OPS-001: persist HA panel and rollback instructions

Closes #21

.env.example

@@ -1,3 +1,4 @@
 SILLYHOME_HA_URL=http://homeassistant.local:8123
 SILLYHOME_HA_TOKEN=REPLACE_ME_WITH_LONG_LIVED_TOKEN
 SILLYHOME_MODEL_STORE=.model_store
+SILLYHOME_AUTOMATION_STORE=.automation_store

CHANGELOG.md

@@ -1,9 +1,15 @@
 # Changelog
 
 ## Unreleased
+- Deterministische, nutzerverständliche Erklärungen für jede Modellvorhersage
+- Persistenter Automation-Freigabeprozess mit sicherem YAML-Export
+
+## 0.2.0 - 2026-06-13
 - Klassifizierte Home-Assistant-Entity-Discovery mit Lernrelevanz und Filtern
 - Validierter Zugriff auf die Home-Assistant-History-API
 - Normalisierte, chronologisch sortierte numerische Zeitreihen über `/v1/history`
+- Trainierbares statistisches Baseline-Modell mit persistierten Parametern
+- Numerische Vorhersagen mit Confidence sowie MAE-/RMSE-Evaluation
 
 ## 0.1.0 - 2026-06-13
 - Projektinitiierung

Dockerfile

@@ -4,6 +4,7 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
     PIP_NO_CACHE_DIR=1 \
     SILLYHOME_MODEL_STORE=/app/data/models
+ENV SILLYHOME_AUTOMATION_STORE=/app/data/automations
 
 WORKDIR /app
 
@@ -14,7 +15,7 @@ COPY app ./app
 COPY backend ./backend
 RUN python -m pip install --upgrade pip && \
     python -m pip install . && \
-    mkdir -p /app/data/models && \
+    mkdir -p /app/data/models /app/data/automations && \
     chown -R sillyhome:sillyhome /app/data
 
 EXPOSE 8000

README.md

@@ -4,10 +4,11 @@ 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**.
+Die aktuelle Entwicklungslinie stellt eine gehärtete technische Basis bereit:
+Home-Assistant-Entities und Historie lesen, Sensoren klassifizieren,
+regelbasierte Bausteine sowie ein lokal trainierbares statistisches
+Baseline-Modell mit persistenter Registry, Confidence und echten
+Evaluationsmetriken.
 
 ## 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.
@@ -39,6 +40,7 @@ uvicorn app.main:app --reload
 ```
 
 4. Erreichbar unter:
+- `http://127.0.0.1:8000/` - lokales Dashboard
 - `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
@@ -46,6 +48,8 @@ uvicorn app.main:app --reload
 - `http://127.0.0.1:8000/v1/history` - normalisierte numerische Zeitreihen
 - `http://127.0.0.1:8000/ml/health` - Registry-/Serving-Health
 - `POST http://127.0.0.1:8000/ml/retrain` - Modell-Metadaten aktualisieren
+- `POST http://127.0.0.1:8000/ml/evaluate` - MAE/RMSE/Coverage berechnen
+- `POST http://127.0.0.1:8000/v1/automations/proposals` - sicheren Entwurf anlegen
 
 Ohne vollständige HA-Konfiguration liefert `/v1/entities` bewusst `503`.
 
@@ -64,10 +68,27 @@ dem Netz muss ein authentifizierender Reverse Proxy vorgeschaltet werden.
 - `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
+- `SILLYHOME_AUTOMATION_STORE` – Verzeichnis für Automation-Entwürfe
 
 Niemals Administrator-Tokens oder Passwörter eintragen. `.env` gehört nicht ins
 Versionskontrollsystem.
 
+### Home-Assistant-Add-on
+
+Das Repository ist zugleich ein Home-Assistant-Add-on-Repository. In Home Assistant
+unter **Einstellungen → Add-ons → Add-on-Shop → Repositories** diese URL eintragen:
+
+`http://192.168.6.31:3000/pino/sillyhome-next`
+
+Danach **SillyHome Next** installieren und starten. Das Dashboard wird per Ingress
+geöffnet. Das Add-on nutzt die Supervisor-API nur lesend; Automation-Entwürfe werden
+lokal gespeichert und niemals automatisch ausgeführt.
+
+Vor einem Update sollte in Home Assistant unter **Einstellungen → System → Backups**
+eine Teil-Sicherung des Add-ons erstellt werden. Zur Wiederherstellung das gewünschte
+Backup öffnen, **SillyHome Next** auswählen und wiederherstellen. Der erste produktive
+Teststand `v0.3.0` wurde als HA-Backup `7df0fca0` gesichert.
+
 ### Tests
 ```bash
 pytest

addon/Dockerfile

@@ -0,0 +1,19 @@
+FROM python:3.13-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1
+
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends git \
+    && git clone --depth 1 --branch main \
+       http://192.168.6.31:3000/pino/sillyhome-next.git /app \
+    && python -m pip install --upgrade pip \
+    && python -m pip install /app \
+    && rm -rf /var/lib/apt/lists/* /app/.git
+
+COPY run.sh /run.sh
+RUN chmod 0755 /run.sh
+
+EXPOSE 8000
+CMD ["/run.sh"]

addon/config.yaml

@@ -0,0 +1,23 @@
+name: SillyHome Next
+version: "0.3.0"
+slug: sillyhome_next
+description: Lokale HA-Analyse, Vorhersagen und sichere Automation-Entwürfe
+url: http://192.168.6.31:3000/pino/sillyhome-next
+arch:
+  - amd64
+startup: application
+boot: auto
+init: false
+ingress: true
+ingress_port: 8000
+panel_title: SillyHome Next
+panel_icon: mdi:home-analytics
+panel_admin: true
+homeassistant_api: true
+hassio_api: false
+auth_api: false
+options: {}
+schema: {}
+map:
+  - type: addon_config
+    read_only: false

addon/run.sh

@@ -0,0 +1,11 @@
+#!/bin/sh
+set -eu
+
+export SILLYHOME_HA_URL="${SILLYHOME_HA_URL:-http://supervisor/core}"
+export SILLYHOME_HA_TOKEN="${SILLYHOME_HA_TOKEN:-${SUPERVISOR_TOKEN:-}}"
+export SILLYHOME_MODEL_STORE=/data/models
+export SILLYHOME_AUTOMATION_STORE=/data/automations
+
+mkdir -p "$SILLYHOME_MODEL_STORE" "$SILLYHOME_AUTOMATION_STORE"
+exec uvicorn app.main:app --app-dir /app --host 0.0.0.0 --port 8000 \
+  --proxy-headers --forwarded-allow-ips='*'

