Upload 2 files

api.py

@@ -114,9 +114,9 @@ async def process_video(
 async def create_initial_casting(
     background_tasks: BackgroundTasks,
     video: UploadFile = File(...),
-    max_groups: int = Form(default=5),
+    max_groups: int = Form(default=3),
     min_cluster_size: int = Form(default=3),
-    voice_max_groups: int = Form(default=5),
+    voice_max_groups: int = Form(default=3),
     voice_min_cluster_size: int = Form(default=3),
     max_frames: int = Form(default=100),
 ):
@@ -726,7 +726,7 @@ def serve_scene_file(video_name: str, scene_id: str, filename: str):
 @app.post("/detect_scenes")
 async def detect_scenes(
     video: UploadFile = File(...),
-    max_groups: int = Form(default=5),
+    max_groups: int = Form(default=3),
     min_cluster_size: int = Form(default=3),
     frame_interval_sec: float = Form(default=0.5),
 ):

character_detection.py

@@ -303,7 +303,7 @@ class CharacterDetector:
         
         return analysis_path
     
-    def detect_characters(self, max_groups: int = 5, min_cluster_size: int = 3,
+    def detect_characters(self, max_groups: int = 3, min_cluster_size: int = 3,
                           *, start_offset_sec: float = 3.0, extract_every_sec: float = 0.5) -> Tuple[List[Dict], Path, np.ndarray, List[Dict[str, Any]]]:
         """
         Pipeline completo de detección de personajes con clustering jerárquico.
@@ -338,7 +338,7 @@ class CharacterDetector:
 
 # Función de conveniencia para usar en el API
 def detect_characters_from_video(video_path: str, output_base: str, 
-                                 max_groups: int = 5, min_cluster_size: int = 3,
+                                 max_groups: int = 3, min_cluster_size: int = 3,
                                  video_name: str = None,
                                  *, start_offset_sec: float = 3.0, extract_every_sec: float = 0.5) -> Dict[str, Any]:
     """