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/runner/sdk.py

@@ -0,0 +1,80 @@
+"""meb_ml — SDK importabile dal codice utente dentro il container runner.
+
+API:
+    from meb_ml import emit_metric, emit_series, emit_matrix, emit_log, save_artifact
+
+    emit_metric(iter=10, loss=0.23)
+    emit_series("roc_curve", x=fpr, y=tpr, kind="line")
+    emit_matrix("confusion", labels=[...], values=[[...],[...]])
+    emit_log("info", "epoch done")
+
+Scrive righe JSON su stdout; il parent (ml-service) le inoltra su Redis/Influx.
+Per risultati finali scrivere `out/metrics.json` con:
+    {"metrics": {...}, "plots": {"loss_curve": {"x": [...], "y": [...]}, ...}}
+"""
+from __future__ import annotations
+
+import json
+import os
+import sys
+from pathlib import Path
+from typing import Any, Iterable, Sequence
+
+
+def _print(obj: dict) -> None:
+    sys.stdout.write(json.dumps(obj, default=float) + "\n")
+    sys.stdout.flush()
+
+
+def emit_metric(**fields: Any) -> None:
+    _print({"type": "metric", **fields})
+
+
+def emit_series(name: str, x: Sequence, y: Sequence, kind: str = "line") -> None:
+    _print({
+        "type": "series",
+        "name": name,
+        "kind": kind,
+        "x": list(x),
+        "y": list(y),
+    })
+
+
+def emit_matrix(name: str, labels: Sequence, values: Sequence[Sequence]) -> None:
+    _print({
+        "type": "matrix",
+        "name": name,
+        "labels": list(labels),
+        "values": [list(row) for row in values],
+    })
+
+
+def emit_log(level: str, message: str) -> None:
+    _print({"type": "log", "level": level, "message": message})
+
+
+def save_artifact(path: str) -> str:
+    """Copia `path` nella cartella artefatti (MEB_ARTIFACTS_DIR). Ritorna la dest."""
+    dest_dir = Path(os.environ.get("MEB_ARTIFACTS_DIR", "/workdir/out"))
+    dest_dir.mkdir(parents=True, exist_ok=True)
+    src = Path(path)
+    dest = dest_dir / src.name
+    dest.write_bytes(src.read_bytes())
+    return str(dest)
+
+
+def dataset_path() -> str:
+    return os.environ["MEB_DATASET_PATH"]
+
+
+def artifacts_dir() -> str:
+    return os.environ.get("MEB_ARTIFACTS_DIR", "/workdir/out")
+
+
+def read_test_input() -> dict:
+    """Legge un singolo JSON da stdin (per script di test)."""
+    return json.loads(sys.stdin.readline())
+
+
+def write_test_output(outputs: dict) -> None:
+    _print({"type": "result", "outputs": outputs})