release: finalize v0.1.0 changelog

Closes #13

.dockerignore

@@ -0,0 +1,16 @@
+.env
+.env.*
+!.env.example
+.venv
+.venv/*
+__pycache__
+.mypy_cache
+.pytest_cache
+.ruff_cache
+node_modules
+.idea
+.vscode
+.git
+.gitignore
+.dockerignore
+docker-compose*.yml

.env.example

@@ -1,3 +1,3 @@
-# Home Assistant Zugriff
-SILLYHOME_HA_URL=http://localhost:8123
-SILLYHOME_HA_TOKEN=dein_long_lived_access_token
+SILLYHOME_HA_URL=http://homeassistant.local:8123
+SILLYHOME_HA_TOKEN=REPLACE_ME_WITH_LONG_LIVED_TOKEN
+SILLYHOME_MODEL_STORE=.model_store

.gitea/workflows/quality.yml

@@ -0,0 +1,24 @@
+name: quality
+
+on:
+  push:
+    branches: ["main", "otto/**", "feature/**"]
+  pull_request:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.11", "3.13"]
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: pip
+      - run: python -m pip install --upgrade pip
+      - run: python -m pip install -e ".[dev]"
+      - run: python -m pytest
+      - run: ruff check .
+      - run: mypy

.gitignore

@@ -4,6 +4,7 @@
 /.vscode
 __pycache__/
 *.pyc
+*.egg-info/
 .mypy_cache/
 .pytest_cache/
 .ruff_cache/

CHANGELOG.md

@@ -1,5 +1,13 @@
 # Changelog
 
 ## Unreleased
+
+## 0.1.0 - 2026-06-13
 - Projektinitiierung
 - Architektur, ADRs und Roadmap
+- Einheitliche produktive FastAPI-App für HA- und ML-Routen
+- Funktionierende ENV-Konfiguration und sauberer HA-503-Zustand
+- Persistente, validierte und gegen Path Traversal gehärtete Model Registry
+- Reproduzierbares Packaging, CI-Gates und gehärteter non-root Container
+- Definierte API-Fehler und korrigierte Evaluationsmetriken
+- Scheduler-tauglicher Retraining-Service mit API und atomischem Registry-Update

Dockerfile

@@ -0,0 +1,27 @@
+FROM python:3.13-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1 \
+    SILLYHOME_MODEL_STORE=/app/data/models
+
+WORKDIR /app
+
+RUN addgroup --system sillyhome && adduser --system --ingroup sillyhome sillyhome
+
+COPY pyproject.toml README.md ./
+COPY app ./app
+COPY backend ./backend
+RUN python -m pip install --upgrade pip && \
+    python -m pip install . && \
+    mkdir -p /app/data/models && \
+    chown -R sillyhome:sillyhome /app/data
+
+EXPOSE 8000
+
+USER sillyhome
+
+HEALTHCHECK --interval=30s --timeout=3s --start-period=10s --retries=3 \
+    CMD ["python", "-c", "import urllib.request; urllib.request.urlopen('http://127.0.0.1:8000/health', timeout=2)"]
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -1,6 +1,13 @@
 # SillyHome Next
 
-Modern, lokal-first und datenschutzfreundliches Smart-Home-Intelligenzsystem für Home Assistant.
+Lokaler, datenschutzfreundlicher API-Prototyp für Home Assistant.
+
+## Reifegrad
+
+Version `0.1.0` stellt eine gehärtete technische Basis bereit: Home-Assistant-Entities
+lesen, regelbasierte Bausteine und eine persistente Modell-Artefakt-Registry. Die
+aktuelle Trainings- und Vorhersagelogik ist noch eine deterministische
+Schnittstellen-Implementierung und **kein produktives Machine-Learning-Modell**.
 
 ## Motivation
 TheSillyHome zeigte die Idee: statt statischer Regeln das Zuhause aus Verhaltensmustern verstehen. Diese Architektur modernisiert den Ansatz in Richtung Explainable AI, hybride Intelligenzebenen und langlebige Wartbarkeit.
@@ -13,33 +20,55 @@ TheSillyHome zeigte die Idee: statt statischer Regeln das Zuhause aus Verhaltens
 - Lokal-first ohne Cloudpflicht
 - Erweiterbar, testbar, dokumentiert
 
-## Quickstart (lokaler Betrieb)
+## Quickstart
+1. Python-Venv anlegen und Abhängigkeiten installieren:
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install -e ".[dev]"
+```
 
-1. **Voraussetzungen**
-   - Python 3.11+
-   - Home Assistant mit REST-API erreichbar
-   - `pip install -e .[dev]`
+2. Konfiguration aus `.env.example` übernehmen und anpassen:
+```bash
+cp .env.example .env
+```
 
-2. **Umgebungsvariablen** (`.env` im Projektroot)
-   ```
-   SILLYHOME_HA_URL=http://localhost:8123
-   SILLYHOME_HA_TOKEN=dein_long_lived_access_token
-   ```
-   Tipp: `.env.example` kopieren und anpassen. Tokens niemals committen!
+3. API starten:
+```bash
+uvicorn app.main:app --reload
+```
 
-3. **Server starten**
-   ```
-   uvicorn app.main:app --reload
-   ```
+4. Erreichbar unter:
+- `http://127.0.0.1:8000/health` - Health-Check
+- `http://127.0.0.1:8000/docs/` - OpenAPI-Dokumentation
+- `http://127.0.0.1:8000/v1/entities` - Home-Assistant-Entities
+- `http://127.0.0.1:8000/ml/health` - Registry-/Serving-Health
+- `POST http://127.0.0.1:8000/ml/retrain` - Modell-Metadaten aktualisieren
 
-4. **Prüfen**
-   - OpenAPI-Docs: http://localhost:8000/docs
-   - Health: http://localhost:8000/health
-   - Entities: http://localhost:8000/v1/entities (benötigt gültige HA-Konfiguration)
+Ohne vollständige HA-Konfiguration liefert `/v1/entities` bewusst `503`.
 
-5. **Tests**
-   ```
-   pytest -q
-   ruff check .
-   mypy app tests
-   ```
+### Docker Compose
+
+```bash
+cp .env.example .env
+docker compose up --build -d
+curl --fail http://127.0.0.1:8000/health
+```
+
+Compose veröffentlicht die API standardmäßig nur auf `127.0.0.1`. Für Zugriff aus
+dem Netz muss ein authentifizierender Reverse Proxy vorgeschaltet werden.
+
+### ENV-Konfiguration (`.env.example`)
+- `SILLYHOME_HA_URL` – Basis-URL deiner Home-Assistant-Instanz (z. B. `http://homeassistant.local:8123`)
+- `SILLYHOME_HA_TOKEN` – Long-Lived Access Token eines dedizierten HA-Benutzers mit minimalen Rechten
+- `SILLYHOME_MODEL_STORE` – Verzeichnis für persistierte Modell-Metadaten
+
+Niemals Administrator-Tokens oder Passwörter eintragen. `.env` gehört nicht ins
+Versionskontrollsystem.
+
+### Tests
+```bash
+pytest
+ruff check .
+mypy
+```

app/__init__.py

@@ -0,0 +1 @@
+"""SillyHome Next application package."""

app/api/__init__.py

@@ -0,0 +1 @@
+"""API package."""

app/api/v1/__init__.py

@@ -0,0 +1 @@
+"""Version 1 API package."""

app/api/v1/entities.py

@@ -1,10 +1,12 @@
 from __future__ import annotations
 
-from typing import List, Sequence
+from typing import List
 
-from fastapi import APIRouter
+from fastapi import APIRouter, Depends
 
+from app.dependencies import get_ha_reader
 from app.ha.models import HaEntitySummary
+from app.ha.reader import HaReader
 
 router = APIRouter(prefix="/v1", tags=["entities"])
 
@@ -15,5 +17,5 @@ router = APIRouter(prefix="/v1", tags=["entities"])
     description="Gibt eine kompakte Zusammenfassung aller erreichbaren HA-Entitäten zurück.",
     response_model=List[HaEntitySummary],
 )
-def list_entities() -> Sequence[HaEntitySummary]:
-    raise NotImplementedError("Integration mit dem HA-Client folgt in separatem Issue.")
+def list_entities(ha_reader: HaReader = Depends(get_ha_reader)) -> List[HaEntitySummary]:
+    return list(ha_reader.read_entities())

app/config.py

@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class Settings:
+    ha_url: str | None = None
+    ha_token: str | None = None
+    model_store: str = ".model_store"
+
+    @property
+    def ha_configured(self) -> bool:
+        return bool(self.ha_url and self.ha_token)
+
+
+def load_settings() -> Settings:
+    return Settings(
+        ha_url=os.getenv("SILLYHOME_HA_URL") or os.getenv("HA_URL"),
+        ha_token=os.getenv("SILLYHOME_HA_TOKEN") or os.getenv("HA_TOKEN"),
+        model_store=os.getenv("SILLYHOME_MODEL_STORE", ".model_store"),
+    )

app/core/__init__.py

@@ -0,0 +1 @@
+"""Core application helpers."""

