Update app.py

app.py

@@ -31,25 +31,6 @@ class PrintCapture(io.StringIO):
     def get_output(self):
         return ''.join(self.output)
 
-# 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")
-    embeddings_image_path = os.path.join(EMBEDDINGS_PATH, f"percentage_{percentage}_complexity_16.png")
-    
-    # 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
-
 # 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
@@ -161,7 +142,8 @@ def run_inference(uploaded_file):
         sys.stdout = sys.__stdout__  # Reset print statements
 
 # Function to handle classification after inference (using Gradio state)
-def los_nlos_classification(output_emb, output_raw, labels, percentage_idx):
+def los_nlos_classification(inference_output, percentage_idx):
+    output_emb, output_raw, labels = inference_output
     if output_emb is not None and output_raw is not None:
         train_data_emb, test_data_emb, train_data_raw, test_data_raw, train_labels, test_labels = identical_train_test_split(
             output_emb.view(len(output_emb), -1),
@@ -221,7 +203,7 @@ with gr.Blocks(css="""
         # Handle dropdown change for classification
         percentage_dropdown_los.change(
             fn=los_nlos_classification,
-            inputs=[inference_output['output_emb'], inference_output['output_raw'], inference_output['labels'], percentage_dropdown_los],
+            inputs=[inference_output, percentage_dropdown_los],
             outputs=[raw_img_los, embeddings_img_los, output_textbox]
         )