ == "__main__":
     demo.launch()