Update app.py

app.py

@@ -247,13 +247,21 @@ def display_predefined_images(percentage_idx):
     return raw_image, embeddings_image
 
 # Updated los_nlos_classification to handle missing outputs properly
+#def los_nlos_classification(file, percentage_idx):
+#    if file is not None:
+#        raw_cm_image, emb_cm_image, console_output = process_hdf5_file(file, percentage_idx)
+#        return raw_cm_image, emb_cm_image, console_output
+#    else:
+#        raw_image, embeddings_image = display_predefined_images(percentage_idx)
+#        return raw_image, embeddings_image, "No file uploaded. Displaying predefined images."
+
 def los_nlos_classification(file, percentage_idx):
     if file is not None:
         raw_cm_image, emb_cm_image, console_output = process_hdf5_file(file, percentage_idx)
-        return raw_cm_image, emb_cm_image, console_output
+        return raw_cm_image, emb_cm_image, console_output  # Returning all three: two images and console output
     else:
         raw_image, embeddings_image = display_predefined_images(percentage_idx)
-        return raw_image, embeddings_image, "No file uploaded. Displaying predefined images."
+        return raw_image, embeddings_image, ""  # Return an empty string for console output when no file is uploaded
 
 # Function to create random images for LoS/NLoS classification results
 def create_random_image(size=(300, 300)):
@@ -488,7 +496,7 @@ with gr.Blocks(css="""
         gr.Markdown("### LoS/NLoS Classification Task")
 
         # Radio button for user choice: predefined data or upload dataset
-        choice_radio = gr.Radio(choices=["Use Predefined Data", "Upload Dataset"], label="Choose how to proceed", value="Use Predefined Data")
+        choice_radio = gr.Radio(choices=["Use Default Dataset", "Upload Dataset"], label="Choose how to proceed", value="Use Predefined Data")
         
         # Dropdown for selecting percentage for predefined data
         percentage_dropdown_los = gr.Dropdown(choices=list(range(20)), value=0, label="Percentage of Data for Training")