app/api/v1/automations.py

@@ -0,0 +1,77 @@
+from __future__ import annotations
+
+from fastapi import APIRouter, HTTPException, Request, Response, status
+
+from app.automations.models import (
+    AutomationProposal,
+    ProposalDecision,
+    ProposalStatus,
+)
+from app.automations.store import AutomationStore
+
+router = APIRouter(prefix="/v1/automations", tags=["automations"])
+
+
+@router.post("/proposals", response_model=AutomationProposal, status_code=201)
+def create_proposal(payload: AutomationProposal, request: Request) -> AutomationProposal:
+    if payload.trigger.above is None and payload.trigger.below is None:
+        raise HTTPException(status_code=422, detail="Trigger benötigt above oder below.")
+    return _store(request).create(payload.model_copy(update={"status": ProposalStatus.DRAFT}))
+
+
+@router.get("/proposals", response_model=list[AutomationProposal])
+def list_proposals(request: Request) -> list[AutomationProposal]:
+    return _store(request).list()
+
+
+@router.post("/proposals/{proposal_id}/approve", response_model=AutomationProposal)
+def approve(
+    proposal_id: str,
+    payload: ProposalDecision,
+    request: Request,
+) -> AutomationProposal:
+    return _decide(request, proposal_id, ProposalStatus.APPROVED, payload.expected_revision)
+
+
+@router.post("/proposals/{proposal_id}/reject", response_model=AutomationProposal)
+def reject(
+    proposal_id: str,
+    payload: ProposalDecision,
+    request: Request,
+) -> AutomationProposal:
+    return _decide(request, proposal_id, ProposalStatus.REJECTED, payload.expected_revision)
+
+
+@router.get("/proposals/{proposal_id}/yaml")
+def export_yaml(proposal_id: str, request: Request) -> Response:
+    try:
+        content = _store(request).export_yaml(proposal_id)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=409, detail=str(exc)) from exc
+    return Response(content=content, media_type="application/yaml")
+
+
+def _decide(
+    request: Request,
+    proposal_id: str,
+    decision: ProposalStatus,
+    expected_revision: int,
+) -> AutomationProposal:
+    try:
+        return _store(request).decide(proposal_id, decision, expected_revision)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=409, detail=str(exc)) from exc
+
+
+def _store(request: Request) -> AutomationStore:
+    store = getattr(request.app.state, "automation_store", None)
+    if not isinstance(store, AutomationStore):
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Automation Store nicht initialisiert.",
+        )
+    return store

app/automations/__init__.py

@@ -0,0 +1,3 @@
+from app.automations.store import AutomationStore
+
+__all__ = ["AutomationStore"]

app/automations/models.py

@@ -0,0 +1,41 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from enum import StrEnum
+from uuid import uuid4
+
+from pydantic import BaseModel, Field
+
+
+class ProposalStatus(StrEnum):
+    DRAFT = "draft"
+    APPROVED = "approved"
+    REJECTED = "rejected"
+
+
+class NumericStateTrigger(BaseModel):
+    entity_id: str = Field(pattern=r"^sensor\.[a-z0-9_]+$")
+    above: float | None = None
+    below: float | None = None
+
+
+class ServiceAction(BaseModel):
+    service: str = Field(pattern=r"^(light|switch|climate|fan|cover)\.[a-z0-9_]+$")
+    entity_id: str = Field(pattern=r"^(light|switch|climate|fan|cover)\.[a-z0-9_]+$")
+    data: dict[str, str | int | float | bool] = Field(default_factory=dict)
+
+
+class AutomationProposal(BaseModel):
+    proposal_id: str = Field(default_factory=lambda: uuid4().hex)
+    alias: str = Field(min_length=1, max_length=120)
+    description: str = Field(min_length=1, max_length=500)
+    trigger: NumericStateTrigger
+    action: ServiceAction
+    status: ProposalStatus = ProposalStatus.DRAFT
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    revision: int = 1
+
+
+class ProposalDecision(BaseModel):
+    expected_revision: int = Field(ge=1)

app/automations/store.py

@@ -0,0 +1,124 @@
+from __future__ import annotations
+
+import json
+import os
+from datetime import datetime, timezone
+from pathlib import Path
+from threading import RLock
+
+from app.automations.models import AutomationProposal, ProposalStatus
+
+
+class AutomationStore:
+    def __init__(self, root: str | Path) -> None:
+        self._root = Path(root).resolve()
+        self._root.mkdir(parents=True, exist_ok=True)
+        self._lock = RLock()
+
+    def create(self, proposal: AutomationProposal) -> AutomationProposal:
+        with self._lock:
+            target = self._target(proposal.proposal_id)
+            if target.exists():
+                raise ValueError("Automation-Vorschlag existiert bereits.")
+            self._persist(proposal)
+        return proposal
+
+    def list(self) -> list[AutomationProposal]:
+        with self._lock:
+            return [self._load(path) for path in sorted(self._root.glob("*.json"))]
+
+    def get(self, proposal_id: str) -> AutomationProposal:
+        with self._lock:
+            target = self._target(proposal_id)
+            if not target.exists():
+                raise KeyError("Automation-Vorschlag nicht gefunden.")
+            return self._load(target)
+
+    def decide(
+        self,
+        proposal_id: str,
+        status: ProposalStatus,
+        expected_revision: int,
+    ) -> AutomationProposal:
+        if status is ProposalStatus.DRAFT:
+            raise ValueError("Entscheidung darf nicht auf draft gesetzt werden.")
+        with self._lock:
+            proposal = self.get(proposal_id)
+            if proposal.revision != expected_revision:
+                raise ValueError("Revision stimmt nicht mit dem aktuellen Vorschlag überein.")
+            if proposal.status is not ProposalStatus.DRAFT:
+                raise ValueError("Über den Vorschlag wurde bereits entschieden.")
+            updated = proposal.model_copy(
+                update={
+                    "status": status,
+                    "updated_at": datetime.now(timezone.utc),
+                    "revision": proposal.revision + 1,
+                }
+            )
+            self._persist(updated)
+            return updated
+
+    def export_yaml(self, proposal_id: str) -> str:
+        proposal = self.get(proposal_id)
+        if proposal.status is not ProposalStatus.APPROVED:
+            raise ValueError("Nur freigegebene Vorschläge dürfen exportiert werden.")
+        trigger_lines = [
+            "trigger:",
+            "  - platform: numeric_state",
+            f"    entity_id: {proposal.trigger.entity_id}",
+        ]
+        if proposal.trigger.above is not None:
+            trigger_lines.append(f"    above: {proposal.trigger.above}")
+        if proposal.trigger.below is not None:
+            trigger_lines.append(f"    below: {proposal.trigger.below}")
+        action_lines = [
+            "action:",
+            f"  - service: {proposal.action.service}",
+            "    target:",
+            f"      entity_id: {proposal.action.entity_id}",
+        ]
+        if proposal.action.data:
+            action_lines.append("    data:")
+            action_lines.extend(
+                f"      {key}: {_yaml_scalar(value)}"
+                for key, value in sorted(proposal.action.data.items())
+            )
+        return "\n".join(
+            [
+                f"alias: {_yaml_scalar(proposal.alias)}",
+                f"description: {_yaml_scalar(proposal.description)}",
+                *trigger_lines,
+                *action_lines,
+                "mode: single",
+                "",
+            ]
+        )
+
+    def _target(self, proposal_id: str) -> Path:
+        if len(proposal_id) != 32 or not proposal_id.isalnum():
+            raise ValueError("Ungültige proposal_id.")
+        return self._root / f"{proposal_id}.json"
+
+    def _persist(self, proposal: AutomationProposal) -> None:
+        target = self._target(proposal.proposal_id)
+        temporary = target.with_suffix(".json.tmp")
+        temporary.write_text(
+            json.dumps(proposal.model_dump(mode="json"), ensure_ascii=True, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        os.replace(temporary, target)
+
+    @staticmethod
+    def _load(path: Path) -> AutomationProposal:
+        try:
+            return AutomationProposal.model_validate_json(path.read_text(encoding="utf-8"))
+        except ValueError as exc:
+            raise ValueError(f"Ungültiger Automation-Vorschlag: {path.name}") from exc
+
+
+def _yaml_scalar(value: str | int | float | bool) -> str:
+    if isinstance(value, bool):
+        return "true" if value else "false"
+    if isinstance(value, (int, float)):
+        return str(value)
+    return json.dumps(value, ensure_ascii=True)