app/core/exception_handlers.py

@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from fastapi import FastAPI, Request, status
+from fastapi.responses import JSONResponse
+
+from app.ha.exceptions import HaAuthError, HaClientError, HaHttpError, HaTimeoutError
+
+
+def register_exception_handlers(app: FastAPI) -> None:
+    @app.exception_handler(HaClientError)
+    async def handle_ha_client_error(_: Request, exc: HaClientError) -> JSONResponse:
+        return JSONResponse(
+            status_code=_status_code_for_ha_error(exc),
+            content={"detail": exc.public_detail},
+        )
+
+
+def _status_code_for_ha_error(exc: HaClientError) -> int:
+    if isinstance(exc, HaTimeoutError):
+        return status.HTTP_504_GATEWAY_TIMEOUT
+    if isinstance(exc, (HaAuthError, HaHttpError)):
+        return status.HTTP_502_BAD_GATEWAY
+    return status.HTTP_502_BAD_GATEWAY

app/core/exceptions.py

@@ -0,0 +1,20 @@
+from __future__ import annotations
+
+from typing import Any
+
+from fastapi import FastAPI, Request
+
+from app.ha.exceptions import HaAuthError, HaClientError, HaHttpError
+
+
+def register_exception_handlers(app: FastAPI) -> None:
+    @app.exception_handler(HaClientError)
+    async def handle_ha_client_error(request: Request, exc: HaClientError) -> Any:  # pragma: no cover - einfacher Wrapper
+        if isinstance(exc, HaAuthError):
+            return {"detail": "Ungültige Authentifizierung gegenüber Home Assistant."}
+        if isinstance(exc, HaHttpError):
+            return {
+                "detail": "Home Assistant meldet einen Fehler.",
+                "upstream_status": exc.status_code,
+            }
+        return {"detail": str(exc)}

app/dependencies.py

@@ -0,0 +1,15 @@
+from __future__ import annotations
+
+from fastapi import HTTPException, Request, status
+
+from app.ha.reader import HaReader
+
+
+def get_ha_reader(request: Request) -> HaReader:
+    reader = getattr(request.app.state, "ha_reader", None)
+    if not isinstance(reader, HaReader):
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Home Assistant is not configured.",
+        )
+    return reader

app/ha/client.py

@@ -5,6 +5,13 @@ from dataclasses import dataclass
 
 import requests
 
+from app.ha.exceptions import (
+    HaAuthError,
+    HaHttpError,
+    HaTimeoutError,
+    HaUnexpectedPayloadError,
+)
+
 logger = logging.getLogger(__name__)
 
 
@@ -24,10 +31,47 @@ class HaClient:
             "Content-Type": "application/json",
         })
 
+    def close(self) -> None:
+        self._session.close()
+
     def list_entities(self) -> list[dict[str, object]]:
-        response = self._session.get(
-            f"{self._settings.url}/api/states",
-            timeout=self._settings.timeout_seconds,
-        )
-        response.raise_for_status()
-        return response.json()
+        try:
+            response = self._session.get(
+                f"{self._settings.url.rstrip('/')}/api/states",
+                timeout=self._settings.timeout_seconds,
+            )
+        except requests.Timeout as exc:
+            raise HaTimeoutError("Zeitüberschreitung beim Zugriff auf Home Assistant.") from exc
+        except requests.RequestException as exc:
+            raise HaHttpError(
+                getattr(getattr(exc, "response", None), "status_code", 502),
+                "Netzwerkfehler beim Zugriff auf Home Assistant.",
+            ) from exc
+
+        if response.status_code in (401, 403):
+            raise HaAuthError(
+                response.status_code,
+                "Authentifizierung bei Home Assistant fehlgeschlagen.",
+            )
+
+        try:
+            response.raise_for_status()
+        except requests.HTTPError as exc:
+            raise HaHttpError(
+                response.status_code,
+                "Home Assistant meldet einen Fehler.",
+            ) from exc
+
+        try:
+            payload = response.json()
+        except ValueError as exc:
+            raise HaUnexpectedPayloadError(
+                "Antwort von Home Assistant ist kein gültiges JSON."
+            ) from exc
+
+        if not isinstance(payload, list):
+            raise HaUnexpectedPayloadError(
+                "Antwort von Home Assistant hat unerwartetes Format."
+            )
+
+        return payload

app/ha/exceptions.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+
+class HaClientError(Exception):
+    """Basisklasse für HA-Client-Fehler."""
+
+    public_detail: str | None = None
+
+
+class HaTimeoutError(HaClientError):
+    """Zeitüberschreitung bei Request an Home Assistant."""
+
+    public_detail = "Home Assistant request timed out."
+
+
+class HaHttpError(HaClientError):
+    """Nicht erfolgreicher HTTP-Statuscode."""
+
+    public_detail = "Home Assistant request failed."
+
+    def __init__(self, status_code: int, message: str = "") -> None:
+        super().__init__(message)
+        self.status_code = status_code
+
+
+class HaAuthError(HaHttpError):
+    """Authentifizierung oder Berechtigung fehlgeschlagen."""
+
+    public_detail = "Home Assistant authentication failed."
+
+
+class HaUnexpectedPayloadError(HaClientError):
+    """Antwort hat nicht das erwartete Format."""
+
+    public_detail = "Home Assistant returned an unexpected payload."

app/ha/reader.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
+from typing import Any
 
 from app.ha.client import HaClient
-from app.ha.models import HaEntitySummary, HaState
+from app.ha.models import HaEntitySummary
 
 
 class HaReader:
@@ -14,18 +15,26 @@ class HaReader:
         entities = self._client.list_entities()
         summaries: list[HaEntitySummary] = []
         for item in entities:
-            entity_id = item.get("entity_id", "")
-            if "." not in entity_id:
+            raw_entity_id = item.get("entity_id")
+            if not isinstance(raw_entity_id, str) or "." not in raw_entity_id:
                 continue
+            entity_id = raw_entity_id
             domain = entity_id.split(".", 1)[0]
-            attributes = item.get("attributes") or {}
+            raw_attributes = item.get("attributes") or {}
+            attributes: dict[str, Any] = raw_attributes if isinstance(raw_attributes, dict) else {}
             summaries.append(
                 HaEntitySummary(
                     entity_id=entity_id,
                     domain=domain,
-                    state_class=str(attributes.get("state_class") or ""),
-                    device_class=str(attributes.get("device_class") or ""),
-                    unit_of_measurement=str(attributes.get("unit_of_measurement") or ""),
+                    state_class=_optional_str(attributes.get("state_class")),
+                    device_class=_optional_str(attributes.get("device_class")),
+                    unit_of_measurement=_optional_str(attributes.get("unit_of_measurement")),
                 )
             )
         return summaries
+
+
+def _optional_str(value: object) -> str | None:
+    if value is None or value == "":
+        return None
+    return str(value)

app/main.py

@@ -1,21 +1,38 @@
 from contextlib import asynccontextmanager
+from collections.abc import AsyncIterator
+from typing import cast
 
-from fastapi import FastAPI, Depends
+from fastapi import FastAPI
 
 from app.api.v1.entities import router as entities_router
+from app.config import load_settings
+from app.core.exception_handlers import register_exception_handlers
 from app.ha.client import HaClient, HaClientSettings
 from app.ha.reader import HaReader
+from app.ml.registry.model_registry import ModelRegistry
+from backend.routes.ml import init_ml_routes
 
 
 @asynccontextmanager
-async def lifespan(app: FastAPI):
-    settings = HaClientSettings(
-        url=app.state.settings.ha_url,
-        token=app.state.settings.ha_token,
-    )
-    client = HaClient(settings=settings)
-    app.state.ha_reader = HaReader(client=client)
-    yield
+async def lifespan(app: FastAPI) -> AsyncIterator[None]:
+    settings = app.state.settings
+    client: HaClient | None = None
+    app.state.registry = ModelRegistry(settings.model_store)
+    if hasattr(app.state, "ha_reader"):
+        del app.state.ha_reader
+    if settings.ha_configured:
+        client = HaClient(
+            settings=HaClientSettings(
+                url=cast(str, settings.ha_url),
+                token=cast(str, settings.ha_token),
+            )
+        )
+        app.state.ha_reader = HaReader(client=client)
+    try:
+        yield
+    finally:
+        if client is not None:
+            client.close()
 
 
 app = FastAPI(
@@ -24,21 +41,10 @@ app = FastAPI(
     version="0.1.0",
     lifespan=lifespan,
 )
-
-
-class Settings:
-    ha_url: str
-    ha_token: str
-
-
-app.state.settings = Settings()
-
-
-def get_ha_reader() -> HaReader:
-    return app.state.ha_reader
-
-
-app.include_router(entities_router, dependencies=[Depends(get_ha_reader)])
+app.state.settings = load_settings()
+register_exception_handlers(app)
+app.include_router(entities_router)
+init_ml_routes(app, model_store=app.state.settings.model_store)
 
