ivideon: add face_report generated diagram

ivideon/puml/faces/report_creation.puml

@@ -0,0 +1,96 @@
+@startuml
+  title Создание отчёта о рабочем времени (face_work_time_json)
+
+  actor Client
+  participant "face/api" as API
+  database "MongoDB\nuser_reports" as ReportsDB
+  participant "report_checker\n(Worker)" as Checker
+  participant "reporter.py" as Reporter
+  participant "WorkTimeJsonReport" as WorkTime
+  participant "data_loader" as DataLoader
+  database "MongoDB\nface_events" as EventsDB
+  database "MongoDB\nfaces" as FacesDB
+  participant "tools.py" as Tools
+  database "Redis" as Redis
+
+  == 1. Создание задачи на отчёт ==
+  Client -> API: POST /reports\n(type=face_work_time_json)
+  API -> API: FaceReport.create()\nvalidate options, build query
+  API -> ReportsDB: insert(status='in_queue')
+  API --> Client: {success: true, id: report_id}
+
+  == 2. Обработка отчёта воркером ==
+  loop каждые SLEEP_INTERVAL секунд
+      Checker -> ReportsDB: find_one_and_update\n(status='in_queue' → 'in_progress')
+  end
+
+  Checker -> Checker: ProcessPoolExecutor\ncreate_report_sync(report)
+  note right: Новый процесс\nс asyncio.run()
+
+  Checker -> Reporter: create_report(report)
+  Reporter -> Reporter: @with_context\nустановить контекст пользователя
+  Reporter -> Reporter: Выбрать класс из REGISTRY\n(face_work_time_json → WorkTimeJsonReport)
+  Reporter -> WorkTime: make_report()
+
+  == 3. Загрузка данных ==
+  WorkTime -> DataLoader: get_events(query)
+  DataLoader -> EventsDB: find(query)\n[best_shot_time, face_id, camera_id]
+  EventsDB --> DataLoader: events[]
+  DataLoader --> WorkTime: events
+
+  WorkTime -> DataLoader: get_faces(galleries, events,\ncameras_in, cameras_out, tz)
+
+  loop для каждого события
+      DataLoader -> DataLoader: result[face_id] = GroupedFace(face_id)
+      DataLoader -> DataLoader: group.handle(best_shot_time, direction)
+  end
+
+  DataLoader -> DataLoader: _get_faces_without_events()\n(лица из галерей без событий)
+  DataLoader -> FacesDB: find(_id NOT IN face_ids,\ngallery IN galleries)
+  FacesDB --> DataLoader: faces_without_events
+
+  DataLoader -> DataLoader: face_ids = events + faces_without_events
+
+  == 4. Обогащение данных фотографиями ==
+  DataLoader -> DataLoader: _get_faces_data(face_ids)
+  DataLoader -> FacesDB: find(_id IN face_ids)\n[person, photos, description, gallery_id]
+  FacesDB --> DataLoader: faces_data[]
+
+  loop для каждого face
+      DataLoader -> DataLoader: _get_photo(face_data)
+      alt photos пустой или None
+          DataLoader --> DataLoader: return None
+          note right #pink: **ПРИЧИНА 1**\nphotos отсутствует
+      else photos есть
+          DataLoader -> DataLoader: найти default фото\nили первое из списка
+          DataLoader -> Tools: get_thumb_url(photo)
+          alt есть thumbnails.thumbnail_200.url
+              Tools --> DataLoader: url
+          else есть thumbnails.thumbnail_200.obj_ref
+              Tools -> Tools: storage.generate_presigned_url()
+              Tools --> DataLoader: presigned_url
+          else нет thumbnail_200
+              Tools --> DataLoader: None
+              note right #pink: **ПРИЧИНА 2**\nнет thumbnail_200
+          end
+      end
+      DataLoader -> DataLoader: face.photo = photo_url
+  end
+
+  DataLoader --> WorkTime: List[GroupedFace]
+
+  == 5. Генерация и сохранение ==
+  WorkTime -> WorkTime: _generate(query, faces, tz, schedule)
+  note right: Формирует JSON:\n[name, photo, face_id, ...]
+
+  WorkTime -> Redis: setex(key, json, TTL=1 day)
+  WorkTime -> ReportsDB: update(status='done',\n_redis_key=key)
+
+  == 6. Получение результата ==
+  Client -> API: GET /reports/{id}/json
+  API -> ReportsDB: find(_id=id)
+  API -> Redis: get(_redis_key)
+  Redis --> API: json_data
+  API --> Client: {summary: {...}, details: {...}}
+
+  @enduml