app/config.py

@@ -9,6 +9,7 @@ class Settings:
     ha_url: str | None = None
     ha_token: str | None = None
     model_store: str = ".model_store"
+    automation_store: str = ".automation_store"
 
     @property
     def ha_configured(self) -> bool:
@@ -20,4 +21,5 @@ def load_settings() -> 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"),
+        automation_store=os.getenv("SILLYHOME_AUTOMATION_STORE", ".automation_store"),
     )

app/main.py

@@ -1,10 +1,15 @@
 from contextlib import asynccontextmanager
 from collections.abc import AsyncIterator
+from pathlib import Path
 from typing import cast
 
 from fastapi import FastAPI
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
 
 from app.api.v1.entities import router as entities_router
+from app.api.v1.automations import router as automations_router
+from app.automations.store import AutomationStore
 from app.config import load_settings
 from app.core.exception_handlers import register_exception_handlers
 from app.ha.client import HaClient, HaClientSettings
@@ -18,6 +23,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
     settings = app.state.settings
     client: HaClient | None = None
     app.state.registry = ModelRegistry(settings.model_store)
+    app.state.automation_store = AutomationStore(settings.automation_store)
     if hasattr(app.state, "ha_reader"):
         del app.state.ha_reader
     if settings.ha_configured:
@@ -38,14 +44,18 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
     title="SillyHome Next API",
     description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
-    version="0.1.0",
+    version="0.3.0",
     lifespan=lifespan,
 )
 app.state.settings = load_settings()
 register_exception_handlers(app)
 app.include_router(entities_router)
+app.include_router(automations_router)
 init_ml_routes(app, model_store=app.state.settings.model_store)
 
+STATIC_DIR = Path(__file__).with_name("static")
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
 
 @app.get("/health")
 def health() -> dict[str, str]:
@@ -53,5 +63,5 @@ def health() -> dict[str, str]:
 
 
 @app.get("/")
-def root() -> dict[str, str]:
-    return {"service": "sillyhome-next", "docs": "/docs"}
+def root() -> FileResponse:
+    return FileResponse(STATIC_DIR / "index.html")

app/ml/__init__.py

@@ -3,6 +3,10 @@
 __all__ = [
     "FeatureStore",
     "FeatureVector",
+    "FeatureModel",
+    "FeatureExplanation",
+    "PredictionResult",
+    "Predictor",
     "RetrainingResult",
     "RetrainingService",
     "TrainedArtifact",
@@ -10,5 +14,7 @@ __all__ = [
     "retrain_model",
 ]
 from app.ml.feature_store import FeatureStore, FeatureVector
+from app.ml.explanation import FeatureExplanation
+from app.ml.predictor import PredictionResult, Predictor
 from app.ml.retraining import RetrainingResult, RetrainingService, retrain_model
-from app.ml.training import TrainedArtifact, TrainingPipeline
+from app.ml.training import FeatureModel, TrainedArtifact, TrainingPipeline

app/ml/evaluation.py

@@ -1,9 +1,13 @@
 from __future__ import annotations
 
 import logging
+import math
 from collections.abc import Sequence
 from dataclasses import dataclass
 
+from app.ml.feature_store import FeatureVector
+from app.ml.predictor import Predictor
+from app.ml.registry.model_registry import ModelRegistry
 from app.ml.training import TrainingPipeline
 
 logger = logging.getLogger(__name__)
@@ -24,41 +28,62 @@ class EvalReport:
 
 
 class Evaluator:
-    def __init__(self, pipeline: TrainingPipeline) -> None:
+    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("Evaluator erfordert TrainingPipeline oder ModelRegistry.")
         self._pipeline = pipeline
+        self._registry = registry
+        self._predictor = Predictor(pipeline=pipeline, registry=registry)
 
-    def evaluate(self, artifact_id: str, predictions: Sequence[str]) -> EvalReport:
+    def evaluate(self, artifact_id: str, samples: Sequence[FeatureVector]) -> EvalReport:
         try:
-            supported_sensors = set(self._pipeline.export(artifact_id).supported_sensors)
+            if self._registry is not None:
+                self._registry.load_artifact(artifact_id)
+            elif self._pipeline is not None:
+                self._pipeline.export(artifact_id)
         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
+        absolute_errors: list[float] = []
+        squared_errors: list[float] = []
+        for sample in samples:
+            try:
+                prediction = self._predictor.predict(artifact_id, sample)
+            except ValueError:
+                continue
+            for feature_name, predicted in prediction.predictions.items():
+                actual = float(sample.values[feature_name])
+                error = predicted - actual
+                absolute_errors.append(abs(error))
+                squared_errors.append(error**2)
 
-        coverage_metric = Metric(name="coverage", value=coverage, threshold=0.8)
-        unknown_metric = Metric(name="unknown_rate", value=unknown_rate, threshold=0.1)
+        sample_size = len(absolute_errors)
+        mae = sum(absolute_errors) / sample_size if sample_size else 0.0
+        rmse = math.sqrt(sum(squared_errors) / sample_size) if sample_size else 0.0
+        expected_values = sum(len(sample.values) for sample in samples)
+        coverage = sample_size / expected_values if expected_values else 0.0
 