 
 @app.get("/health")
@@ -48,4 +54,4 @@ def health() -> dict[str, str]:
 
 @app.get("/")
 def root() -> dict[str, str]:
-    return {"service": "sillyhome-next", "docs": "/docs"}
+    return {"service": "sillyhome-next", "docs": "/docs"}

app/ml/__init__.py

@@ -0,0 +1,14 @@
+
+"""Machine-Learning-Grundbausteine für SillyHome Next."""
+__all__ = [
+    "FeatureStore",
+    "FeatureVector",
+    "RetrainingResult",
+    "RetrainingService",
+    "TrainedArtifact",
+    "TrainingPipeline",
+    "retrain_model",
+]
+from app.ml.feature_store import FeatureStore, FeatureVector
+from app.ml.retraining import RetrainingResult, RetrainingService, retrain_model
+from app.ml.training import TrainedArtifact, TrainingPipeline

app/ml/evaluation.py

@@ -0,0 +1,64 @@
+from __future__ import annotations
+
+import logging
+from collections.abc import Sequence
+from dataclasses import dataclass
+
+from app.ml.training import TrainingPipeline
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Metric:
+    name: str
+    value: float
+    threshold: float | None = None
+
+
+@dataclass
+class EvalReport:
+    artifact_id: str
+    sample_size: int
+    metrics: list[Metric]
+
+
+class Evaluator:
+    def __init__(self, pipeline: TrainingPipeline) -> None:
+        self._pipeline = pipeline
+
+    def evaluate(self, artifact_id: str, predictions: Sequence[str]) -> EvalReport:
+        try:
+            supported_sensors = set(self._pipeline.export(artifact_id).supported_sensors)
+        except KeyError as exc:
+            raise ValueError("Kein trainiertes Modell für Evaluation vorhanden.") from exc
+
+        parsed_sensors = [_prediction_sensor(prediction) for prediction in predictions]
+        supported_hits = sum(sensor in supported_sensors for sensor in parsed_sensors)
+        unknown_hits = sum(sensor not in supported_sensors for sensor in parsed_sensors)
+        sample_size = len(predictions)
+        coverage = supported_hits / sample_size if sample_size else 0.0
+        unknown_rate = unknown_hits / sample_size if sample_size else 0.0
+
+        coverage_metric = Metric(name="coverage", value=coverage, threshold=0.8)
+        unknown_metric = Metric(name="unknown_rate", value=unknown_rate, threshold=0.1)
+
+        report = EvalReport(
+            artifact_id=artifact_id,
+            sample_size=sample_size,
+            metrics=[coverage_metric, unknown_metric],
+        )
+        logger.info(
+            "Evaluation %s -> coverage=%.2f, unknown_rate=%.2f",
+            artifact_id,
+            coverage,
+            unknown_rate,
+        )
+        return report
+
+
+def _prediction_sensor(prediction: str) -> str | None:
+    parts = prediction.split(":", 2)
+    if len(parts) != 3 or not parts[0] or not parts[1]:
+        return None
+    return parts[1]

app/ml/feature_store.py

@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import Iterable
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class FeatureVector:
+    sensor_id: str
+    values: dict[str, float]
+    label: str | None = None
+
+
+class FeatureStore:
+    def __init__(self) -> None:
+        self._vectors: dict[str, list[FeatureVector]] = defaultdict(list)
+
+    def add(self, vector: FeatureVector) -> None:
+        self._vectors[vector.sensor_id].append(vector)
+
+    def add_batch(self, vectors: Iterable[FeatureVector]) -> None:
+        for vector in vectors:
+            self.add(vector)
+
+    def latest(self, sensor_id: str) -> FeatureVector | None:
+        series = self._vectors.get(sensor_id)
+        return series[-1] if series else None
+
+    def all(self) -> list[FeatureVector]:
+        return [vector for vectors in self._vectors.values() for vector in vectors]

app/ml/predictor.py

@@ -0,0 +1,50 @@
+from __future__ import annotations
+
+import logging
+from typing import Sequence
+
+from app.ml.feature_store import FeatureVector
+from app.ml.registry.model_registry import ModelRegistry
+from app.ml.training import TrainedArtifact, TrainingPipeline
+
+logger = logging.getLogger(__name__)
+
+
+class Predictor:
+    def __init__(
+        self,
+        pipeline: TrainingPipeline | None = None,
+        registry: ModelRegistry | None = None,
+    ) -> None:
+        if isinstance(pipeline, ModelRegistry) and registry is None:
+            registry = pipeline
+            pipeline = None
+        if pipeline is None and registry is None:
+            raise ValueError("Predictor erfordert TrainingPipeline oder ModelRegistry.")
+        self._pipeline = pipeline
+        self._registry = registry
+
+    def predict(self, artifact_id: str, entity: FeatureVector) -> str:
+        artifact = self._get_artifact(artifact_id)
+        if entity.sensor_id not in artifact.supported_sensors:
+            raise ValueError(
+                f"Sensor '{entity.sensor_id}' wird vom Modell '{artifact_id}' nicht unterstützt."
+            )
+        return f"{artifact_id}:{entity.sensor_id}:{entity.values}"
+
+    def predict_batch(self, artifact_id: str, entities: Sequence[FeatureVector]) -> list[str]:
+        return [self.predict(artifact_id, entity) for entity in entities]
+
+    @staticmethod
+    def default_artifact(pipeline: TrainingPipeline) -> TrainedArtifact:
+        artifacts = list(pipeline._artifacts)
+        if not artifacts:
+            raise ValueError("Kein trainiertes Modell gefunden.")
+        return pipeline.export(artifacts[-1])
+
+    def _get_artifact(self, artifact_id: str) -> TrainedArtifact:
+        if self._registry is not None:
+            return self._registry.load_artifact(artifact_id)
+        if self._pipeline is not None:
+            return self._pipeline.export(artifact_id)
+        raise RuntimeError("Predictor nicht initialisiert.")

app/ml/registry/__init__.py

@@ -0,0 +1,3 @@
+from .model_registry import ModelRegistry
+
+__all__ = ["ModelRegistry"]

app/ml/registry/model_registry.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+import json
+import logging
+import os
+from pathlib import Path
+import re
+from threading import RLock
+from collections.abc import Iterable
+
+from app.ml.training import TrainedArtifact
+
+logger = logging.getLogger(__name__)
+
+_ARTIFACT_ID_PATTERN = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._-]{0,127}$")
+
+
+class ModelRegistry:
+    def __init__(self, root: str | Path) -> None:
+        self._root = Path(root).resolve()
+        self._root.mkdir(parents=True, exist_ok=True)
+        self._artifacts: dict[str, TrainedArtifact] = {}
+        self._lock = RLock()
+        self._load_existing()
+
+    def register(self, artifact: TrainedArtifact) -> TrainedArtifact:
+        registered, _ = self.register_with_status(artifact)
+        return registered
+
+    def register_with_status(self, artifact: TrainedArtifact) -> tuple[TrainedArtifact, bool]:
+        self._validate_artifact_id(artifact.artifact_id)
+        with self._lock:
+            replaced = artifact.artifact_id in self._artifacts
+            self._persist(artifact)
+            self._artifacts[artifact.artifact_id] = artifact
+        return artifact, replaced
+
+    def load_artifact(self, artifact_id: str) -> TrainedArtifact:
+        self._validate_artifact_id(artifact_id)
+        with self._lock:
+            if artifact_id not in self._artifacts:
+                raise KeyError(f"Artifact '{artifact_id}' nicht registriert.")
+            return self._artifacts[artifact_id]
+
+    def list_models(self) -> Iterable[TrainedArtifact]:
+        with self._lock:
+            return [self._artifacts[key] for key in sorted(self._artifacts)]
+
+    def _load_existing(self) -> None:
+        for source in sorted(self._root.glob("*.json")):
+            try:
+                raw = json.loads(source.read_text(encoding="utf-8"))
+                artifact_id = raw["artifact_id"]
+                supported_sensors = raw["supported_sensors"]
+                if not isinstance(artifact_id, str) or not isinstance(supported_sensors, list):
+                    raise ValueError("invalid artifact structure")
+                self._validate_artifact_id(artifact_id)
+                if source.name != f"{artifact_id}.json":
+                    raise ValueError("artifact id does not match filename")
+                if not all(isinstance(sensor, str) for sensor in supported_sensors):
+                    raise ValueError("supported_sensors must contain strings")
+            except (KeyError, TypeError, ValueError, json.JSONDecodeError) as exc:
+                raise ValueError(f"Ungültiges Modell-Artefakt: {source.name}") from exc
+
+            self._artifacts[artifact_id] = TrainedArtifact(
+                artifact_id=artifact_id,
+                supported_sensors=tuple(supported_sensors),
+            )
+
+    def _persist(self, artifact: TrainedArtifact) -> None:
+        target = self._root / f"{artifact.artifact_id}.json"
+        temporary = target.with_suffix(".json.tmp")
+        payload = {
+            "artifact_id": artifact.artifact_id,
+            "supported_sensors": list(artifact.supported_sensors),
+        }
+        temporary.write_text(
+            json.dumps(payload, ensure_ascii=True, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        os.replace(temporary, target)
+        logger.info("Modell gespeichert: %s", target)
+
+    @staticmethod
+    def _validate_artifact_id(artifact_id: str) -> None:
+        if not _ARTIFACT_ID_PATTERN.fullmatch(artifact_id) or ".." in artifact_id:
+            raise ValueError(
+                "artifact_id darf nur Buchstaben, Ziffern, Punkt, Unterstrich "
+                "und Bindestrich enthalten."
+            )

