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/routers/trainings.py

@@ -0,0 +1,129 @@
+"""API /api/trainings — enqueue, list, get, artifacts."""
+from __future__ import annotations
+
+import json
+import uuid
+from typing import Optional
+
+from fastapi import APIRouter, Depends, HTTPException, Query
+
+from core import db, minio_client, redis_client, api_client
+from core.auth import require_auth
+
+router = APIRouter(prefix="/api/trainings", tags=["trainings"])
+
+
+def _row(r) -> Optional[dict]:
+    if r is None:
+        return None
+    d = dict(r)
+    for k in ("queued_at", "started_at", "finished_at"):
+        if d.get(k) is not None and hasattr(d[k], "isoformat"):
+            d[k] = d[k].isoformat()
+    return d
+
+
+@router.get("")
+async def list_trainings(
+    model_id: Optional[str] = Query(None),
+    status: Optional[str] = Query(None),
+    limit: int = Query(100, le=500),
+    user=Depends(require_auth),
+):
+    where = []
+    args: list = []
+    if model_id:
+        args.append(uuid.UUID(model_id))
+        where.append(f"model_id = ${len(args)}")
+    if status:
+        args.append(status)
+        where.append(f"status = ${len(args)}")
+    sql = "SELECT * FROM trainings"
+    if where:
+        sql += " WHERE " + " AND ".join(where)
+    args.append(limit)
+    sql += f" ORDER BY queued_at DESC LIMIT ${len(args)}"
+    rows = await db.fetch(sql, *args)
+    return {"count": len(rows), "trainings": [_row(r) for r in rows]}
+
+
+@router.post("", status_code=202)
+async def enqueue_training(body: dict, user=Depends(require_auth)):
+    for k in ("model_id", "version", "patch", "dataset_id"):
+        if not body.get(k):
+            raise HTTPException(400, f"missing field: {k}")
+
+    model_row = await db.fetchrow("SELECT * FROM models WHERE id = $1", uuid.UUID(body["model_id"]))
+    if not model_row:
+        raise HTTPException(404, "model not found")
+
+    ds_row = await db.fetchrow("SELECT id FROM datasets WHERE id = $1", uuid.UUID(body["dataset_id"]))
+    if not ds_row:
+        raise HTTPException(404, "dataset not found")
+
+    try:
+        training_row = await db.fetchrow(
+            """
+            INSERT INTO trainings (model_id, version, patch, dataset_id, started_by, status)
+            VALUES ($1,$2,$3,$4,$5,'queued')
+            RETURNING *
+            """,
+            uuid.UUID(body["model_id"]),
+            body["version"],
+            body["patch"],
+            uuid.UUID(body["dataset_id"]),
+            user.get("username") or "unknown",
+        )
+    except Exception as e:
+        raise HTTPException(409, f"training already exists or invalid: {e}")
+
+    training_id = str(training_row["id"])
+
+    # crea job lato api-service (cross-service registry)
+    try:
+        await api_client.create_job(
+            "train",
+            created_by=user.get("username") or "unknown",
+            payload={
+                "training_id": training_id,
+                "model_id": body["model_id"],
+                "version": body["version"],
+                "patch": body["patch"],
+                "dataset_id": body["dataset_id"],
+            },
+        )
+    except Exception as e:
+        # non-fatale: il worker locale può comunque procedere; logghiamo e continuiamo
+        import logging
+        logging.warning("create_job failed: %s", e)
+
+    # enqueue in Redis (il worker locale lo raccoglie)
+    await redis_client.client().lpush("ml:queue:train", training_id)
+    await redis_client.client().hset(
+        f"ml:train:{training_id}",
+        mapping={"status": "queued", "progress": "0", "message": "queued"},
+    )
+    await redis_client.client().expire(f"ml:train:{training_id}", 48 * 3600)
+
+    return _row(training_row)
+
+
+@router.get("/{training_id}")
+async def get_training(training_id: str, user=Depends(require_auth)):
+    row = await db.fetchrow("SELECT * FROM trainings WHERE id = $1", uuid.UUID(training_id))
+    if not row:
+        raise HTTPException(404, "not found")
+    return _row(row)
+
+
+@router.get("/{training_id}/artifacts")
+async def list_artifacts(training_id: str, user=Depends(require_auth)):
+    row = await db.fetchrow(
+        "SELECT artifacts_prefix FROM trainings WHERE id = $1", uuid.UUID(training_id)
+    )
+    if not row or not row["artifacts_prefix"]:
+        raise HTTPException(404, "no artifacts")
+    objs = minio_client.list_prefix(row["artifacts_prefix"] + "/")
+    for o in objs:
+        o["url"] = minio_client.presigned_get(o["name"], 3600)
+    return objs