         report = EvalReport(
             artifact_id=artifact_id,
             sample_size=sample_size,
-            metrics=[coverage_metric, unknown_metric],
+            metrics=[
+                Metric(name="mae", value=mae),
+                Metric(name="rmse", value=rmse),
+                Metric(name="coverage", value=coverage, threshold=0.8),
+            ],
         )
         logger.info(
-            "Evaluation %s -> coverage=%.2f, unknown_rate=%.2f",
+            "Evaluation %s -> mae=%.4f, rmse=%.4f, coverage=%.2f",
             artifact_id,
+            mae,
+            rmse,
             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/explanation.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from app.ml.training import FeatureModel
+
+
+@dataclass(frozen=True)
+class FeatureExplanation:
+    feature: str
+    current_value: float
+    predicted_value: float
+    change: float
+    direction: str
+    sample_count: int
+    historical_mean: float
+    historical_range: tuple[float, float]
+    standard_deviation: float
+    trend_per_step: float
+    confidence: float
+    summary: str
+
+
+def explain_feature(
+    feature_name: str,
+    current_value: float,
+    predicted_value: float,
+    model: FeatureModel,
+) -> FeatureExplanation:
+    change = predicted_value - current_value
+    direction = _direction(change)
+    summary = (
+        f"{feature_name}: {direction}; Prognose {predicted_value:.3f} "
+        f"aus aktuellem Wert {current_value:.3f} und Trend {model.slope:+.3f}. "
+        f"Basis: {model.sample_count} Messwerte, Mittelwert {model.mean:.3f}, "
+        f"Confidence {model.confidence:.0%}."
+    )
+    return FeatureExplanation(
+        feature=feature_name,
+        current_value=current_value,
+        predicted_value=predicted_value,
+        change=change,
+        direction=direction,
+        sample_count=model.sample_count,
+        historical_mean=model.mean,
+        historical_range=(model.minimum, model.maximum),
+        standard_deviation=model.standard_deviation,
+        trend_per_step=model.slope,
+        confidence=model.confidence,
+        summary=summary,
+    )
+
+
+def _direction(change: float) -> str:
+    if abs(change) < 1e-12:
+        return "stabil"
+    return "steigend" if change > 0 else "fallend"

app/ml/predictor.py

@@ -1,8 +1,11 @@
 from __future__ import annotations
 
 import logging
+import math
+from dataclasses import dataclass
 from typing import Sequence
 
+from app.ml.explanation import FeatureExplanation, explain_feature
 from app.ml.feature_store import FeatureVector
 from app.ml.registry.model_registry import ModelRegistry
 from app.ml.training import TrainedArtifact, TrainingPipeline
@@ -10,6 +13,16 @@ from app.ml.training import TrainedArtifact, TrainingPipeline
 logger = logging.getLogger(__name__)
 
 
+@dataclass(frozen=True)
+class PredictionResult:
+    artifact_id: str
+    sensor_id: str
+    predictions: dict[str, float]
+    confidence: float
+    model_type: str
+    explanations: dict[str, FeatureExplanation]
+
+
 class Predictor:
     def __init__(
         self,
@@ -24,15 +37,54 @@ class Predictor:
         self._pipeline = pipeline
         self._registry = registry
 
-    def predict(self, artifact_id: str, entity: FeatureVector) -> str:
+    def predict(self, artifact_id: str, entity: FeatureVector) -> PredictionResult:
         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}"
+        sensor_models = artifact.feature_models.get(entity.sensor_id, {})
+        if not sensor_models:
+            raise ValueError(f"Modell '{artifact_id}' enthält keine statistischen Parameter.")
 
-    def predict_batch(self, artifact_id: str, entities: Sequence[FeatureVector]) -> list[str]:
+        feature_names = sorted(set(sensor_models).intersection(entity.values))
+        if not feature_names:
+            raise ValueError(
+                f"Keine Eingabemerkmale werden vom Modell '{artifact_id}' unterstützt."
+            )
+
+        predictions: dict[str, float] = {}
+        explanations: dict[str, FeatureExplanation] = {}
+        confidences: list[float] = []
+        for feature_name in feature_names:
+            model = sensor_models[feature_name]
+            current_value = float(entity.values[feature_name])
+            if not math.isfinite(current_value):
+                raise ValueError("Vorhersagewerte müssen endlich sein.")
+            predicted_value = model.forecast(current_value)
+            predictions[feature_name] = predicted_value
+            explanations[feature_name] = explain_feature(
+                feature_name,
+                current_value,
+                predicted_value,
+                model,
+            )
+            confidences.append(model.confidence)
+
+        return PredictionResult(
+            artifact_id=artifact_id,
+            sensor_id=entity.sensor_id,
+            predictions=predictions,
+            confidence=sum(confidences) / len(confidences),
+            model_type=artifact.model_type,
+            explanations=explanations,
+        )
+
+    def predict_batch(
+        self,
+        artifact_id: str,
+        entities: Sequence[FeatureVector],
+    ) -> list[PredictionResult]:
         return [self.predict(artifact_id, entity) for entity in entities]
 
     @staticmethod
@@ -47,4 +99,4 @@ class Predictor:
             return self._registry.load_artifact(artifact_id)
         if self._pipeline is not None:
             return self._pipeline.export(artifact_id)
-        raise RuntimeError("Predictor nicht initialisiert.")
+        raise RuntimeError("Predictor nicht initialisiert.")

app/ml/registry/model_registry.py

@@ -2,13 +2,14 @@ from __future__ import annotations
 
 import json
 import logging
+import math
 import os
 from pathlib import Path
 import re
 from threading import RLock
 from collections.abc import Iterable
 
-from app.ml.training import TrainedArtifact
+from app.ml.training import FeatureModel, TrainedArtifact
 
 logger = logging.getLogger(__name__)
 
@@ -52,19 +53,26 @@ class ModelRegistry:
                 raw = json.loads(source.read_text(encoding="utf-8"))
                 artifact_id = raw["artifact_id"]
                 supported_sensors = raw["supported_sensors"]
+                model_type = raw.get("model_type", "metadata")
+                raw_feature_models = raw.get("feature_models", {})
                 if not isinstance(artifact_id, str) or not isinstance(supported_sensors, list):
                     raise ValueError("invalid artifact structure")
+                if not isinstance(model_type, str):
+                    raise ValueError("model_type must be a string")
                 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")
+                feature_models = _deserialize_feature_models(raw_feature_models)
             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),
+                feature_models=feature_models,
+                model_type=model_type,
             )
 
