Windline

Система почасового прогноза выработки двух ветротурбин на 48 часов на LightGBM с агентом, который проверяет доступность погодных данных, решает о…

Доведённый продукт

HackAlem ML Дашборды Оптимизация Энергетика

GitHub

Команда «TISHAB» 7💬 0 комментариевс 23 сентября 2026 г.

О проекте

Команда: TISHAB
Кейс HackAlem.ai: Энергетика — Самрук-Казына
Стадия: Доведённый продукт

Задача кейса: AI-агент, прогнозирующий выработку ветровой электростанции по погодным данным

Суть

Система почасового прогноза выработки двух ветротурбин на 48 часов на LightGBM с агентом, который проверяет доступность погодных данных, решает о публикации прогноза и объясняет результат оператору.

Как устроено

SCADA-данные турбин и сохранённые запуски ECMWF IFS из Open-Meteo Single Runs → признаки → LightGBM для T1/T2 (при отсутствии свежей SCADA — модель weather_only). Детерминированный агент выбирает погодный запуск с учётом времени доступности (vintage-aware, без утечки будущего), проверяет физические границы и cutoff обучения, принимает решение PUBLISH/SHADOW/REJECT и сравнивает с прошлой публикацией (порог 10%). FastAPI + SQLite хранят результаты, React-интерфейс показывает графики, паспорт прогноза и журнал агента; LLM-сводка опциональна, с проверкой чисел и шаблонным fallback.

Ключевые решения

LightGBM
power curve baseline
persistence baseline
vintage-aware выбор погодного прогноза
Open-Meteo ECMWF IFS
политика публикации PUBLISH/SHADOW/REJECT
проверка чисел в LLM-ответе

Особенности

Акцент на vintage-aware бэктесте: агент гарантирует, что при историческом воспроизведении не используются погода и данные, ставшие доступными позже момента выпуска; честно помечено, что сохранённые MAE/RMSE относятся к baseline, а не к LightGBM.

Стек

Python · FastAPI · Pydantic · SQLAlchemy · SQLite · React · Vite · Recharts · LightGBM · scikit-learn · pandas · pytest

Модели

OpenAI (модель через OPENAI_MODEL, опционально)

Проект создан за 5 часов на финале хакатона HackAlem.ai (23 сентября 2026, Астана). Код — BAITC-Hacks/hack-a6d47084-tishab.

README

README.md

Windline — Agentic AI for Vintage-Aware Wind Power Forecasting

Почасовой прогноз выработки двух ветровых турбин с проверкой доступности исходных данных, историей решений и объяснением результата для оператора.

Текущее состояние: веб-интерфейс соединён с ML/агентской частью из feature/shihab. Режим ML + агент использует обученную LightGBM и сохранённые погодные прогнозы; демо-сценарии остаются отдельным режимом. LLM-сводка опциональна и по умолчанию выключена. Ниже описано то, что есть в репозитории, а не планируемые возможности.

1. Проблема и аудитория

Операторам ВЭС и аналитикам энергосистемы нужно понимать ожидаемую выработку и происхождение прогноза. При исторической проверке нельзя использовать погоду или обучающую выборку, которые стали доступны позже момента выпуска.

Vintage-aware в этом проекте означает выбор погодного запуска с проверкой времени доступности и контроль границы обучения модели. Windline показывает численный прогноз вместе с источниками, версиями и решением о публикации.

Это хакатонный прототип исторического воспроизведения, а не промышленная система управления ВЭС.