app/ml/retraining.py

@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+from collections.abc import Iterable
+from dataclasses import dataclass
+
+from app.ml.feature_store import FeatureStore, FeatureVector
+from app.ml.registry.model_registry import ModelRegistry
+from app.ml.training import TrainedArtifact, TrainingPipeline
+
+
+@dataclass(frozen=True)
+class RetrainingResult:
+    artifact: TrainedArtifact
+    replaced: bool
+
+
+class RetrainingService:
+    """Runs one retraining cycle without owning scheduling or background threads."""
+
+    def __init__(self, registry: ModelRegistry) -> None:
+        self._registry = registry
+
+    def retrain(
+        self,
+        artifact_id: str,
+        vectors: Iterable[FeatureVector],
+    ) -> RetrainingResult:
+        store = FeatureStore()
+        store.add_batch(vectors)
+        pipeline = TrainingPipeline(store)
+        artifact = pipeline.run(artifact_id)
+        _, replaced = self._registry.register_with_status(artifact)
+        return RetrainingResult(artifact=artifact, replaced=replaced)
+
+
+def retrain_model(
+    registry: ModelRegistry,
+    artifact_id: str,
+    vectors: Iterable[FeatureVector],
+) -> RetrainingResult:
+    """Scheduler-compatible entry point for exactly one retraining run."""
+
+    return RetrainingService(registry).retrain(artifact_id, vectors)

app/ml/training.py

@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+
+from app.ml.feature_store import FeatureStore
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class TrainedArtifact:
+    artifact_id: str
+    supported_sensors: tuple[str, ...]
+
+
+class TrainingPipeline:
+    def __init__(self, store: FeatureStore) -> None:
+        self._store = store
+        self._artifacts: dict[str, TrainedArtifact] = {}
+
+    def run(self, artifact_id: str) -> TrainedArtifact:
+        vectors = self._store.all()
+        if not vectors:
+            raise ValueError("FeatureStore enthält keine Trainingsdaten.")
+
+        sensors = tuple(sorted({vector.sensor_id for vector in vectors}))
+        artifact = TrainedArtifact(artifact_id=artifact_id, supported_sensors=sensors)
+        self._artifacts[artifact_id] = artifact
+        logger.info("Training abgeschlossen für %s mit %d Sensoren", artifact_id, len(sensors))
+        return artifact
+
+    def export(self, artifact_id: str) -> TrainedArtifact:
+        if artifact_id not in self._artifacts:
+            raise KeyError(f"Artifact '{artifact_id}' nicht gefunden.")
+        return self._artifacts[artifact_id]

app/rules/heating.py

@@ -7,9 +7,28 @@ from app.rules.recommender import Rule
 
 
 class HeatingRule(Rule):
+    """Heizungsregel: Nur auf heizungsrelevante Entitäten reagieren.
+
+    Triggert bei:
+    - `climate`-Entitäten direkt
+    - `sensor` mit `device_class` in {temperature, humidity}
+    - `binary_sensor` mit `device_class` in {occupancy, presence}
+
+    Alle anderen Domains/Device-Klassen bleiben ohne Effekt.
+    """
+
+    HEATING_SENSOR_CLASSES: frozenset[str] = frozenset({"temperature", "humidity"})
+    HEATING_PRESENCE_CLASSES: frozenset[str] = frozenset({"occupancy", "presence"})
+
     def matches(self, entities: Sequence[HaEntitySummary]) -> bool:
-        domains = {item.domain for item in entities}
-        return "climate" in domains or "sensor" in domains
+        for item in entities:
+            if item.domain == "climate":
+                return True
+            if item.domain == "sensor" and item.device_class in self.HEATING_SENSOR_CLASSES:
+                return True
+            if item.domain == "binary_sensor" and item.device_class in self.HEATING_PRESENCE_CLASSES:
+                return True
+        return False
 
     def recommendation(self, entities: Sequence[HaEntitySummary]) -> str:
         return "Prüfe Heizungsregelung: Aktiviere energiesparenden Modus bei Abwesenheit."

backend/__init__.py

@@ -0,0 +1 @@
+"""Secondary application entry points for SillyHome Next."""

backend/app.py

@@ -0,0 +1,43 @@
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI
+from starlette.datastructures import State
+
+from backend.routes.ml import init_ml_routes
+from app.ml.registry.model_registry import ModelRegistry
+from app.ml.training import TrainingPipeline
+from app.ml.feature_store import FeatureStore, FeatureVector
+
+
+@asynccontextmanager
+async def lifespan(application: FastAPI) -> AsyncIterator[None]:
+    application.state.registry = ModelRegistry(application.state.model_store)
+    _seed_default_model(application.state)
+    yield
+
+
+def create_app() -> FastAPI:
+    application = FastAPI(title="SillyHome Next ML", lifespan=lifespan)
+    init_ml_routes(application)
+    return application
+
+
+def _seed_default_model(state: State) -> None:
+    registry = getattr(state, "registry", None)
+    if registry is None:
+        registry = ModelRegistry(".model_store")
+        state.registry = registry
+
+    if list(registry.list_models()):
+        return
+
+    store = FeatureStore()
+    store.add(FeatureVector(sensor_id="sensor.front_door", values={"contact": 1.0}))
+    store.add(FeatureVector(sensor_id="sensor.living_room", values={"temperature": 21.0}))
+    pipeline = TrainingPipeline(store)
+    artifact = pipeline.run("default")
+    registry.register(artifact)
+
+
+app = create_app()

backend/routes/__init__.py

@@ -0,0 +1 @@
+"""API route modules."""