     def _persist(self, artifact: TrainedArtifact) -> None:
@@ -73,6 +81,22 @@ class ModelRegistry:
         payload = {
             "artifact_id": artifact.artifact_id,
             "supported_sensors": list(artifact.supported_sensors),
+            "model_type": artifact.model_type,
+            "feature_models": {
+                sensor_id: {
+                    feature_name: {
+                        "sample_count": model.sample_count,
+                        "mean": model.mean,
+                        "standard_deviation": model.standard_deviation,
+                        "minimum": model.minimum,
+                        "maximum": model.maximum,
+                        "slope": model.slope,
+                        "intercept": model.intercept,
+                    }
+                    for feature_name, model in sorted(models.items())
+                }
+                for sensor_id, models in sorted(artifact.feature_models.items())
+            },
         }
         temporary.write_text(
             json.dumps(payload, ensure_ascii=True, sort_keys=True) + "\n",
@@ -88,3 +112,51 @@ class ModelRegistry:
                 "artifact_id darf nur Buchstaben, Ziffern, Punkt, Unterstrich "
                 "und Bindestrich enthalten."
             )
+
+
+def _deserialize_feature_models(raw: object) -> dict[str, dict[str, FeatureModel]]:
+    if not isinstance(raw, dict):
+        raise ValueError("feature_models must be an object")
+
+    result: dict[str, dict[str, FeatureModel]] = {}
+    for sensor_id, raw_features in raw.items():
+        if not isinstance(sensor_id, str) or not isinstance(raw_features, dict):
+            raise ValueError("invalid sensor feature models")
+        features: dict[str, FeatureModel] = {}
+        for feature_name, raw_model in raw_features.items():
+            if not isinstance(feature_name, str) or not isinstance(raw_model, dict):
+                raise ValueError("invalid feature model")
+            sample_count = raw_model.get("sample_count")
+            if not isinstance(sample_count, int) or isinstance(sample_count, bool) or sample_count < 1:
+                raise ValueError("sample_count must be a positive integer")
+            values = {
+                key: _finite_number(raw_model.get(key))
+                for key in (
+                    "mean",
+                    "standard_deviation",
+                    "minimum",
+                    "maximum",
+                    "slope",
+                    "intercept",
+                )
+            }
+            features[feature_name] = FeatureModel(
+                sample_count=sample_count,
+                mean=values["mean"],
+                standard_deviation=values["standard_deviation"],
+                minimum=values["minimum"],
+                maximum=values["maximum"],
+                slope=values["slope"],
+                intercept=values["intercept"],
+            )
+        result[sensor_id] = features
+    return result
+
+
+def _finite_number(value: object) -> float:
+    if not isinstance(value, (int, float)) or isinstance(value, bool):
+        raise ValueError("feature model values must be finite numbers")
+    converted = float(value)
+    if not math.isfinite(converted):
+        raise ValueError("feature model values must be finite numbers")
+    return converted

app/ml/training.py

@@ -1,17 +1,44 @@
 from __future__ import annotations
 
 import logging
-from dataclasses import dataclass
+import math
+from collections import defaultdict
+from dataclasses import dataclass, field
 
 from app.ml.feature_store import FeatureStore
 
 logger = logging.getLogger(__name__)
 
 
-@dataclass
+@dataclass(frozen=True)
+class FeatureModel:
+    sample_count: int
+    mean: float
+    standard_deviation: float
+    minimum: float
+    maximum: float
+    slope: float
+    intercept: float
+
+    def forecast(self, current_value: float | None = None) -> float:
+        if current_value is not None:
+            return current_value + self.slope
+        return self.intercept + self.slope * self.sample_count
+
+    @property
+    def confidence(self) -> float:
+        sample_score = self.sample_count / (self.sample_count + 2)
+        scale = abs(self.mean) if abs(self.mean) > 1e-9 else 1.0
+        stability_score = 1.0 / (1.0 + self.standard_deviation / scale)
+        return min(0.99, max(0.05, sample_score * stability_score))
+
+
+@dataclass(frozen=True)
 class TrainedArtifact:
     artifact_id: str
     supported_sensors: tuple[str, ...]
+    feature_models: dict[str, dict[str, FeatureModel]] = field(default_factory=dict)
+    model_type: str = "statistical_baseline"
 
 
 class TrainingPipeline:
@@ -24,8 +51,33 @@ class TrainingPipeline:
         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)
+        samples: dict[str, dict[str, list[float]]] = defaultdict(lambda: defaultdict(list))
+        for vector in vectors:
+            for feature_name, raw_value in vector.values.items():
+                value = float(raw_value)
+                if math.isfinite(value):
+                    samples[vector.sensor_id][feature_name].append(value)
+
+        feature_models = {
+            sensor_id: {
+                feature_name: _fit_feature(values)
+                for feature_name, values in sorted(features.items())
+                if values
+            }
+            for sensor_id, features in sorted(samples.items())
+        }
+        feature_models = {
+            sensor_id: models for sensor_id, models in feature_models.items() if models
+        }
+        if not feature_models:
+            raise ValueError("Trainingsdaten enthalten keine endlichen numerischen Werte.")
+
+        sensors = tuple(feature_models)
+        artifact = TrainedArtifact(
+            artifact_id=artifact_id,
+            supported_sensors=sensors,
+            feature_models=feature_models,
+        )
         self._artifacts[artifact_id] = artifact
         logger.info("Training abgeschlossen für %s mit %d Sensoren", artifact_id, len(sensors))
         return artifact
@@ -34,3 +86,32 @@ class TrainingPipeline:
         if artifact_id not in self._artifacts:
             raise KeyError(f"Artifact '{artifact_id}' nicht gefunden.")
         return self._artifacts[artifact_id]
+
+
+def _fit_feature(values: list[float]) -> FeatureModel:
+    sample_count = len(values)
+    mean = sum(values) / sample_count
+    variance = sum((value - mean) ** 2 for value in values) / sample_count
+    standard_deviation = math.sqrt(variance)
+
+    if sample_count == 1:
+        slope = 0.0
+        intercept = mean
+    else:
+        x_mean = (sample_count - 1) / 2
+        denominator = sum((index - x_mean) ** 2 for index in range(sample_count))
+        numerator = sum(
+            (index - x_mean) * (value - mean) for index, value in enumerate(values)
+        )
+        slope = numerator / denominator
+        intercept = mean - slope * x_mean
+
+    return FeatureModel(
+        sample_count=sample_count,
+        mean=mean,
+        standard_deviation=standard_deviation,
+        minimum=min(values),
+        maximum=max(values),
+        slope=slope,
+        intercept=intercept,
+    )

