Update preprocessing_router.py

preprocessing_router.py

@@ -314,29 +314,43 @@ async def finalize_casting(
 
     try:
         if face_identities and router_llm is not None:
+            print("Face identities:", face_identities)
             factory = router_llm.client_factories.get("salamandra-vision")  # type: ignore[attr-defined]
+            print("Factory:", factory)
+    
             if factory is not None:
                 vclient = factory()
+                print("VClient creado:", vclient)
                 gclient = getattr(vclient, "_client", None)
+                print("GClient obtenido:", gclient)
             else:
                 gclient = None
-
+                print("No se encontró la factory")
+    
             if gclient is not None:
                 for identity in face_identities:
+                    print("Procesando identidad:", identity)
                     id_dir = faces_out / identity
                     if not id_dir.is_dir():
+                        print(f"No existe directorio para {identity}")
                         continue
+    
                     img_path = None
                     for ext in (".jpg", ".jpeg", ".png", ".bmp", ".webp"):
                         candidates = list(id_dir.glob(f"*{ext}"))
                         if candidates:
                             img_path = candidates[0]
+                            print("Imagen encontrada:", img_path)
                             break
+    
                     if not img_path:
+                        print(f"No se encontró imagen para {identity}")
                         continue
-
+    
                     try:
                         out = gclient.predict(str(img_path), api_name="/face_image_embedding")
+                        print("Salida de predict:", out)
+    
                         emb = None
                         if isinstance(out, list):
                             if out and isinstance(out[0], (list, tuple, float, int)):
@@ -346,16 +360,20 @@ async def finalize_casting(
                                     emb = list(out)
                         elif isinstance(out, dict) and "embedding" in out:
                             emb = out.get("embedding")
-
+    
                         if not emb:
+                            print(f"No se pudo extraer embedding de {identity}")
                             continue
-
+    
+                        print(f"Embedding obtenido para {identity}: {emb[:5]}...")  # solo los primeros 5 valores
                         casting_json["face_col"].append({
                             "nombre": identity,
                             "embedding": emb,
                         })
-                    except Exception:
-                        continue
+    
+                    except Exception as e:
+                        print(f"Error al predecir {identity}: {e}")
+                    
     except Exception:
         casting_json["face_col"] = []