backend/routes/ml.py

@@ -0,0 +1,159 @@
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timezone
+from collections.abc import Sequence
+
+from fastapi import APIRouter, FastAPI, HTTPException, Request, status
+from pydantic import BaseModel, Field
+
+from app.ml.feature_store import FeatureVector
+from app.ml.predictor import Predictor
+from app.ml.registry.model_registry import ModelRegistry
+from app.ml.retraining import retrain_model
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/ml", tags=["ml"])
+
+
+class HealthResponse(BaseModel):
+    status: str
+    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+class PredictRequest(BaseModel):
+    model_id: str = Field(..., alias="modelId")
+    sensor_id: str
+    values: dict[str, float]
+
+
+class PredictResponse(BaseModel):
+    model_id: str
+    sensor_id: str
+    prediction: str
+
+
+class BatchRequest(BaseModel):
+    requests: Sequence[PredictRequest]
+
+
+class BatchResponse(BaseModel):
+    predictions: Sequence[PredictResponse]
+
+
+class ModelsResponse(BaseModel):
+    models: list[str]
+
+
+class TrainingSample(BaseModel):
+    sensor_id: str = Field(min_length=1)
+    values: dict[str, float]
+    label: str | None = None
+
+
+class RetrainRequest(BaseModel):
+    model_id: str = Field(..., alias="modelId", min_length=1, max_length=128)
+    samples: list[TrainingSample] = Field(min_length=1)
+
+
+class RetrainResponse(BaseModel):
+    model_id: str
+    supported_sensors: list[str]
+    replaced: bool
+
+
+@router.get("/health", response_model=HealthResponse, status_code=200)
+def health() -> HealthResponse:
+    return HealthResponse(status="ok")
+
+
+@router.get("/models", response_model=ModelsResponse, status_code=200)
+def list_models(request: Request) -> ModelsResponse:
+    registry = _require_registry(request)
+    models = [artifact.artifact_id for artifact in registry.list_models()]
+    return ModelsResponse(models=models)
+
+
+@router.post("/retrain", response_model=RetrainResponse, status_code=200)
+def retrain(payload: RetrainRequest, request: Request) -> RetrainResponse:
+    registry = _require_registry(request)
+    vectors = [
+        FeatureVector(
+            sensor_id=sample.sensor_id,
+            values=sample.values,
+            label=sample.label,
+        )
+        for sample in payload.samples
+    ]
+    try:
+        result = retrain_model(registry, payload.model_id, vectors)
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
+            detail=str(exc),
+        ) from exc
+    return RetrainResponse(
+        model_id=result.artifact.artifact_id,
+        supported_sensors=list(result.artifact.supported_sensors),
+        replaced=result.replaced,
+    )
+
+
+@router.post("/predict", response_model=PredictResponse, status_code=200)
+def predict(payload: PredictRequest, request: Request) -> PredictResponse:
+    registry = _require_registry(request)
+    predictor = Predictor(registry=registry)
+    vector = FeatureVector(sensor_id=payload.sensor_id, values=payload.values)
+    try:
+        prediction = predictor.predict(payload.model_id, vector)
+    except KeyError as exc:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
+            detail=str(exc),
+        ) from exc
+    return PredictResponse(
+        model_id=payload.model_id,
+        sensor_id=payload.sensor_id,
+        prediction=prediction,
+    )
+
+
+@router.post("/batch", response_model=BatchResponse, status_code=200)
+def predict_batch(payload: BatchRequest, request: Request) -> BatchResponse:
+    registry = _require_registry(request)
+    predictor = Predictor(registry=registry)
+    responses: list[PredictResponse] = []
+    for item in payload.requests:
+        vector = FeatureVector(sensor_id=item.sensor_id, values=item.values)
+        try:
+            prediction = predictor.predict(item.model_id, vector)
+        except KeyError as exc:
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc
+        except ValueError as exc:
+            raise HTTPException(
+                status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
+                detail=str(exc),
+            ) from exc
+        responses.append(
+            PredictResponse(model_id=item.model_id, sensor_id=item.sensor_id, prediction=prediction)
+        )
+    return BatchResponse(predictions=responses)
+
+
+def _require_registry(request: Request) -> ModelRegistry:
+    registry = getattr(request.app.state, "registry", None)
+    if not isinstance(registry, ModelRegistry):
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="ML registry nicht initialisiert.",
+        )
+    return registry
+
+
+def init_ml_routes(app: FastAPI, model_store: str = ".model_store") -> None:
+    app.state.model_store = model_store
+    app.include_router(router)
+    logger.info("ML routes registered")

docker-compose.yml

@@ -0,0 +1,23 @@
+services:
+  api:
+    build: .
+    ports:
+      - "127.0.0.1:8000:8000"
+    env_file:
+      - path: .env
+        required: false
+    environment:
+      SILLYHOME_MODEL_STORE: /app/data/models
+    volumes:
+      - model-data:/app/data/models
+    read_only: true
+    tmpfs:
+      - /tmp
+    security_opt:
+      - no-new-privileges:true
+    cap_drop:
+      - ALL
+    restart: unless-stopped
+
+volumes:
+  model-data:

docs/ml_api.md

@@ -0,0 +1,158 @@
+# ML-Serving-API
+
+Diese Dokumentation beschreibt die REST-Endpunkte der aktuellen
+Modell-Artefakt- und Vorhersage-Schnittstelle.
+
+> Hinweis: Version 0.1.0 enthält noch kein statistisch trainiertes ML-Modell.
+> Die Vorhersage ist eine deterministische Referenzimplementierung für den
+> späteren Modellvertrag.
+
+## Basis-URL
+
+- Standard: `http://127.0.0.1:8000/ml`
+- Health: `/health`
+- Modelle: `/models`
+- Retraining: `/retrain`
+- Einzelvorhersage: `/predict`
+- Batchvorhersage: `/batch`
+
+Der Standard-Start erfolgt über `uvicorn app.main:app`, danach stehen HA- und
+ML-Routen in derselben Anwendung bereit.
+
+## Endpoints
+
+### `GET /ml/health`
+
+Health-Check der ML-Services.
+
+**Beispielantwort**
+```json
+{
+  "status": "ok",
+  "updated_at": "2026-06-11T12:00:00Z"
+}
+```
+
+### `GET /ml/models`
+
+Listet alle registrierten Modell-Artefakte auf.
+
+**Beispielantwort**
+```json
+{
+  "models": ["default"]
+}
+```
+
+### `POST /ml/predict`
+
+Einzelne Vorhersage für einen Sensor.
+
+**Request**
+```json
+{
+  "modelId": "default",
+  "sensor_id": "sensor.kitchen",
+  "values": {"temperature": 21.0}
+}
+```
+
+**Antwort**
+```json
+{
+  "model_id": "default",
+  "sensor_id": "sensor.kitchen",
+  "prediction": "default:sensor.kitchen:{'temperature': 21.0}"
+}
+```
+
+### `POST /ml/retrain`
+
+Trainiert die Artefakt-Metadaten aus neuen Sensordaten. Existiert `modelId`
+bereits, wird das Artefakt atomisch ersetzt und beim nächsten Prozessstart aus
+dem Modellverzeichnis geladen.
+
+**Request**
+```json
+{
+  "modelId": "home-model",
+  "samples": [
+    {
+      "sensor_id": "sensor.kitchen",
+      "values": {"temperature": 21.0},
+      "label": "occupied"
+    }
+  ]
+}
+```
+
+**Antwort**
+```json
+{
+  "model_id": "home-model",
+  "supported_sensors": ["sensor.kitchen"],
+  "replaced": false
+}
+```
+
+### `POST /ml/batch`
+
+Batch-Vorhersage für mehrere Sensorwerte.
+
+**Request**
+```json
+{
+  "requests": [
+    {
+      "modelId": "default",
+      "sensor_id": "sensor.kitchen",
+      "values": {"temperature": 21.0}
+    },
+    {
+      "modelId": "default",
+      "sensor_id": "sensor.bedroom",
+      "values": {"temperature": 18.5}
+    }
+  ]
+}
+```
+
+**Antwort**
+```json
+{
+  "predictions": [
+    {
+      "model_id": "default",
+      "sensor_id": "sensor.kitchen",
+      "prediction": "default:sensor.kitchen:{'temperature': 21.0}"
+    },
+    {
+      "model_id": "default",
+      "sensor_id": "sensor.bedroom",
+      "prediction": "default:sensor.bedroom:{'temperature': 18.5}"
+    }
+  ]
+}
+```
+
+## Fehlerfälle
+
+- `404 Not Found`: Modell nicht registriert.
+- `422 Unprocessable Content`: Sensor wird vom Modell nicht unterstützt oder Eingabe ist ungültig.
+- `503 Service Unavailable`: Registry ist nicht initialisiert.
+
+## Betrieb
+
+Die produktive App lädt Artefakte aus `SILLYHOME_MODEL_STORE`. Neue Artefakte
+werden über `/ml/retrain`, `RetrainingService` oder direkt über
+`ModelRegistry.register(...)` registriert. Die Registry speichert validiertes
+JSON atomisch und lädt es beim Neustart. Die API sollte nur in einem
+vertrauenswürdigen Netz oder hinter einem authentifizierenden Reverse Proxy
+erreichbar sein.
+
+## Verweise
+
+- `app/ml/predictor.py`
+- `app/ml/retraining.py`
+- `app/ml/registry/model_registry.py`
+- `backend/routes/ml.py`

