Update app.py

app.py

@@ -324,7 +324,7 @@ def classify_based_on_distance(train_data, train_labels, test_data):
     
     return torch.tensor(predictions)  # Return predictions as a PyTorch tensor
 
-def plot_confusion_matrix(y_true, y_pred, title):
+def plot_confusion_matrix(y_true, y_pred, title, light_mode=True):
     cm = confusion_matrix(y_true, y_pred)
     
     # Calculate F1 Score
@@ -585,7 +585,7 @@ with gr.Blocks(css="""
             <div style="background-color: #f0f0f0; padding: 15px; border-radius: 10px; color: #333;">
               <h3 style="color: #0056b3;">🔍 <b>LoS/NLoS Classification Task</b></h3>
               <ul style="padding-left: 20px;">
-                <li><b>🎯 Goal</b>: Classify whether a channel is <b>LoS</b> (Line-of-Sight) or <b>NLoS</b> (Non-Line-of-Sight).</li>
+                <li><b>🎯 Goal</b>: Classify whether a channel is <b>LoS</b> (Line-of-Sight) or <b>NLoS</b> (Non-Line-of-Sight) with very small LWM CLS embeddings.</li>
                 <li><b>📂 Dataset</b>: Use the default dataset (a combination of six scenarios from the DeepMIMO dataset) or upload your own dataset in <b>h5py</b> format.</li>
                 <li><b>💡 Custom Dataset Requirements:</b>
                   <ul>
@@ -594,7 +594,7 @@ with gr.Blocks(css="""
                   </ul>
                 </li>
                 <li><b>🔗 Tip</b>: You can find guidance on how to structure your dataset in the provided model repository.</li>
-                <li><b>💼 No Downstream Model</b>: Instead of a complex downstream model, we classify each sample based on its distance to the centroid of training samples from each class (LoS/NLoS).</li>
+                <li><b>💼 No Downstream Model</b>: Instead of a complex downstream model, we classify each sample based on its distance to the centroid of training samples from each class (LoS/NLoS).</il>
               </ul>
             </div>
         """)