Update app.py

app.py

@@ -34,14 +34,31 @@ class PrintCapture(io.StringIO):
 # Function to load and display predefined images based on user selection
 def display_predefined_images(percentage_idx):
     percentage = percentage_values[percentage_idx]
-    raw_image_path = os.path.join(RAW_PATH, f"percentage_{percentage}_complexity_16.png")  # Assume complexity 16 for simplicity
+    raw_image_path = os.path.join(RAW_PATH, f"percentage_{percentage}_complexity_16.png")
     embeddings_image_path = os.path.join(EMBEDDINGS_PATH, f"percentage_{percentage}_complexity_16.png")
     
-    raw_image = Image.open(raw_image_path)
-    embeddings_image = Image.open(embeddings_image_path)
+    # Check if the images exist
+    if os.path.exists(raw_image_path):
+        raw_image = Image.open(raw_image_path)
+    else:
+        raw_image = create_random_image()  # Use a fallback random image
     
+    if os.path.exists(embeddings_image_path):
+        embeddings_image = Image.open(embeddings_image_path)
+    else:
+        embeddings_image = create_random_image()  # Use a fallback random image
+
     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."
+
 # Function to create random images for LoS/NLoS classification results
 def create_random_image(size=(300, 300)):
     random_image = np.random.rand(*size, 3) * 255
@@ -227,13 +244,6 @@ def process_hdf5_file(uploaded_file, percentage_idx):
         os.chdir(original_dir)
         sys.stdout = sys.__stdout__  # Reset print statements
 
-# Function to handle logic based on whether a file is uploaded or not
-def los_nlos_classification(file, percentage_idx):
-    if file is not None:
-        return process_hdf5_file(file, percentage_idx)
-    else:
-        return display_predefined_images(percentage_idx), None
-
 # Define the Gradio interface
 with gr.Blocks(css="""
     .vertical-slider input[type=range] {