docs/ml_training.md

@@ -0,0 +1,59 @@
+# ML Training- und Evaluations-Workflow
+
+Dieser Workflow beschreibt den aktuellen Platzhalter für Modell-Metadaten,
+Evaluation und Serving. Er trainiert in Version 0.1.0 noch kein statistisches
+Modell.
+
+## 1. Daten sammeln
+
+Alle Trainingsvektoren werden über `FeatureStore.add(...)` oder `add_batch(...)` eingepflegt. Jeder Vektor enthält eine Sensor-ID sowie ein Dictionary mit Merkmalen.
+
+## 2. Artefakt-Metadaten erzeugen
+
+```python
+store = FeatureStore()
+store.add(FeatureVector(sensor_id="sensor.kitchen", values={"temperature": 21.0}))
+pipeline = TrainingPipeline(store)
+artifact = pipeline.run("my_artifact")
+pipeline.export("my_artifact")
+```
+
+`TrainingPipeline.run(...)` erzeugt ein `TrainedArtifact` mit den unterstützten
+Sensor-IDs. Gewichte, Parameter oder ein echtes Modell werden noch nicht
+berechnet.
+
+## 3. Modell evaluieren
+
+```python
+evaluator = Evaluator(pipeline)
+report = evaluator.evaluate(artifact.artifact_id, predictions)
+```
+
+Der Report enthält:
+- `artifact_id`
+- `sample_size`
+- Metriken wie `coverage` und `unknown_rate` mit Default-Schwellenwerten
+
+## 4. Modell registrieren
+
+Das trainierte Artefakt kann anschließend über `ModelRegistry.register(artifact)` bereitgestellt werden. Die ML-Serving-API stellt es unter `/ml/predict` und `/ml/batch` zur Verfügung.
+
+## 5. Retraining ausführen
+
+`RetrainingService.retrain(...)` führt genau einen Trainingslauf aus und ersetzt
+ein vorhandenes Artefakt mit derselben ID atomisch in der Registry:
+
+```python
+service = RetrainingService(registry)
+result = service.retrain("home-model", vectors)
+```
+
+Scheduler, Cronjobs oder Home-Assistant-Automationen können alternativ die
+zustandslose Funktion `retrain_model(registry, artifact_id, vectors)` aufrufen.
+Der Service startet bewusst keinen eigenen Hintergrundprozess. Über
+`POST /ml/retrain` kann derselbe Ablauf per API angestoßen werden.
+
+## Hinweise
+- Für reproduzierbare Sensor-Reihenfolgen wird in `TrainingPipeline.run(...)` eine sortierte Sensor-Liste verwendet.
+- Fehlende Trainingsdaten lösen `ValueError` aus; nicht registrierte Artefakte lösen `KeyError` aus.
+- `coverage` zählt nur exakte Sensor-Referenzen und bleibt im Bereich 0 bis 1.

pyproject.toml

@@ -1,3 +1,7 @@
+[build-system]
+requires = ["setuptools>=69"]
+build-backend = "setuptools.build_meta"
+
 [project]
 name = "sillyhome-next"
 version = "0.1.0"
@@ -7,10 +11,12 @@ dependencies = [
   "fastapi>=0.110.0",
   "uvicorn[standard]>=0.29.0",
   "pydantic>=2.6.0",
+  "requests>=2.31.0",
 ]
 
 [project.optional-dependencies]
 dev = [
+  "httpx2>=2.3.0",
   "pytest>=8.0.0",
   "ruff>=0.4.0",
   "mypy>=1.9.0",
@@ -22,7 +28,11 @@ addopts = "-q"
 
 [tool.mypy]
 strict = true
+files = ["app", "backend", "tests"]
+
+[tool.setuptools.packages.find]
+include = ["app*", "backend*"]
 
 [tool.ruff]
 line-length = 100
-target-version = "py311"
+target-version = "py311"

scripts/gitea_setup.py

@@ -1,5 +1,4 @@
 import requests
-import json
 from pathlib import Path
 
 p = Path('/root/.openclaw/secrets/gitea.env')

tests/api/test_entities.py

@@ -1,10 +1,61 @@
+from collections.abc import Sequence
+
 from fastapi.testclient import TestClient
+
+from app.ha.exceptions import HaTimeoutError
+from app.ha.models import HaEntitySummary
+from app.ha.reader import HaReader
 from app.main import app
 
-client = TestClient(app)
+
+class FakeHaReader(HaReader):
+    def __init__(self) -> None:
+        pass
+
+    def read_entities(self) -> Sequence[HaEntitySummary]:
+        return [HaEntitySummary(entity_id="sensor.temperature", domain="sensor")]
+
+
+class TimeoutHaReader(HaReader):
+    def __init__(self) -> None:
+        pass
+
+    def read_entities(self) -> Sequence[HaEntitySummary]:
+        raise HaTimeoutError("contains internal details that must not leak")
 
 
 def test_openapi_docs_are_available() -> None:
-    response = client.get("/docs")
+    with TestClient(app) as client:
+        response = client.get("/docs")
     assert response.status_code == 200
     assert "SillyHome Next API" in response.text
+
+
+def test_entities_returns_reader_data() -> None:
+    with TestClient(app) as client:
+        app.state.ha_reader = FakeHaReader()
+        response = client.get("/v1/entities")
+    assert response.status_code == 200
+    assert response.json() == [
+        {
+            "entity_id": "sensor.temperature",
+            "domain": "sensor",
+            "state_class": None,
+            "device_class": None,
+            "unit_of_measurement": None,
+        }
+    ]
+
+
+def test_entities_returns_503_without_home_assistant_config() -> None:
+    with TestClient(app) as client:
+        response = client.get("/v1/entities")
+    assert response.status_code == 503
+
+
+def test_entities_maps_ha_errors_without_leaking_details() -> None:
+    with TestClient(app) as client:
+        app.state.ha_reader = TimeoutHaReader()
+        response = client.get("/v1/entities")
+    assert response.status_code == 504
+    assert response.json() == {"detail": "Home Assistant request timed out."}

tests/api/test_ml_routes.py

@@ -0,0 +1,109 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+from fastapi.testclient import TestClient
+
+from app.main import app
+
+
+def test_ml_routes_are_exposed_by_production_app() -> None:
+    with TestClient(app) as client:
+        health = client.get("/ml/health")
+        models = client.get("/ml/models")
+
+    assert health.status_code == 200
+    assert models.status_code == 200
+    assert isinstance(models.json()["models"], list)
+
+
+def test_unknown_model_returns_404() -> None:
+    with TestClient(app) as client:
+        response = client.post(
+            "/ml/predict",
+            json={
+                "modelId": "missing",
+                "sensor_id": "sensor.kitchen",
+                "values": {"temperature": 21.0},
+            },
+        )
+
+    assert response.status_code == 404
+
+
+def test_unsupported_sensor_returns_422(tmp_path: Path) -> None:
+    from app.ml.registry.model_registry import ModelRegistry
+    from app.ml.training import TrainedArtifact
+
+    registry = ModelRegistry(tmp_path)
+    registry.register(TrainedArtifact("default", ("sensor.kitchen",)))
+
+    with TestClient(app) as client:
+        app.state.registry = registry
+        response = client.post(
+            "/ml/predict",
+            json={
+                "modelId": "default",
+                "sensor_id": "sensor.unknown",
+                "values": {"temperature": 21.0},
+            },
+        )
+
+    assert response.status_code == 422
+
+
+def test_retrain_creates_and_replaces_persisted_model(tmp_path: Path) -> None:
+    from app.ml.registry.model_registry import ModelRegistry
+
+    registry = ModelRegistry(tmp_path)
+    with TestClient(app) as client:
+        app.state.registry = registry
+        created = client.post(
+            "/ml/retrain",
+            json={
+                "modelId": "home-model",
+                "samples": [
+                    {
+                        "sensor_id": "sensor.kitchen",
+                        "values": {"temperature": 21.0},
+                    }
+                ],
+            },
+        )
+        replaced = client.post(
+            "/ml/retrain",
+            json={
+                "modelId": "home-model",
+                "samples": [
+                    {
+                        "sensor_id": "sensor.bedroom",
+                        "values": {"temperature": 18.0},
+                    }
+                ],
+            },
+        )
+
+    assert created.status_code == 200
+    assert created.json() == {
+        "model_id": "home-model",
+        "supported_sensors": ["sensor.kitchen"],
+        "replaced": False,
+    }
+    assert replaced.status_code == 200
+    assert replaced.json() == {
+        "model_id": "home-model",
+        "supported_sensors": ["sensor.bedroom"],
+        "replaced": True,
+    }
+    restarted = ModelRegistry(tmp_path)
+    assert restarted.load_artifact("home-model").supported_sensors == ("sensor.bedroom",)
+
+
+def test_retrain_rejects_empty_samples() -> None:
+    with TestClient(app) as client:
+        response = client.post(
+            "/ml/retrain",
+            json={"modelId": "home-model", "samples": []},
+        )
+
+    assert response.status_code == 422