app/static/index.html

@@ -0,0 +1,149 @@
+<!doctype html>
+<html lang="de">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width,initial-scale=1">
+  <title>SillyHome Next</title>
+  <style>
+    :root { color-scheme: dark; font-family: system-ui, sans-serif; background: #10151c; color: #eaf1f8; }
+    body { margin: 0; }
+    header { padding: 20px; background: linear-gradient(135deg,#142b3a,#193f36); }
+    h1,h2 { margin: 0 0 12px; }
+    header p { margin: 4px 0; color: #b9c9d6; }
+    main { display: grid; grid-template-columns: repeat(auto-fit,minmax(310px,1fr)); gap: 14px; padding: 14px; }
+    section { background: #18212b; border: 1px solid #2d3a47; border-radius: 12px; padding: 16px; }
+    .wide { grid-column: 1 / -1; }
+    .ok { color: #66dfa9; } .bad { color: #ff8f8f; }
+    label { display: block; margin: 9px 0 4px; color: #b9c9d6; }
+    input,select,textarea,button { box-sizing: border-box; width: 100%; border-radius: 7px; border: 1px solid #3b4b5b; padding: 9px; background: #101820; color: #fff; }
+    button { margin-top: 10px; background: #23715b; border: 0; font-weight: 700; cursor: pointer; }
+    button.secondary { background: #37495c; }
+    pre { white-space: pre-wrap; max-height: 310px; overflow: auto; background: #0d141b; padding: 10px; border-radius: 7px; }
+    table { width: 100%; border-collapse: collapse; font-size: .9rem; }
+    td,th { padding: 7px; border-bottom: 1px solid #2d3a47; text-align: left; }
+    .notice { border-left: 4px solid #e8b34b; padding-left: 10px; }
+  </style>
+</head>
+<body>
+<header>
+  <h1>SillyHome Next</h1>
+  <p>Lokale Home-Assistant-Analyse, Vorhersagen und kontrollierte Automation-Entwürfe.</p>
+  <p class="notice">Sicherheitsmodus: Entwürfe werden niemals automatisch in Home Assistant ausgeführt.</p>
+</header>
+<main>
+  <section>
+    <h2>Systemstatus</h2>
+    <div id="status">Prüfung läuft ...</div>
+    <button class="secondary" onclick="loadStatus()">Neu laden</button>
+  </section>
+  <section>
+    <h2>Entity Discovery</h2>
+    <label for="domain">Domain (optional)</label>
+    <input id="domain" placeholder="sensor">
+    <button onclick="discover()">HA-Entities analysieren</button>
+    <pre id="discovery">Noch nicht geladen.</pre>
+  </section>
+  <section>
+    <h2>Modell trainieren</h2>
+    <label for="train-model">Modell-ID</label><input id="train-model" value="home-model">
+    <label for="train-sensor">Sensor</label><input id="train-sensor" placeholder="sensor.temperatur">
+    <label for="train-feature">Merkmal</label><input id="train-feature" value="value">
+    <label for="train-values">Messwerte, komma-getrennt</label><input id="train-values" placeholder="19,20,21">
+    <button onclick="train()">Trainieren</button>
+    <pre id="training">Bereit.</pre>
+  </section>
+  <section>
+    <h2>Vorhersage</h2>
+    <label for="predict-model">Modell-ID</label><input id="predict-model" value="home-model">
+    <label for="predict-sensor">Sensor</label><input id="predict-sensor" placeholder="sensor.temperatur">
+    <label for="predict-feature">Merkmal</label><input id="predict-feature" value="value">
+    <label for="predict-value">Aktueller Wert</label><input id="predict-value" type="number" step="any">
+    <button onclick="predict()">Vorhersagen und erklären</button>
+    <pre id="prediction">Bereit.</pre>
+  </section>
+  <section class="wide">
+    <h2>Automation-Entwurf</h2>
+    <p>Der Entwurf muss explizit freigegeben werden. Auch danach wird nur YAML exportiert, nichts geschaltet.</p>
+    <div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(220px,1fr));gap:8px">
+      <div><label for="alias">Name</label><input id="alias" value="Licht bei Dunkelheit"></div>
+      <div><label for="trigger">Trigger-Entity</label><input id="trigger" placeholder="sensor.flur_illuminance"></div>
+      <div><label for="below">Unter Grenzwert</label><input id="below" type="number" value="10"></div>
+      <div><label for="service">Dienst</label><select id="service"><option>light.turn_on</option><option>light.turn_off</option><option>switch.turn_on</option><option>switch.turn_off</option></select></div>
+      <div><label for="target">Ziel-Entity</label><input id="target" placeholder="light.flur"></div>
+    </div>
+    <button onclick="createProposal()">Entwurf speichern</button>
+    <button class="secondary" onclick="loadProposals()">Entwürfe aktualisieren</button>
+    <div id="proposals"></div>
+  </section>
+</main>
+<script>
+const pretty = value => JSON.stringify(value, null, 2);
+async function api(path, options={}) {
+  const response = await fetch(path, {headers: {"Content-Type":"application/json"}, ...options});
+  const body = await response.json().catch(() => ({}));
+  if (!response.ok) throw new Error(body.detail || `${response.status} ${response.statusText}`);
+  return body;
+}
+async function loadStatus() {
+  const box=document.getElementById("status");
+  try {
+    const [health, ml, models]=await Promise.all([api("health"),api("ml/health"),api("ml/models")]);
+    box.innerHTML=`<p class="ok">API und ML bereit</p><p>Modelle: ${models.models.length}</p>`;
+  } catch(e) { box.innerHTML=`<p class="bad">${e.message}</p>`; }
+}
+async function discover() {
+  const out=document.getElementById("discovery"), domain=document.getElementById("domain").value.trim();
+  out.textContent="Lade ...";
+  try {
+    const rows=await api(`v1/discovery?learnable=true${domain?`&domain=${encodeURIComponent(domain)}`:""}`);
+    out.textContent=pretty({learnable_entities:rows.length, entities:rows.slice(0,100)});
+  } catch(e) { out.textContent=e.message; }
+}
+async function train() {
+  const out=document.getElementById("training");
+  try {
+    const values=document.getElementById("train-values").value.split(",").map(Number).filter(Number.isFinite);
+    if (!values.length) throw new Error("Mindestens einen Messwert eingeben.");
+    const sensor=document.getElementById("train-sensor").value.trim(), feature=document.getElementById("train-feature").value.trim();
+    const samples=values.map(value=>({sensor_id:sensor,values:{[feature]:value}}));
+    out.textContent=pretty(await api("ml/retrain",{method:"POST",body:JSON.stringify({modelId:document.getElementById("train-model").value,samples})}));
+    await loadStatus();
+  } catch(e) { out.textContent=e.message; }
+}
+async function predict() {
+  const out=document.getElementById("prediction");
+  try {
+    const feature=document.getElementById("predict-feature").value.trim();
+    out.textContent=pretty(await api("ml/predict",{method:"POST",body:JSON.stringify({
+      modelId:document.getElementById("predict-model").value,
+      sensor_id:document.getElementById("predict-sensor").value.trim(),
+      values:{[feature]:Number(document.getElementById("predict-value").value)}
+    })}));
+  } catch(e) { out.textContent=e.message; }
+}
+async function createProposal() {
+  try {
+    await api("v1/automations/proposals",{method:"POST",body:JSON.stringify({
+      alias:document.getElementById("alias").value,
+      description:"Manuell im SillyHome-Dashboard erstellter und nicht automatisch ausgeführter Entwurf.",
+      trigger:{entity_id:document.getElementById("trigger").value,below:Number(document.getElementById("below").value)},
+      action:{service:document.getElementById("service").value,entity_id:document.getElementById("target").value,data:{}}
+    })});
+    await loadProposals();
+  } catch(e) { alert(e.message); }
+}
+async function decide(id, revision, action) {
+  try { await api(`v1/automations/proposals/${id}/${action}`,{method:"POST",body:JSON.stringify({expected_revision:revision})}); await loadProposals(); }
+  catch(e) { alert(e.message); }
+}
+async function loadProposals() {
+  const box=document.getElementById("proposals");
+  try {
+    const rows=await api("v1/automations/proposals");
+    box.innerHTML=rows.length?`<table><tr><th>Name</th><th>Status</th><th>Aktion</th></tr>${rows.map(x=>`<tr><td>${x.alias}</td><td>${x.status}</td><td>${x.status==="draft"?`<button onclick="decide('${x.proposal_id}',${x.revision},'approve')">Freigeben</button><button class="secondary" onclick="decide('${x.proposal_id}',${x.revision},'reject')">Ablehnen</button>`:`${x.status==="approved"?`<a href="v1/automations/proposals/${x.proposal_id}/yaml">YAML laden</a>`:"-"}`}</td></tr>`).join("")}</table>`:"<p>Keine Entwürfe.</p>";
+  } catch(e) { box.textContent=e.message; }
+}
+loadStatus(); loadProposals();
+</script>
+</body>
+</html>

backend/routes/ml.py

@@ -7,6 +7,7 @@ from collections.abc import Sequence
 from fastapi import APIRouter, FastAPI, HTTPException, Request, status
 from pydantic import BaseModel, Field
 
