Update app.py

app.py

@@ -154,7 +154,7 @@ def compute_average_confusion_matrix(folder):
 LOS_PATH = "images_LoS"
 
 # Define the percentage values
-percentage_values_los = np.linspace(0.001, 1, 20) * 100  # 20 percentage values
+percentage_values_los = np.linspace(0.05, 1, 20) * 100 # np.linspace(0.001, 1, 20) * 100  # 20 percentage values
 
 from sklearn.metrics import f1_score
 import seaborn as sns
@@ -381,6 +381,9 @@ def plot_confusion_matrix(y_true, y_pred, title, light_mode=False):
 
 
 def identical_train_test_split(output_emb, output_raw, labels, train_percentage):
+
+    torch.manual_seed(seed)
+    
     N = output_emb.shape[0]
     indices = torch.randperm(N) 
     test_split_index = int(N * 0.20)
@@ -632,7 +635,7 @@ with gr.Blocks(css="""
         
         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]), 
+                                  step=float((percentage_values_los[-1] - percentage_values_los[0]) / (len(percentage_values_los) - 1)), #step=float(percentage_values_los[1] - percentage_values_los[0]), 
                                   value=float(percentage_values_los[0]), 
                                   label="Percentage of Data for Training", interactive=True)
 
@@ -681,8 +684,11 @@ with gr.Blocks(css="""
         """)
         gr.Markdown("""
             <div class="explanation-box">
-                To use your preferred DeepMIMO scenarios for the custom dataset, please [clone the model and datasets](https://huggingface.co/wi-lab/lwm) and follow the instructions below:
+                To use your preferred DeepMIMO scenarios for the custom dataset, please 
+                <a href="https://huggingface.co/wi-lab/lwm" target="_blank">clone the model and datasets</a> 
+                and follow the instructions below:
             </div>
+
             
             ```python
             from input_preprocess import DeepMIMO_data_gen deepmimo_data_cleaning label_gen # Import required modules from the model repository