"""Configurazione centralizzata del servizio ML, letta da env."""
from __future__ import annotations

import os
from dataclasses import dataclass


def _b(name: str, default: bool = False) -> bool:
    return os.environ.get(name, str(default)).lower() in ("1", "true", "yes", "on")


@dataclass(frozen=True)
class Settings:
    # Postgres (db "ml")
    pg_host: str = os.environ.get("PG_HOST", "meb-postgres")
    pg_port: int = int(os.environ.get("PG_PORT", "5432"))
    pg_user: str = os.environ.get("DB_USER", "meb")
    pg_password: str = os.environ.get("DB_PASSWORD", "meb")
    pg_db: str = os.environ.get("ML_DB", "ml")

    # Redis
    redis_host: str = os.environ.get("REDIS_HOST", "meb-redis")
    redis_port: int = int(os.environ.get("REDIS_PORT", "6379"))

    # MinIO (bucket unico)
    minio_endpoint: str = os.environ.get("MINIO_ENDPOINT", "minio")
    minio_port: int = int(os.environ.get("MINIO_PORT", "9000"))
    minio_use_ssl: bool = _b("MINIO_USE_SSL", False)
    minio_access_key: str = os.environ.get("MINIO_ACCESS_KEY", "")
    minio_secret_key: str = os.environ.get("MINIO_SECRET_KEY", "")
    minio_bucket: str = os.environ.get("MINIO_BUCKET", "ml")

    # InfluxDB — accetta sia INFLUX_* che INFLX_* per allinearsi alle var già
    # usate dagli altri servizi (realtime, api) senza dover duplicare la config.
    influx_url: str = os.environ.get("INFLUX_URL") or os.environ.get("INFLX_URL", "http://meb-influx:8086")
    influx_token: str = os.environ.get("INFLUX_TOKEN") or os.environ.get("INFLX_TOKEN", "")
    influx_org: str = os.environ.get("INFLUX_ORG") or os.environ.get("INFLX_ORG", "meb")
    # Bucket dedicato alle metriche di training/test ML, separato dai logs e
    # dai dati meteo. Sovrascrivibile via INFLUX_BUCKET o ML_INFLUX_BUCKET.
    influx_bucket: str = os.environ.get("ML_INFLUX_BUCKET") or os.environ.get("INFLUX_BUCKET", "ml_metrics")

    # Gitea (installato esternamente)
    gitea_url: str = os.environ.get("GITEA_URL", "")
    gitea_token: str = os.environ.get("GITEA_TOKEN", "")

    # API service (per jobs/queue/pageconnections)
    api_url: str = os.environ.get("API_URL", "http://api:3003")
    internal_api_key: str = os.environ.get("INTERNAL_API_KEY", "")

    # Auth (condiviso)
    jwt_secret: str = os.environ.get("JWT_SECRET", "")
    auth_login_url: str = os.environ.get("AUTH_LOGIN_URL", "https://auth.mebboat.it/login")

    # Esecuzione training
    train_concurrency: int = int(os.environ.get("ML_TRAIN_CONCURRENCY", "1"))
    runner_image: str = os.environ.get("ML_RUNNER_IMAGE", "meb-ml-runner:latest")
    runner_tmp_dir: str = os.environ.get("ML_RUNNER_TMP", "/var/ml/tmp")
    gitcache_dir: str = os.environ.get("ML_GITCACHE_DIR", "/var/ml/gitcache")

    # Limiti runtime
    max_upload_mb: int = int(os.environ.get("ML_MAX_UPLOAD_MB", "500"))


settings = Settings()