2. Что реализовано

ML-прогноз: готовые артефакты LightGBM для T1/T2; почасовые значения нормализованной активной мощности в диапазоне [0, 1].
Февральский fallback: для дат после границы доступной SCADA скрипт автоматически выбирает модель lightgbm_weather_only, использующую погодные и календарные признаки.
Погода: клиент Open-Meteo Single Runs / ECMWF IFS, локальный кэш запусков и подготовленный Parquet.
Детерминированный агент: проверка погоды, горизонта, физических границ прогноза, срока обучения и отсутствия будущих данных; решения PUBLISH, SHADOW, REJECT.
Пересмотр: сравнение с последней публикацией на общих целевых часах. Порог изменения суммарных нормализованных значений — 10%; это не измерение энергии в МВт·ч.
Сводка: русский шаблон; при отдельном включении — запрос к LLM с проверкой чисел и fallback на шаблон.
Интерфейс: запуск расчёта, графики, переключение T1/T2, паспорт прогноза, погодный контекст, журнал агента, история и версии.
Метрики: страница проверки показывает сохранённые MAE/RMSE для power curve и persistence за январь 2026 по турбинам и группам горизонта. Это не метрики LightGBM и не итоговая метрика организаторов.
Оформление: тёмная/светлая темы, қазақша/русский/English с сохранением выбора, адаптивная вёрстка, SVG-турбины и анимация ветра. Учитывается системное уменьшение движения.
Демо-режим: шесть синтетических сценариев, включая намеренную блокировку будущей погоды и недоступность агента.

Чего не следует ожидать: реальная ML-часть не возвращает P10/P90. В API эти поля равны null, на графике показывается только точечный прогноз. Демонстрационные интервалы доступны исключительно в демо-режиме. Три турбины и подписи состояния в декоративной сцене не являются телеметрией реальной ВЭС.

3. Как работает решение

Пользователь выбирает ML + агент и время выпуска в UTC, кратное целому часу.
FastAPI передаёт запрос интеграционному сервису; реальный веб-режим использует горизонт 48 часов.
Агент выбирает доступный погодный запуск из локального кэша. Отсутствующие запуски пропускаются; если подходящего нет, результат отклоняется.
Адаптер M1 вызывает scripts/predict.py с той же схемой, что в handoff Shihab. Выбор артефакта и проверка cutoff относятся к фактически используемой модели.
LightGBM выдаёт 96 точек: 48 часов × 2 турбины. Агент валидирует результат и решает, публиковать его, сохранить без публикации или отклонить.
Создаются receipt и журнал, затем результат сохраняется в SQLite и отображается в интерфейсе.
При выключенной/недоступной LLM численный прогноз сохраняется, а объяснение формируется шаблоном.

Повторный запуск с теми же параметрами переиспользует сохранённый результат. Переключение языка и темы не пересчитывает прогноз.

4. Технологии

Часть	Реализация
Frontend	JavaScript, React, Vite, React Router, Recharts, CSS, SVG
Backend	Python, FastAPI, Pydantic, SQLAlchemy, SQLite, Uvicorn
ML и данные	LightGBM, scikit-learn, pandas, NumPy, joblib, PyArrow
Погодная интеграция	requests; Open-Meteo Single Runs API, модель ECMWF IFS
Агент	Python: проверки, политика публикации, Parquet/JSON/JSONL
LLM	OpenAI Python SDK; модель задаётся через `OPENAI_MODEL`, конкретная модель не зашита
Проверки	pytest; Node.js test runner для локализации

Зависимости AI и backend устанавливаются через корневой requirements.txt, который включает backend/requirements.txt. Frontend имеет pnpm-lock.yaml; в package.json версии зависимостей указаны как latest, поэтому установка npm без lockfile менее воспроизводима.

5. Архитектура

React → POST /api/forecasts/run
              │
              ├─ mode=mock → синтетические провайдеры
              │
              └─ mode=integrated → IntegratedForecastService
                                      │
                                      └─ ai.agent.run.run_issue
                                          ├─ ai.weather → локальный кэш
                                          ├─ M1Predictor → scripts/predict.py → LightGBM
                                          ├─ проверки + политика публикации
                                          └─ шаблон / опциональная LLM-сводка
              │
              └─ SQLite → графики, паспорт, история, журнал

Каталог	Назначение
`frontend/src/`	Страницы, графики, сцена ВЭС, темы и словари языков
`backend/app/`	API, контракты, SQLite и адаптер M1/M2 → web
`m1/`, `scripts/`	SCADA, признаки, обучение, оценка и прогноз LightGBM
`ai/weather/`	Погодный кэш, выбор vintage и проверка доступности
`ai/agent/`	Валидация, политика, receipt, журнал и сводка
`models/`	Готовые LightGBM-артефакты и power curve
`data/processed/`	Подготовленные данные и сохранённая оценка power curve
`data/cache/`	Погодные ответы; часть кэша уже включена в ветку Shihab
`outputs/web/off/`, `outputs/web/on/`	Локальные результаты агента отдельно для режимов LLM
`predictions/`	Результаты CLI; не добавляются в Git