tests/ha/test_ha_client.py

@@ -0,0 +1,71 @@
+from __future__ import annotations
+
+from unittest.mock import Mock
+
+import pytest
+import requests
+
+from app.ha.client import HaClient, HaClientSettings
+from app.ha.exceptions import (
+    HaAuthError,
+    HaHttpError,
+    HaTimeoutError,
+    HaUnexpectedPayloadError,
+)
+
+
+def _client_with_response(response: Mock) -> HaClient:
+    client = HaClient(HaClientSettings(url="http://ha.local", token="test-token"))
+    client._session.get = Mock(return_value=response)  # type: ignore[method-assign]
+    return client
+
+
+def _response(status_code: int = 200, payload: object | None = None) -> Mock:
+    response = Mock()
+    response.status_code = status_code
+    response.json.return_value = [] if payload is None else payload
+    if status_code >= 400:
+        response.raise_for_status.side_effect = requests.HTTPError("upstream failed")
+    return response
+
+
+def test_list_entities_returns_home_assistant_payload() -> None:
+    payload = [{"entity_id": "sensor.temperature", "state": "21"}]
+    client = _client_with_response(_response(payload=payload))
+    assert client.list_entities() == payload
+
+
+def test_list_entities_maps_timeout() -> None:
+    client = HaClient(HaClientSettings(url="http://ha.local", token="test-token"))
+    client._session.get = Mock(side_effect=requests.Timeout("timed out"))  # type: ignore[method-assign]
+    with pytest.raises(HaTimeoutError):
+        client.list_entities()
+
+
+@pytest.mark.parametrize("status_code", [401, 403])
+def test_list_entities_maps_auth_errors(status_code: int) -> None:
+    client = _client_with_response(_response(status_code=status_code))
+    with pytest.raises(HaAuthError) as exc_info:
+        client.list_entities()
+    assert exc_info.value.status_code == status_code
+
+
+def test_list_entities_maps_http_errors() -> None:
+    client = _client_with_response(_response(status_code=500))
+    with pytest.raises(HaHttpError) as exc_info:
+        client.list_entities()
+    assert exc_info.value.status_code == 500
+
+
+def test_list_entities_rejects_invalid_json() -> None:
+    response = _response()
+    response.json.side_effect = ValueError("not json")
+    client = _client_with_response(response)
+    with pytest.raises(HaUnexpectedPayloadError):
+        client.list_entities()
+
+
+def test_list_entities_rejects_non_list_payload() -> None:
+    client = _client_with_response(_response(payload={"entity_id": "sensor.temperature"}))
+    with pytest.raises(HaUnexpectedPayloadError):
+        client.list_entities()

tests/ha/test_ha_reader.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 from app.ha.client import HaClient, HaClientSettings
-from app.ha.models import HaEntitySummary, HaState
 from app.ha.reader import HaReader
 
 

tests/ml/test_evaluation.py

@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+import pytest
+
+from app.ml.evaluation import Evaluator
+from app.ml.feature_store import FeatureStore, FeatureVector
+from app.ml.training import TrainingPipeline
+
+
+def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
+    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
+
+
+def evaluator_factory() -> Evaluator:
+    store = FeatureStore()
+    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
+    pipeline = TrainingPipeline(store)
+    pipeline.run("artifact_v1")
+    return Evaluator(pipeline)
+
+
+def test_evaluate_returns_report_with_metrics() -> None:
+    evaluator = evaluator_factory()
+    report = evaluator.evaluate(
+        "artifact_v1",
+        [
+            "artifact_v1:sensor.kitchen:{'temperature': 21.0}",
+            "artifact_v1:sensor.bedroom:{'temperature': 18.5}",
+        ],
+    )
+    assert report.artifact_id == "artifact_v1"
+    assert report.sample_size == 2
+    assert {metric.name for metric in report.metrics} == {"coverage", "unknown_rate"}
+    assert next(metric.value for metric in report.metrics if metric.name == "coverage") == 1.0
+
+
+def test_evaluate_without_training_raises_value_error() -> None:
+    evaluator = Evaluator(TrainingPipeline(FeatureStore()))
+    with pytest.raises(ValueError):
+        evaluator.evaluate("artifact_v1", [])
+
+
+def test_coverage_is_bounded_and_requires_exact_sensor_match() -> None:
+    evaluator = evaluator_factory()
+    report = evaluator.evaluate(
+        "artifact_v1",
+        [
+            "artifact_v1:sensor.kitchen:{'note': 'sensor.bedroom'}",
+            "artifact_v1:sensor.kitchen_extra:{}",
+            "malformed",
+        ],
+    )
+
+    metrics = {metric.name: metric.value for metric in report.metrics}
+    assert metrics == {"coverage": pytest.approx(1 / 3), "unknown_rate": pytest.approx(2 / 3)}

tests/ml/test_feature_store.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+from app.ml.feature_store import FeatureStore, FeatureVector
+
+
+def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
+    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
+
+
+def test_append_and_latest_returns_last_vector() -> None:
+    store = FeatureStore()
+    vectors = [_vector("sensor.living_room", 20.0), _vector("sensor.living_room", 21.5)]
+    for item in vectors:
+        store.add(item)
+    assert store.latest("sensor.living_room") == vectors[-1]
+
+
+def test_latest_returns_none_when_empty() -> None:
+    store = FeatureStore()
+    assert store.latest("sensor.living_room") is None
+
+
+def test_add_batch_appends_all_vectors() -> None:
+    store = FeatureStore()
+    vectors = [
+        _vector("sensor.kitchen", 19.0),
+        _vector("sensor.kitchen", 20.0),
+        _vector("sensor.bathroom", 23.5),
+    ]
+    store.add_batch(vectors)
+    assert len(store.all()) == 3
+    latest = store.latest("sensor.kitchen")
+    assert latest is not None
+    assert latest.values["temperature"] == 20.0
+
+
+def test_different_sensors_are_stored_independently() -> None:
+    store = FeatureStore()
+    store.add(_vector("sensor.living_room", 21.0))
+    store.add(_vector("sensor.bedroom", 18.5))
+    living_room = store.latest("sensor.living_room")
+    bedroom = store.latest("sensor.bedroom")
+    assert living_room is not None
+    assert bedroom is not None
+    assert living_room.values["temperature"] == 21.0
+    assert bedroom.values["temperature"] == 18.5

tests/ml/test_model_registry.py

@@ -0,0 +1,50 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+
+from app.ml.registry.model_registry import ModelRegistry
+from app.ml.training import TrainedArtifact
+
+
+def test_registry_loads_persisted_artifacts_after_restart(tmp_path: Path) -> None:
+    registry = ModelRegistry(tmp_path)
+    artifact = TrainedArtifact("model-v1", ("sensor.kitchen", "sensor.bedroom"))
+    registry.register(artifact)
+
+    restarted = ModelRegistry(tmp_path)
+
+    assert restarted.load_artifact("model-v1") == artifact
+
+
+def test_registry_replaces_persisted_artifact_after_restart(tmp_path: Path) -> None:
+    registry = ModelRegistry(tmp_path)
+    registry.register(TrainedArtifact("model-v1", ("sensor.kitchen",)))
+    replacement = TrainedArtifact("model-v1", ("sensor.bedroom",))
+
+    registry.register(replacement)
+
+    assert registry.load_artifact("model-v1") == replacement
+    assert ModelRegistry(tmp_path).load_artifact("model-v1") == replacement
+
+
+@pytest.mark.parametrize("artifact_id", ["../escape", "nested/model", "..", ""])
+def test_registry_rejects_unsafe_artifact_ids(tmp_path: Path, artifact_id: str) -> None:
+    registry = ModelRegistry(tmp_path)
+
+    with pytest.raises(ValueError):
+        registry.register(TrainedArtifact(artifact_id, ("sensor.kitchen",)))
+
+    assert list(tmp_path.parent.glob("escape.json")) == []
+
+
+def test_registry_rejects_corrupt_persisted_artifact(tmp_path: Path) -> None:
+    (tmp_path / "broken.json").write_text(
+        json.dumps({"artifact_id": "../broken", "supported_sensors": []}),
+        encoding="utf-8",
+    )
+
+    with pytest.raises(ValueError, match="broken.json"):
+        ModelRegistry(tmp_path)

tests/ml/test_predictor.py

@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+import pytest
+
+from app.ml.feature_store import FeatureStore, FeatureVector
+from app.ml.predictor import Predictor
+from app.ml.training import TrainingPipeline
+
+
+def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
+    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
+
+
+def predictor() -> Predictor:
+    store = FeatureStore()
+    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
+    pipeline = TrainingPipeline(store)
+    pipeline.run("artifact_v1")
+    return Predictor(pipeline)
+
+
+def test_predict_returns_expected_format() -> None:
+    p = predictor()
+    result = p.predict("artifact_v1", _vector("sensor.kitchen", 21.0))
+    assert result == "artifact_v1:sensor.kitchen:{'temperature': 21.0}"
+
+
+def test_predict_rejects_unknown_sensor() -> None:
+    p = predictor()
+    with pytest.raises(ValueError):
+        p.predict("artifact_v1", _vector("sensor.unknown", 10.0))
+
+
+def test_predict_batch_matches_single_calls() -> None:
+    p = predictor()
+    entities = [_vector("sensor.kitchen", 21.0), _vector("sensor.bedroom", 19.0)]
+    assert p.predict_batch("artifact_v1", entities) == [
+        p.predict("artifact_v1", item) for item in entities
+    ]
+
+
+def test_default_artifact_returns_last_registered() -> None:
+    store = FeatureStore()
+    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
+    pipeline = TrainingPipeline(store)
+    pipeline.run("first")
+    pipeline.run("second")
+    assert Predictor.default_artifact(pipeline).artifact_id == "second"

