colors not supported so refactored

app.py

@@ -35,9 +35,9 @@ def predict_combined_models(image_np, model1, model2):
     results2 = model2(image_np)
     result2 = {key:value.numpy() for key,value in results2.items()}
 
-    # Define colors for bounding boxes for each model
-    color_final = (0, 255, 0)  # Green for model 1
-    color_initial = (255, 0, 0)  # Red for model 2
+    # Modify category index for each model
+    category_index_model1 = {k: {**v, 'name': v['name'] + ' - Model 1'} for k, v in category_index.items()}
+    category_index_model2 = {k: {**v, 'name': v['name'] + ' - Model 2'} for k, v in category_index.items()}
 
     # Visualization for model 1
     image_np_with_detections = image_np.copy()
@@ -46,26 +46,24 @@ def predict_combined_models(image_np, model1, model2):
         result1['detection_boxes'][0],
         (result1['detection_classes'][0]).astype(int),
         result1['detection_scores'][0],
-        category_index,
+        category_index_model1,
         use_normalized_coordinates=True,
         max_boxes_to_draw=200,
         min_score_thresh=.60,
         agnostic_mode=False,
-        colors=color_final,
         line_thickness=2)
 
-    # Visualization for model 2 (can adjust styles to differentiate)
+    # Visualization for model 2
     viz_utils.visualize_boxes_and_labels_on_image_array(
         image_np_with_detections[0],
         result2['detection_boxes'][0],
         (result2['detection_classes'][0]).astype(int),
         result2['detection_scores'][0],
-        category_index,
+        category_index_model2,
         use_normalized_coordinates=True,
         max_boxes_to_draw=200,
         min_score_thresh=.60,
         agnostic_mode=False,
-        colors=color_initial,
         line_thickness=2)
 
     # Combine and return final image