Основные API:

Метод	Путь	Результат
GET	`/api/health`	Доступность API и legacy-настройки провайдеров
POST	`/api/forecasts/run`	Реальный или демонстрационный расчёт
GET	`/api/forecasts`	История
GET	`/api/forecasts/{id}`	Полный результат
GET	`/api/forecasts/{id}/weather`	Погода и knowledge boundary
GET	`/api/forecasts/{id}/agent`, `/events`, `/lineage`	Агент, журнал, версии
GET	`/api/backtests/latest`	Сохранённые результаты оценки

mode=integrated явно выбирает связанный M1/M2 runtime, независимо от legacy-флагов *_PROVIDER. Без mode используются настройки провайдеров; все три real также выбирают интеграционный сервис. Старые отдельные Real*-адаптеры для смешанных конфигураций остаются заглушками. Для жюри используйте явный integrated или mock.

6. Установка и запуск

Нужны Git, Python 3.12 и Node.js, совместимый с Vite 8: 20.19+ либо 22.12+. Рекомендуется отдельное виртуальное окружение. API-ключ не нужен для стандартного офлайн-запуска.

Клонируйте репозиторий и используйте версию с объединённой интеграцией:

git clone https://github.com/BAITC-Hacks/hack-a6d47084-tishab.git
cd hack-a6d47084-tishab

Если интеграционная ветка ещё не опубликована, обычный clone главной ветки не содержит локальных изменений. Не считайте их доступными на GitHub до отдельного push.

Windows / PowerShell

Из корня проекта:

py -3.12 -m venv .venv
.\.venv\Scripts\python.exe -m pip install -r requirements.txt
if (-not (Test-Path backend/.env)) { Copy-Item backend/.env.example backend/.env }
cd backend
..\.venv\Scripts\python.exe -m uvicorn app.main:app --host 127.0.0.1 --port 8000 --reload

В другом терминале, из корня:

cd frontend
npm install
npm run dev -- --port 5173 --strictPort

Если pnpm уже установлен, для версий из lockfile используйте pnpm install --frozen-lockfile и pnpm run dev -- --port 5173 --strictPort. Файла package-lock.json нет, поэтому npm ci для чистого clone не подходит.

Linux / macOS

python3 -m venv .venv
.venv/bin/python -m pip install -r requirements.txt
test -f backend/.env || cp backend/.env.example backend/.env
cd backend
../.venv/bin/python -m uvicorn app.main:app --host 127.0.0.1 --port 8000 --reload

Frontend запускается теми же npm-командами в другом терминале. Если LightGBM сообщает об отсутствии OpenMP, требуется системная библиотека OpenMP для вашей ОС.

Адреса:

Сайт: http://localhost:5173.
Swagger: http://127.0.0.1:8000/docs.
Проверка API: http://127.0.0.1:8000/api/health.

Frontend использует VITE_API_URL из frontend/.env, по умолчанию http://localhost:8000. При смене адреса перезапустите Vite. Для другого origin измените CORS_ORIGINS в backend/.env и перезапустите API.

Опциональная LLM-сводка

По умолчанию INTEGRATED_LLM=off: внешнего LLM-запроса нет.

Для включения создайте локальный корневой .env с OPENAI_API_KEY и OPENAI_MODEL. Опционален OPENAI_BASE_URL. В backend/.env задайте INTEGRATED_LLM=on и перезапустите backend. Ключи не коммитьте.

При включении факты о прогнозе отправляются настроенному LLM-провайдеру; запрос может быть платным. При отсутствии ключа/модели, ошибке сети или провале проверки ответа используется шаблон. Флаг llm_used и причина fallback отображаются отдельно. Наличие SDK не означает, что LLM реально использовалась.

7. Проверка для жюри

Основной сценарий через сайт

Откройте сайт и выберите язык.
В режиме ML + агент оставьте 2026-02-10 07:00 UTC и 48 часов.
Нажмите Запустить прогноз. Первый расчёт может занять несколько секунд.
Проверьте T1/T2, 96 точек через API, модель m1-lgbm-weather-only-v1, погодный запуск и границу обучения.
P10/P90 отсутствуют; это не ошибка. В офлайн-конфигурации отображается шаблонная сводка.
Откройте историю и сохранённый прогноз; переключите тему и язык.