+from app.ml.evaluation import Evaluator
 from app.ml.feature_store import FeatureVector
 from app.ml.predictor import Predictor
 from app.ml.registry.model_registry import ModelRegistry
@@ -31,7 +32,25 @@ class PredictRequest(BaseModel):
 class PredictResponse(BaseModel):
     model_id: str
     sensor_id: str
-    prediction: str
+    predictions: dict[str, float]
+    confidence: float
+    model_type: str
+    explanations: dict[str, "FeatureExplanationResponse"]
+
+
+class FeatureExplanationResponse(BaseModel):
+    feature: str
+    current_value: float
+    predicted_value: float
+    change: float
+    direction: str
+    sample_count: int
+    historical_mean: float
+    historical_range: tuple[float, float]
+    standard_deviation: float
+    trend_per_step: float
+    confidence: float
+    summary: str
 
 
 class BatchRequest(BaseModel):
@@ -60,9 +79,28 @@ class RetrainRequest(BaseModel):
 class RetrainResponse(BaseModel):
     model_id: str
     supported_sensors: list[str]
+    trained_features: int
+    model_type: str
     replaced: bool
 
 
+class EvaluateRequest(BaseModel):
+    model_id: str = Field(..., alias="modelId", min_length=1, max_length=128)
+    samples: list[TrainingSample] = Field(min_length=1)
+
+
+class MetricResponse(BaseModel):
+    name: str
+    value: float
+    threshold: float | None = None
+
+
+class EvaluateResponse(BaseModel):
+    model_id: str
+    sample_size: int
+    metrics: list[MetricResponse]
+
+
 @router.get("/health", response_model=HealthResponse, status_code=200)
 def health() -> HealthResponse:
     return HealthResponse(status="ok")
@@ -96,10 +134,50 @@ def retrain(payload: RetrainRequest, request: Request) -> RetrainResponse:
     return RetrainResponse(
         model_id=result.artifact.artifact_id,
         supported_sensors=list(result.artifact.supported_sensors),
+        trained_features=sum(
+            len(feature_models)
+            for feature_models in result.artifact.feature_models.values()
+        ),
+        model_type=result.artifact.model_type,
         replaced=result.replaced,
     )
 
 
+@router.post("/evaluate", response_model=EvaluateResponse, status_code=200)
+def evaluate(payload: EvaluateRequest, request: Request) -> EvaluateResponse:
+    registry = _require_registry(request)
+    vectors = [
+        FeatureVector(
+            sensor_id=sample.sensor_id,
+            values=sample.values,
+            label=sample.label,
+        )
+        for sample in payload.samples
+    ]
+    try:
+        report = Evaluator(registry=registry).evaluate(payload.model_id, vectors)
+    except ValueError as exc:
+        try:
+            registry.load_artifact(payload.model_id)
+        except KeyError:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail=str(exc),
+            ) from exc
+        raise HTTPException(
+            status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
+            detail=str(exc),
+        ) from exc
+    return EvaluateResponse(
+        model_id=report.artifact_id,
+        sample_size=report.sample_size,
+        metrics=[
+            MetricResponse(name=metric.name, value=metric.value, threshold=metric.threshold)
+            for metric in report.metrics
+        ],
+    )
+
+
 @router.post("/predict", response_model=PredictResponse, status_code=200)
 def predict(payload: PredictRequest, request: Request) -> PredictResponse:
     registry = _require_registry(request)
@@ -117,7 +195,13 @@ def predict(payload: PredictRequest, request: Request) -> PredictResponse:
     return PredictResponse(
         model_id=payload.model_id,
         sensor_id=payload.sensor_id,
-        prediction=prediction,
+        predictions=prediction.predictions,
+        confidence=prediction.confidence,
+        model_type=prediction.model_type,
+        explanations={
+            name: FeatureExplanationResponse(**explanation.__dict__)
+            for name, explanation in prediction.explanations.items()
+        },
     )
 
 
@@ -138,7 +222,17 @@ def predict_batch(payload: BatchRequest, request: Request) -> BatchResponse:
                 detail=str(exc),
             ) from exc
         responses.append(
-            PredictResponse(model_id=item.model_id, sensor_id=item.sensor_id, prediction=prediction)
+            PredictResponse(
+                model_id=item.model_id,
+                sensor_id=item.sensor_id,
+                predictions=prediction.predictions,
+                confidence=prediction.confidence,
+                model_type=prediction.model_type,
+                explanations={
+                    name: FeatureExplanationResponse(**explanation.__dict__)
+                    for name, explanation in prediction.explanations.items()
+                },
+            )
         )
     return BatchResponse(predictions=responses)
 

docker-compose.yml

@@ -8,8 +8,10 @@ services:
         required: false
     environment:
       SILLYHOME_MODEL_STORE: /app/data/models
+      SILLYHOME_AUTOMATION_STORE: /app/data/automations
     volumes:
       - model-data:/app/data/models
+      - automation-data:/app/data/automations
     read_only: true
     tmpfs:
       - /tmp
@@ -21,3 +23,4 @@ services:
 
 volumes:
   model-data:
+  automation-data:

docs/automations.md

@@ -0,0 +1,14 @@
+# Automation-Vorschläge
+
+SillyHome Next führt Automationen niemals automatisch aus. Der Workflow ist:
+
+1. Vorschlag als `draft` erstellen.
+2. Inhalt und Ziel-Entity prüfen.
+3. Mit aktueller Revision explizit freigeben oder ablehnen.
+4. Nur freigegebene Vorschläge als Home-Assistant-YAML exportieren.
+5. Das YAML außerhalb von SillyHome Next in Home Assistant importieren.
+
+Erlaubt sind numerische Sensor-Trigger und Aktionsdienste aus den Domains
+`light`, `switch`, `climate`, `fan` und `cover`. Shell-Kommandos, Skripte und
+beliebige Service-Domains werden abgewiesen. Eine einmal getroffene Entscheidung
+kann nicht überschrieben werden; Änderungen benötigen einen neuen Vorschlag.

docs/ml_api.md

@@ -3,9 +3,7 @@
 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.
+Das Serving verwendet ein lokal trainiertes statistisches Baseline-Modell.
 
