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/core/db.py

@@ -0,0 +1,53 @@
+"""Connessione asyncpg al database ml. Pool singleton."""
+from __future__ import annotations
+
+import asyncpg
+from typing import Optional
+
+from core.config import settings
+
+_pool: Optional[asyncpg.Pool] = None
+
+
+async def init_pool() -> asyncpg.Pool:
+    global _pool
+    if _pool is None:
+        _pool = await asyncpg.create_pool(
+            host=settings.pg_host,
+            port=settings.pg_port,
+            user=settings.pg_user,
+            password=settings.pg_password,
+            database=settings.pg_db,
+            min_size=1,
+            max_size=10,
+            command_timeout=30,
+        )
+    return _pool
+
+
+async def close_pool() -> None:
+    global _pool
+    if _pool is not None:
+        await _pool.close()
+        _pool = None
+
+
+def pool() -> asyncpg.Pool:
+    if _pool is None:
+        raise RuntimeError("DB pool not initialized — call init_pool() at startup")
+    return _pool
+
+
+async def fetch(sql: str, *args):
+    async with pool().acquire() as c:
+        return await c.fetch(sql, *args)
+
+
+async def fetchrow(sql: str, *args):
+    async with pool().acquire() as c:
+        return await c.fetchrow(sql, *args)
+
+
+async def execute(sql: str, *args):
+    async with pool().acquire() as c:
+        return await c.execute(sql, *args)