В отдельном чистом хранилище первый допустимый прогноз публикуется. При наличии предыдущих публикаций возможен SHADOW — сохранение без публикации по политике агента.

Точная команда из handoff Shihab

Исходный handoff ссылается на commit b24991ca8e0f8f41a11e8d7e8ba04c99bd45617f ветки origin/feature/shihab.

Из корня, в активированном Python-окружении:

python scripts/predict.py --weather data/processed/weather_vintages.parquet --issue-time 2026-02-10T07:00:00Z --output predictions/forecast.csv

На Windows без активации замените python на .\.venv\Scripts\python.exe. На Linux/macOS — на .venv/bin/python.

Команда проверена: 96 строк, T1/T2, 48 часов. Интеграционный тест сравнивает численные значения этого CLI с API.

API, PowerShell

$body = @{
    mode = "integrated"
    issue_time = "2026-02-10T07:00:00Z"
    horizon_hours = 48
    scenario = "normal"
} | ConvertTo-Json
Invoke-RestMethod http://127.0.0.1:8000/api/forecasts/run -Method Post -ContentType application/json -Body $body

Проверка защиты

В UI выберите Демо-сценарии → D · Блокировка утечки и запустите расчёт. BLOCKED ожидаем: погода специально помечена как недоступная на момент выпуска.
Это не требует «починки» модели или отключения защиты. Для обычного результата вернитесь в ML + агент и запустите новый расчёт.
Реальный агент тоже проверяет доступность. В тестах отдельно проверяется, что будущая погода блокируется до вызова модели.
Даты без подходящей погоды в кэше или до границы обучения дают REJECT без выдуманных точек.

Автоматические проверки

Из корня:

.\.venv\Scripts\python.exe -m pytest -q

Из frontend:

npm test
npm run build

Python-тесты охватывают M1, API, точное соответствие handoff, сохранение, повторный запуск, блокировки и LLM fallback без реального внешнего запроса. Frontend-тесты проверяют локализацию. Полного браузерного автотестового набора нет.

8. Данные и интеграции

SCADA T1/T2: исходные CSV с 10-минутными измерениями. M1 считает часовые средние; неполные часы не интерполируются. В признаках используются наблюдения не позже часа перед выпуском.
Часовой пояс: в объединённой AI-части принят фиксированный UTC+5 для SCADA; обоснование автора — data/processed/tz_report.json. Время выпуска реального API нормализуется в UTC.
Погода: Open-Meteo Single Runs, ECMWF IFS. Веб-режим использует только локальный кэш, без автоматического скачивания. CLI-модули умеют запрашивать API.
Доступность погоды: available_at = run_init_time + 7 часов — допущение автора, а не подтверждённое время публикации. Оно записывается в паспорт как assumed:init+7h.
Модели: готовые models/lightgbm/, models/lightgbm_weather/, models/lightgbm_weather_only/; также models/power_curve_v1.json. В веб-режиме подключён M1/LightGBM, power curve применяется как проверка физической согласованности.
Оценка: data/processed/power_curve_eval.json содержит сохранённую январскую проверку. Новая оценка LightGBM при открытии сайта не выполняется.
LLM: отправляются сформированные агентом факты. Численный прогноз и решение принимаются до вызова LLM.
Хранение: SQLite для web; Parquet/JSON/JSONL для агента. По умолчанию БД создаётся в рабочем каталоге backend.

