Update app.py

app.py

@@ -368,7 +368,7 @@ def process_hdf5_file(uploaded_file, percentage):
     sys.stdout = capture  # Redirect print statements to capture
     
     try:
-        model_repo_url = "https://huggingface.co/sadjadalikhani/lwm"
+        model_repo_url = "https://huggingface.co/wi-lab/lwm"
         model_repo_dir = "./LWM"
 
         # Step 1: Clone the repository if not already done
@@ -491,7 +491,7 @@ with gr.Blocks(css="""
     gr.Markdown("""
     <div class="bold-highlight">
         🚀 Explore the pre-trained **LWM Model** here: 
-        <a target="_blank" href="https://huggingface.co/sadjadalikhani/lwm/tree/main">https://huggingface.co/sadjadalikhani/lwm/tree/main</a>
+        <a target="_blank" href="https://huggingface.co/wi-lab/lwm">https://huggingface.co/wi-lab/lwm</a>
     </div>
     """)
 
@@ -501,20 +501,20 @@ with gr.Blocks(css="""
 
         # Explanation section with creative spacing and minimal design
         gr.Markdown("""
-        <div class="explanation-box">
-          <h3>📡 Beam Prediction Task</h3>
-          <ul>
-            <li>🎯 **Goal**: Predict the strongest **mmWave beam** from a predefined codebook using Sub-6 GHz channels.</li>
-            <li>⚙️ **Adjust Settings**: Use the sliders to control the training data percentage and task complexity (beam count) to explore model performance.</li>
-            <li>🧠 **Inferences**:
-              <ul>
-                <li>🔍 First, the LWM model extracts features.</li>
-                <li>🤖 Then, the downstream residual 1D-CNN model (500K parameters) makes beam predictions.</li>
+            <div style="background-color: #f0f0f0; padding: 15px; border-radius: 10px; color: #333;">
+              <h3 style="color: #0056b3;">📡 <b>Beam Prediction Task</b></h3>
+              <ul style="padding-left: 20px;">
+                <li><b>🎯 Goal</b>: Predict the strongest <b>mmWave beam</b> from a predefined codebook using Sub-6 GHz channels.</li>
+                <li><b>⚙️ Adjust Settings</b>: Use the sliders to control the training data percentage and task complexity (beam count) to explore model performance.</li>
+                <li><b>🧠 Inferences</b>:
+                  <ul>
+                    <li>🔍 First, the LWM model extracts features.</li>
+                    <li>🤖 Then, the downstream residual 1D-CNN model (500K parameters) makes beam predictions.</li>
+                  </ul>
+                </li>
+                <li><b>🗺️ Dataset</b>: A combination of six scenarios from the DeepMIMO dataset (excluded from LWM pre-training) highlights the model's strong generalization abilities.</li>
               </ul>
-            </li>
-            <li>🗺️ **Dataset**: A combination of six scenarios from the DeepMIMO dataset (excluded from LWM pre-training) highlights the model's strong generalization abilities.</li>
-          </ul>
-        </div>
+            </div>
         """)
         #gr.Markdown("""
         #<div class="explanation-box">
@@ -541,21 +541,21 @@ with gr.Blocks(css="""
 
         # Explanation section with creative spacing
         gr.Markdown("""
-        <div class="explanation-box">
-          <h3>🔍 LoS/NLoS Classification Task</h3>
-          <ul>
-            <li>🎯 **Goal**: Classify whether a channel is **LoS** (Line-of-Sight) or **NLoS** (Non-Line-of-Sight).</li>
-            <li>📂 **Dataset**: Use the default dataset (a combination of six scenarios from the DeepMIMO dataset) or upload your own dataset in **h5py** format.</li>
-            <li>💡 **Custom Dataset Requirements**:
-              <ul>
-                <li>🛠️ `channels` array: Shape (N,32,32)</li>
-                <li>🏷️ `labels` array: Binary LoS/NLoS values (1/0)</li>
+            <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>📂 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>
+                    <li>🛠️ <b>channels</b> array: Shape (N,32,32)</li>
+                    <li>🏷️ <b>labels</b> array: Binary LoS/NLoS values (1/0)</li>
+                  </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>
               </ul>
-            </li>
-            <li>🔗 **Tip**: You can find guidance on how to structure your dataset in the provided model repository.</li>
-            <li>💼 **No Downstream Model**: 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>
-          </ul>
-        </div>
+            </div>
         """)
         #gr.Markdown("""
         #<div class="explanation-box">