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

api/src/storage/influx.js

@@ -60,7 +60,9 @@ async function query(bucket, relativeTime, measurement, sensor, field) {
 
 }
 
-const sessionBucket = 'boat';
+// Sorgente di verità per i logs di sessione: stesso bucket usato da
+// realtime/store/influx.js. Sovrascrivibile via env per ambiente.
+const sessionBucket = process.env.INFLX_BUCKET_LOGS || process.env.INFLX_BUCKET || 'logs';
 
 /**
  * Query storica per una sessione di registrazione.