feat: Add new API endpoints and HTML pages for ML model management

- Implemented HTML pages for datasets, models, training, testing, and results.
- Created API endpoints for managing repositories, results, tests, and training sessions.
- Added functionality for streaming training progress via Server-Sent Events (SSE).
- Introduced a Dockerfile for the ML runner with necessary dependencies.
- Developed an SDK for user code execution within the runner container.
- Enhanced CSS styles for improved UI layout and navigation.
- Established a layout template for consistent HTML structure across pages.
- Added JavaScript for dynamic interactions on the models page.
- Implemented WebSocket handling for real-time communication with kiosk devices and controllers.
- Implemented model registration and management API at /api/models
- Added Gitea proxy API for repository interactions at /api/repos
- Created results API for listing and comparing training results at /api/results
- Developed training management API for enqueueing and retrieving training jobs at /api/trainings
- Introduced SSE endpoint for live training progress updates
- Added HTML pages for models, datasets, and training management
- Created a Dockerfile for the ML runner with necessary dependencies
- Developed SDK for user code execution within the runner container
- Enhanced CSS styles for improved UI/UX
- Implemented WebSocket communication for real-time device and controller interactions in the kiosk system

ml/core/config.py

@@ -0,0 +1,64 @@
+"""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()