9. Ограничения текущей версии

Реальный веб-режим: только 48 часов и нормальный сценарий. Демонстрационный режим допускает 24/48 часов.
Нет реальных квантильных интервалов, промышленного ансамбля или подключённого TFT. Их названия в mock-сценариях не означают наличие обученных моделей.
Выработка нормализована; нет перевода в МВт, тарифа, физической суммарной мощности станции или подтверждённой экономии.
Это исторический прогноз по сохранённым данным, а не непрерывный онлайн-мониторинг.
Нет подтверждения качества LightGBM на закрытой тестовой выборке организаторов.
LLM-сводка и её шаблон формируются по-русски даже при другом языке UI. Проверка ответа контролирует числа и длину, но не гарантирует полную смысловую корректность.
SQLite и файловое хранилище агента рассчитаны на локальный прототип. Используйте один API worker; нет межпроцессной блокировки, очереди, аутентификации и production-миграций.
Реальные результаты переиспользуются из хранилища; для независимого replay задайте отдельный INTEGRATED_OUTPUT_DIR. Не удаляйте рабочие результаты ради повторного запуска.
Старые отдельные Real*-провайдеры не реализуют произвольные смешанные конфигурации. Используйте связанный режим.
Время доступности погоды и интерпретация SCADA содержат перечисленные выше допущения.
График Actual vs Forecast и nMAE не заполнены, если соответствующих данных нет.
Нативный календарь может использовать язык браузера. Неизвестные тексты провайдера и технические ID не переводятся.

10. Частые проблемы

Симптом	Причина / действие
`Failed to fetch`	Проверьте запуск API, `VITE_API_URL`, CORS и занятость порта
`ML dependencies unavailable`	Установите корневой `requirements.txt` в то окружение, которым запущен Uvicorn
`BLOCKED / REJECT`	Посмотрите режим запуска и причину в карточке; демо D отклоняется намеренно
`no_legal_weather`	Для выбранного выпуска нет пригодного кэшированного погодного запуска; используйте дату handoff
`model_cutoff_after_issue`	Модель обучалась на более поздних данных; такой исторический прогноз публиковать нельзя
`SHADOW`	Изменение относительно прошлой публикации не достигло порога; это штатное решение
Шаблон вместо LLM	Проверьте `INTEGRATED_LLM`, наличие ключа/модели и `briefing_reason`
В Vite предупреждение о bundle >500 kB	Сборка успешна; оптимизация разделения кода пока не выполнена

11. Развёрнутая версия

Подтверждённой публичной deployed-ссылки в репозитории нет. Проверенный сценарий — локальный запуск по инструкции выше.

backend/Dockerfile относится к прежней облегчённой backend-сборке: без дополнительных каталогов и зависимостей он не упаковывает ML-runtime. Для объединённой версии используйте описанное Python-окружение. Frontend собирается в frontend/dist/; при самостоятельном хостинге нужны адрес API и fallback маршрутов SPA на index.html.

GitHub

BAITC-Hacks/hack-a6d47084-tishab

Звёзды

⭐ 0

Форки

Язык

Python

Последний push

в прошлом месяце

Коммиты

Авторы

Файлы

1504

Ветки

Авторы коммитов

Timur Ishmurzin 9ahsan-h-shihab 4ssabyl 3

История коммитов

2026-09-23 17:57last repoTimur Ishmurzin
2026-09-23 17:53Merge branch 'codex/integrate-shihab'Timur Ishmurzin
2026-09-23 17:52chore: keep local backend frontend analysis out of version controlTimur Ishmurzin
2026-09-23 17:49last repostTimur Ishmurzin
2026-09-23 17:48prelast commitTimur Ishmurzin
2026-09-23 17:32feat: preserve Windline UI themes and localization before AI integrationTimur Ishmurzin
2026-09-23 17:30Add M3 integration handoffahsan-h-shihab
2026-09-23 17:26Integrate M2 weather and agent runtimeahsan-h-shihab

Все коммиты (16)

2026-09-23 17:16Add weather-only February forecast fallbackahsan-h-shihab
2026-09-23 16:33Complete M1 integration-ready forecasting pipelineahsan-h-shihab
2026-09-23 16:24docs: add clean machine setup guideTimur Ishmurzin
2026-09-23 15:58feat: add forecast web integration shellTimur Ishmurzin
2026-09-23 15:31Revert "docs: add data contracts, open questions and team architecture specs" and "docs: add task spec, data usage policy and external sources section"ssabyl
2026-09-23 15:29docs: add data contracts, open questions and team architecture specsssabyl
2026-09-23 14:44docs: add task spec, data usage policy and external sources sectionssabyl
2026-09-23 14:04docs: add project skeleton and Russian READMETimur Ishmurzin

Структура репозитория (1504 файлов)

