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/model_spec.py

@@ -0,0 +1,90 @@
+"""Parse e validazione del contratto `model.yml` nelle repo utente.
+
+Schema sintetico (vedi piano):
+  name, type, version, python
+  train: {entrypoint, inputs, outputs, metrics}
+  test:  {entrypoint, io, input_schema[], output_schema[]}
+  resources: {cpu, mem_mb, gpu}
+"""
+from __future__ import annotations
+
+from typing import Any, Optional
+
+import yaml
+from pydantic import BaseModel, ValidationError
+
+from core import gitea, redis_client
+
+
+class _FieldSpec(BaseModel):
+    name: str
+    dtype: str
+    min: Optional[float] = None
+    max: Optional[float] = None
+    unit: Optional[str] = None
+
+
+class _Train(BaseModel):
+    entrypoint: str
+    inputs: dict = {}
+    outputs: dict = {}
+    metrics: dict = {}
+
+
+class _Test(BaseModel):
+    entrypoint: str
+    io: str = "stdio_json"
+    input_schema: list[_FieldSpec] = []
+    output_schema: list[_FieldSpec] = []
+
+
+class ModelSpec(BaseModel):
+    name: str
+    type: str
+    version: str = "0.1.0"
+    python: str = "3.11"
+    train: _Train
+    test: Optional[_Test] = None
+    resources: dict = {}
+
+
+def parse_yaml(content: bytes | str) -> dict:
+    """Parsa stringa YAML → dict validato. Solleva ValueError su errore."""
+    if isinstance(content, bytes):
+        content = content.decode("utf-8")
+    try:
+        raw = yaml.safe_load(content) or {}
+        spec = ModelSpec(**raw)
+        return spec.model_dump()
+    except (yaml.YAMLError, ValidationError) as e:
+        raise ValueError(f"invalid model.yml: {e}") from e
+
+
+async def fetch_and_parse_spec(owner_repo: str, ref: str) -> Optional[dict]:
+    """Recupera model.yml dalla repo alla revisione e lo parsa.
+    Cache Redis `ml:modelspec:{repo}:{ref}` TTL 1h.
+    """
+    cache_key = f"ml:modelspec:{owner_repo}:{ref}"
+    try:
+        cached = await redis_client.client().get(cache_key)
+        if cached:
+            import json
+            return json.loads(cached)
+    except Exception:
+        pass
+
+    try:
+        raw = await gitea.get_file_raw(owner_repo, ref, "model.yml")
+    except Exception:
+        try:
+            raw = await gitea.get_file_raw(owner_repo, ref, "model.yaml")
+        except Exception:
+            return None
+    spec = parse_yaml(raw)
+
+    try:
+        import json
+        await redis_client.client().set(cache_key, json.dumps(spec), ex=3600)
+    except Exception:
+        pass
+    return spec