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_stream.py

@@ -0,0 +1,64 @@
+"""SSE endpoint per live progress del training.
+
+GET /api/trainings/{id}/events
+  Streamma eventi dal Redis stream `ml:train:{id}:events` via Server-Sent Events.
+  Termina quando lo stato del training è terminale (succeeded/failed/cancelled).
+"""
+from __future__ import annotations
+
+import asyncio
+import json
+import uuid
+
+from fastapi import APIRouter, Depends, HTTPException
+from sse_starlette.sse import EventSourceResponse
+
+from core import db, redis_client
+from core.auth import require_auth
+
+router = APIRouter(prefix="/api/trainings", tags=["trainings-sse"])
+
+_TERMINAL = {"succeeded", "failed", "cancelled"}
+
+
+@router.get("/{training_id}/events")
+async def training_events(training_id: str, user=Depends(require_auth)):
+    # verifica esistenza
+    row = await db.fetchrow("SELECT status FROM trainings WHERE id = $1", uuid.UUID(training_id))
+    if not row:
+        raise HTTPException(404, "not found")
+
+    stream_key = f"ml:train:{training_id}:events"
+    status_key = f"ml:train:{training_id}"
+
+    async def gen():
+        last_id = "0-0"
+        r = redis_client.client()
+        while True:
+            try:
+                # XREAD block 5s per non tenere la connessione idle troppo a lungo
+                resp = await r.xread({stream_key: last_id}, count=50, block=5000)
+            except Exception as e:
+                yield {"event": "error", "data": json.dumps({"error": str(e)})}
+                await asyncio.sleep(1)
+                continue
+
+            if resp:
+                for _stream, entries in resp:
+                    for entry_id, fields in entries:
+                        last_id = entry_id
+                        yield {"event": "message", "id": entry_id, "data": json.dumps(fields)}
+
+            # controlla stato terminale
+            state = await r.hget(status_key, "status")
+            if not state:
+                # fallback su db se redis scaduto
+                db_row = await db.fetchrow(
+                    "SELECT status FROM trainings WHERE id = $1", uuid.UUID(training_id)
+                )
+                state = db_row["status"] if db_row else "unknown"
+            if state in _TERMINAL:
+                yield {"event": "end", "data": json.dumps({"status": state})}
+                return
+
+    return EventSourceResponse(gen())