.env.example
.gitignore
Dataset HackAlemAI для участников 11.03.2023-28.02.2026 - turbine 1.csv
Dataset HackAlemAI для участников 11.03.2023-28.02.2026 - turbine 2.csv
M1_AUDIT.md
M1_INTEGRATION.md
M2_AGENT.md
M3_HANDOFF.md
PROJECT_OVERVIEW(1).md
PROJECT_OVERVIEW.md
README.md
ai/README.md
ai/__init__.py
ai/agent/__init__.py
ai/agent/llm.py
ai/agent/policy.py
ai/agent/predictors.py
ai/agent/provenance.py
ai/agent/run.py
ai/config.py
ai/power_curve.py
ai/scada/__init__.py
ai/scada/build.py
ai/weather/__init__.py
ai/weather/build.py
ai/weather/client.py
ai/weather/tz_forensics.py
ai/weather/vintage.py
backend/.env.example
backend/Dockerfile
backend/README.md
backend/app/__init__.py
backend/app/api/__init__.py
backend/app/api/backtests.py
backend/app/api/forecasts.py
backend/app/api/health.py
backend/app/config.py
backend/app/db/__init__.py
backend/app/db/database.py
backend/app/db/models.py
backend/app/db/repository.py
backend/app/dependencies.py
backend/app/integrations/__init__.py
backend/app/integrations/agent/__init__.py
backend/app/integrations/agent/base.py
backend/app/integrations/agent/mock.py
backend/app/integrations/agent/real.py
backend/app/integrations/errors.py
backend/app/integrations/factory.py
backend/app/integrations/forecast/__init__.py
backend/app/integrations/forecast/base.py
backend/app/integrations/forecast/mock.py
backend/app/integrations/forecast/real.py
backend/app/integrations/weather/__init__.py
backend/app/integrations/weather/base.py
backend/app/integrations/weather/mock.py
backend/app/integrations/weather/real.py
backend/app/main.py
backend/app/schemas/__init__.py
backend/app/schemas/agent.py
backend/app/schemas/backtest.py
backend/app/schemas/common.py
backend/app/schemas/forecast.py
backend/app/schemas/health.py
backend/app/schemas/weather.py
backend/app/services/__init__.py
backend/app/services/backtest_service.py
backend/app/services/forecast_service.py
backend/app/services/integrated_forecast_service.py
backend/requirements.txt
backend/tests/conftest.py
backend/tests/test_api.py
backend/tests/test_integrated.py
data/Dataset HackAlemAI для участников 11.03.2023-28.02.2026 - turbine 1.csv
data/Dataset HackAlemAI для участников 11.03.2023-28.02.2026 - turbine 2.csv
data/README.md
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data/processed/power_curve_eval.json
data/processed/scada_hourly.parquet
data/processed/tz_report.json
data/processed/weather_vintages.parquet
data/processed/weather_vintages_provenance.jsonl
frontend/.env.example
frontend/README.md
frontend/index.html
frontend/package.json
frontend/pnpm-lock.yaml
frontend/src/App.jsx
frontend/src/components/AgentTimeline.jsx
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frontend/src/components/KnowledgeBoundary.jsx
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frontend/src/components/StatusBadge.jsx
frontend/src/components/WeatherPanel.jsx
frontend/src/components/WindFarmScene.jsx
frontend/src/hooks/useAgentEvents.js
frontend/src/hooks/useForecast.js
frontend/src/i18n/LanguageProvider.jsx
frontend/src/i18n/messages.js
frontend/src/i18n/messages.test.js
frontend/src/main.jsx
frontend/src/pages/Backtest.jsx
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frontend/src/pages/ForecastDetails.jsx
frontend/src/pages/History.jsx
frontend/src/services/api.js
frontend/src/styles.css
frontend/vite.config.js
m1/__init__.py
m1/evaluation.py
m1/features.py
m1/forecasting.py
m1/scada.py
m1/weather.py
models/lightgbm/metadata.json
models/lightgbm/model.joblib
models/lightgbm_weather/metadata.json
models/lightgbm_weather/model.joblib
models/lightgbm_weather_only/metadata.json
models/lightgbm_weather_only/model.joblib
models/power_curve_v1.json
predictions/forecast.csv
pytest.ini
requirements.txt
scripts/evaluate.py
scripts/predict.py
scripts/train.py
scripts/train_weather_only.py
tests/test_m1.py

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