tests/ml/test_retraining.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from app.ml.feature_store import FeatureVector
+from app.ml.registry.model_registry import ModelRegistry
+from app.ml.retraining import RetrainingService, retrain_model
+
+
+def _vector(sensor_id: str) -> FeatureVector:
+    return FeatureVector(sensor_id=sensor_id, values={"temperature": 21.0})
+
+
+def test_retraining_registers_new_artifact(tmp_path: Path) -> None:
+    registry = ModelRegistry(tmp_path)
+
+    result = retrain_model(registry, "home-model", [_vector("sensor.kitchen")])
+
+    assert result.replaced is False
+    assert registry.load_artifact("home-model") == result.artifact
+
+
+def test_retraining_replaces_existing_artifact(tmp_path: Path) -> None:
+    registry = ModelRegistry(tmp_path)
+    service = RetrainingService(registry)
+    service.retrain("home-model", [_vector("sensor.kitchen")])
+
+    result = service.retrain("home-model", [_vector("sensor.bedroom")])
+
+    assert result.replaced is True
+    assert result.artifact.supported_sensors == ("sensor.bedroom",)
+    assert ModelRegistry(tmp_path).load_artifact("home-model") == result.artifact
+
+
+def test_retraining_rejects_empty_training_data(tmp_path: Path) -> None:
+    with pytest.raises(ValueError, match="keine Trainingsdaten"):
+        retrain_model(ModelRegistry(tmp_path), "home-model", [])

tests/ml/test_training.py

@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+import pytest
+
+from app.ml.feature_store import FeatureStore, FeatureVector
+from app.ml.training import TrainingPipeline
+
+
+def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
+    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
+
+
+def store_with_data() -> TrainingPipeline:
+    store = FeatureStore()
+    store.add_batch(
+        [
+            _vector("sensor.kitchen", 19.0),
+            _vector("sensor.kitchen", 20.0),
+            _vector("sensor.bedroom", 18.5),
+        ]
+    )
+    return TrainingPipeline(store)
+
+
+def test_run_returns_trained_artifact() -> None:
+    pipeline = store_with_data()
+    artifact = pipeline.run("artifact_v1")
+    assert artifact.artifact_id == "artifact_v1"
+    assert artifact.supported_sensors == ("sensor.bedroom", "sensor.kitchen")
+
+
+def test_run_without_data_raises_value_error() -> None:
+    pipeline = TrainingPipeline(FeatureStore())
+    with pytest.raises(ValueError):
+        pipeline.run("artifact_v1")
+
+
+def test_export_returns_registered_artifact() -> None:
+    pipeline = store_with_data()
+    pipeline.run("artifact_v1")
+    exported = pipeline.export("artifact_v1")
+    assert exported == pipeline.export("artifact_v1")
+
+
+def test_export_missing_artifact_raises_key_error() -> None:
+    pipeline = store_with_data()
+    with pytest.raises(KeyError):
+        pipeline.export("artifact_v1")

tests/ml/test_training_evaluation.py

@@ -0,0 +1,33 @@
+from __future__ import annotations
+
+from app.ml.evaluation import Evaluator, EvalReport, Metric
+from app.ml.feature_store import FeatureStore, FeatureVector
+from app.ml.training import TrainingPipeline
+
+
+def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
+    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
+
+
+def test_end_to_end_training_then_evaluation() -> None:
+    store = FeatureStore()
+    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
+    pipeline = TrainingPipeline(store)
+    artifact = pipeline.run("artifact_v1")
+
+    evaluator = Evaluator(pipeline)
+    predictions = [
+        "artifact_v1:sensor.kitchen:{'temperature': 21.0}",
+        "artifact_v1:sensor.bedroom:{'temperature': 18.5}",
+    ]
+    report = evaluator.evaluate(artifact.artifact_id, predictions)
+    assert isinstance(report, EvalReport)
+    assert report.sample_size == len(predictions)
+    assert any(metric.name == "coverage" for metric in report.metrics)
+
+
+def test_metric_helpers_are_serializable() -> None:
+    metric = Metric(name="coverage", value=0.85, threshold=0.8)
+    assert metric.name == "coverage"
+    assert metric.value == 0.85
+    assert metric.threshold == 0.8

tests/rules/test_heating.py

@@ -1,26 +1,64 @@
 from __future__ import annotations
 
+import pytest
+
 from app.ha.models import HaEntitySummary
 from app.rules.heating import HeatingRule
-from app.rules.recommender import Recommender
 
 
-def _sensor(entity_id: str) -> HaEntitySummary:
-    return HaEntitySummary(entity_id=entity_id, domain="sensor")
+def _entity(entity_id: str, domain: str, device_class: str | None = None) -> HaEntitySummary:
+    return HaEntitySummary(entity_id=entity_id, domain=domain, device_class=device_class)
 
 
-def _climate(entity_id: str) -> HaEntitySummary:
-    return HaEntitySummary(entity_id=entity_id, domain="climate")
-
-
-def test_heating_rule_triggers() -> None:
+# --- positive cases --------------------------------------------------------
+@pytest.mark.parametrize(
+    "entity",
+    [
+        _entity("climate.living_room", "climate"),
+        _entity("sensor.temperature_living", "sensor", "temperature"),
+        _entity("sensor.humidity_bathroom", "sensor", "humidity"),
+        _entity("binary_sensor.living_room_occupancy", "binary_sensor", "occupancy"),
+        _entity("binary_sensor.entrance_presence", "binary_sensor", "presence"),
+    ],
+    ids=lambda e: e.entity_id,
+)
+def test_heating_rule_triggers_for_relevant_entities(entity: HaEntitySummary) -> None:
     rule = HeatingRule()
-    assert rule.matches([_climate("climate.living_room")])
-    assert rule.matches([_sensor("sensor.temperature_living")])
+    assert rule.matches([entity]) is True
 
 
-def test_recommender_uses_rule() -> None:
-    recommender = Recommender(rules=[HeatingRule()])
-    assert recommender.run([_climate("climate.living_room")]) == [
-        "Prüfe Heizungsregelung: Aktiviere energiesparenden Modus bei Abwesenheit."
-    ]
+# --- negative cases -------------------------------------------------------
+@pytest.mark.parametrize(
+    "entity",
+    [
+        _entity("sensor.power_consumption", "sensor", "power"),
+        _entity("sensor.door", "sensor", "door"),
+        _entity("sensor.energy", "sensor", "energy"),
+        _entity("binary_sensor.door_window", "binary_sensor", "door"),
+        _entity("binary_sensor.motion", "binary_sensor", "motion"),
+        _entity("light.living_room", "light"),
+        _entity("switch.plug", "switch"),
+        _entity("sensor.some_random", "sensor"),
+        _entity("binary_sensor.some_binary", "binary_sensor"),
+    ],
+    ids=lambda e: e.entity_id,
+)
+def test_heating_rule_ignores_non_heating_entities(entity: HaEntitySummary) -> None:
+    rule = HeatingRule()
+    assert rule.matches([entity]) is False
+
+
+def test_heating_rule_mixed_list_returns_true() -> None:
+    rule = HeatingRule()
+    entities = [
+        _entity("sensor.power", "sensor", "power"),
+        _entity("climate.living_room", "climate"),
+        _entity("light.ceiling", "light"),
+    ]
+    assert rule.matches(entities) is True
+
+
+def test_heating_rule_recommendation_is_stable() -> None:
+    rule = HeatingRule()
+    expected = "Prüfe Heizungsregelung: Aktiviere energiesparenden Modus bei Abwesenheit."
+    assert rule.recommendation([_entity("climate.living_room", "climate")]) == expected

tests/test_config.py

@@ -0,0 +1,18 @@
+from __future__ import annotations
+
+from pytest import MonkeyPatch
+
+from app.config import load_settings
+
+
+def test_load_settings_reads_documented_environment(monkeypatch: MonkeyPatch) -> None:
+    monkeypatch.setenv("SILLYHOME_HA_URL", "http://ha.local:8123")
+    monkeypatch.setenv("SILLYHOME_HA_TOKEN", "secret")
+    monkeypatch.setenv("SILLYHOME_MODEL_STORE", "/tmp/models")
+
+    settings = load_settings()
+
+    assert settings.ha_url == "http://ha.local:8123"
+    assert settings.ha_token == "secret"
+    assert settings.model_store == "/tmp/models"
+    assert settings.ha_configured

tests/test_health.py

@@ -1,10 +1,10 @@
 from fastapi.testclient import TestClient
-from app.main import app
 
-client = TestClient(app)
+from app.main import app
 
 
 def test_health_returns_ok() -> None:
-    response = client.get("/health")
+    with TestClient(app) as client:
+        response = client.get("/health")
     assert response.status_code == 200
     assert response.json() == {"status": "ok"}