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/main.py

@@ -1,19 +1,90 @@
-from fastapi import FastAPI, Request, Response, Header
-from fastapi.responses import HTMLResponse, JSONResponse
-import time
+"""ml-service — FastAPI entrypoint.
+
+Monta:
+  /                 → RedirectResponse
+  /datasets /models /train /test /results    → pagine Jinja
+  /api/datasets /api/models /api/repos /api/trainings /api/tests /api/results → JSON
+  /api/trainings/{id}/events → SSE
+  /health           → check
+  /static/*         → file statici
+"""
+from __future__ import annotations
+
+import logging
+from contextlib import asynccontextmanager
+from pathlib import Path
+
+from fastapi import FastAPI
+from fastapi.staticfiles import StaticFiles
+
+from core import db, minio_client, redis_client, worker
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+log = logging.getLogger(__name__)
+
+STATIC_DIR = Path(__file__).resolve().parent / "static"
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    log.info("ml-service starting")
+    await db.init_pool()
+    try:
+        minio_client.ensure_bucket()
+    except Exception as e:
+        log.warning("minio bucket ensure failed: %s", e)
+    worker.start_workers()
+    yield
+    log.info("ml-service stopping")
+    await worker.stop_workers()
+    await db.close_pool()
+    await redis_client.close()
+
+
+app = FastAPI(title="MEB ML Service", lifespan=lifespan)
+
+# static
+app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static")
 
-app = FastAPI()
 
 @app.get("/health")
-def health():
+async def health():
+    pg_ok = True
+    try:
+        await db.fetchrow("SELECT 1")
+    except Exception:
+        pg_ok = False
+    redis_ok = True
+    try:
+        await redis_client.client().ping()
+    except Exception:
+        redis_ok = False
     return {
-        "status": "ok",
+        "status": "ok" if (pg_ok and redis_ok) else "degraded",
         "service": "ml",
-        "version": "1.0.0",
-        "build_number": "1",
-        "version_state": "dev"
+        "postgres": "connected" if pg_ok else "disconnected",
+        "redis": "connected" if redis_ok else "disconnected",
+        "minio": "connected" if minio_client.check() else "disconnected",
+        "version": "2.0.0",
     }
 
-@app.get("/")
-def root():
-    return {"message": "ML Service"}
+
+from routers import (  # noqa: E402
+    datasets,
+    models,
+    pages,
+    repos,
+    results,
+    tests,
+    trainings,
+    trainings_stream,
+)
+
+app.include_router(pages.router)
+app.include_router(datasets.router)
+app.include_router(models.router)
+app.include_router(repos.router)
+app.include_router(trainings.router)
+app.include_router(trainings_stream.router)
+app.include_router(tests.router)
+app.include_router(results.router)