 ## Basis-URL
 
@@ -13,6 +11,7 @@ Modell-Artefakt- und Vorhersage-Schnittstelle.
 - Health: `/health`
 - Modelle: `/models`
 - Retraining: `/retrain`
+- Evaluation: `/evaluate`
 - Einzelvorhersage: `/predict`
 - Batchvorhersage: `/batch`
 
@@ -62,10 +61,27 @@ Einzelne Vorhersage für einen Sensor.
 {
   "model_id": "default",
   "sensor_id": "sensor.kitchen",
-  "prediction": "default:sensor.kitchen:{'temperature': 21.0}"
+  "predictions": {"temperature": 21.4},
+  "confidence": 0.78,
+  "model_type": "statistical_baseline",
+  "explanations": {
+    "temperature": {
+      "direction": "steigend",
+      "change": 0.4,
+      "sample_count": 24,
+      "historical_mean": 20.7,
+      "trend_per_step": 0.4,
+      "summary": "temperature: steigend; Prognose ..."
+    }
+  }
 }
 ```
 
+Die Erklärung nennt pro Merkmal den aktuellen und prognostizierten Wert,
+Richtung, Veränderung, Datenbasis, historischen Bereich, Streuung, Trend und
+Confidence. Sie wird deterministisch aus den gespeicherten Modellparametern
+erzeugt.
+
 ### `POST /ml/retrain`
 
 Trainiert die Artefakt-Metadaten aus neuen Sensordaten. Existiert `modelId`
@@ -91,10 +107,17 @@ dem Modellverzeichnis geladen.
 {
   "model_id": "home-model",
   "supported_sensors": ["sensor.kitchen"],
+  "trained_features": 1,
+  "model_type": "statistical_baseline",
   "replaced": false
 }
 ```
 
+### `POST /ml/evaluate`
+
+Vergleicht Modellvorhersagen mit Validierungsdaten und liefert MAE, RMSE und
+Coverage. Der Request verwendet dasselbe Sample-Format wie `/ml/retrain`.
+
 ### `POST /ml/batch`
 
 Batch-Vorhersage für mehrere Sensorwerte.
@@ -124,12 +147,16 @@ Batch-Vorhersage für mehrere Sensorwerte.
     {
       "model_id": "default",
       "sensor_id": "sensor.kitchen",
-      "prediction": "default:sensor.kitchen:{'temperature': 21.0}"
+      "predictions": {"temperature": 21.4},
+      "confidence": 0.78,
+      "model_type": "statistical_baseline"
     },
     {
       "model_id": "default",
       "sensor_id": "sensor.bedroom",
-      "prediction": "default:sensor.bedroom:{'temperature': 18.5}"
+      "predictions": {"temperature": 18.3},
+      "confidence": 0.74,
+      "model_type": "statistical_baseline"
     }
   ]
 }

docs/ml_training.md

@@ -1,14 +1,13 @@
 # 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.
+SillyHome Next trainiert ein lokales statistisches Baseline-Modell pro Sensor
+und Merkmal. Es benötigt keine Cloud und keine externe ML-Laufzeit.
 
 ## 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
+## 2. Statistisches Artefakt erzeugen
 
 ```python
 store = FeatureStore()
@@ -18,21 +17,30 @@ 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.
+`TrainingPipeline.run(...)` berechnet für jedes numerische Merkmal:
+
+- Stichprobenzahl
+- Mittelwert und Standardabweichung
+- Minimum und Maximum
+- linearen Trend mit Steigung und Achsenabschnitt
+
+Die nächste Vorhersage kombiniert den letzten beobachteten Wert mit der
+trainierten Trendsteigung. Die Confidence berücksichtigt Datenmenge und
+Stabilität.
 
 ## 3. Modell evaluieren
 
 ```python
 evaluator = Evaluator(pipeline)
-report = evaluator.evaluate(artifact.artifact_id, predictions)
+report = evaluator.evaluate(artifact.artifact_id, validation_samples)
 ```
 
-Der Report enthält:
+Der Report enthält echte numerische Vergleichsmetriken:
 - `artifact_id`
 - `sample_size`
-- Metriken wie `coverage` und `unknown_rate` mit Default-Schwellenwerten
+- `mae` (Mean Absolute Error)
+- `rmse` (Root Mean Squared Error)
+- `coverage` für den Anteil auswertbarer Merkmale
 
 ## 4. Modell registrieren
 
@@ -56,4 +64,5 @@ Der Service startet bewusst keinen eigenen Hintergrundprozess. Über
 ## 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.
+- Nur endliche numerische Werte werden trainiert.
+- `coverage` bleibt im Bereich 0 bis 1.

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-next"
-version = "0.1.0"
+version = "0.3.0"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [

repository.yaml

@@ -0,0 +1,3 @@
+name: SillyHome Next Add-ons
+url: http://192.168.6.31:3000/pino/sillyhome-next
+maintainer: Pino

tests/api/test_automations.py

@@ -0,0 +1,59 @@
+from pathlib import Path
+
+from fastapi.testclient import TestClient
+
+from app.automations.store import AutomationStore
+from app.main import app
+
+
+def _payload() -> dict[str, object]:
+    return {
+        "alias": "Licht bei Dunkelheit",
+        "description": "Schaltet das Flurlicht unter dem Helligkeitsgrenzwert ein.",
+        "trigger": {"entity_id": "sensor.hall_illuminance", "below": 10},
+        "action": {
+            "service": "light.turn_on",
+            "entity_id": "light.hall",
+            "data": {"brightness_pct": 40},
+        },
+    }
+
+
+def test_proposal_requires_explicit_approval_before_yaml(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        app.state.automation_store = AutomationStore(tmp_path)
+        created = client.post("/v1/automations/proposals", json=_payload())
+        proposal_id = created.json()["proposal_id"]
+        blocked = client.get(f"/v1/automations/proposals/{proposal_id}/yaml")
+        approved = client.post(
+            f"/v1/automations/proposals/{proposal_id}/approve",
+            json={"expected_revision": 1},
+        )
+        exported = client.get(f"/v1/automations/proposals/{proposal_id}/yaml")
+    assert created.status_code == 201
+    assert created.json()["status"] == "draft"
+    assert blocked.status_code == 409
+    assert approved.json()["status"] == "approved"
+    assert "service: light.turn_on" in exported.text
+
+
+def test_proposal_rejects_unsafe_service_domain(tmp_path: Path) -> None:
+    payload = _payload()
+    payload["action"] = {
+        "service": "shell_command.run",
+        "entity_id": "light.hall",
+        "data": {},
+    }
+    with TestClient(app) as client:
+        app.state.automation_store = AutomationStore(tmp_path)
+        response = client.post("/v1/automations/proposals", json=payload)
+    assert response.status_code == 422
+
+
+def test_proposal_requires_numeric_threshold(tmp_path: Path) -> None:
+    payload = _payload()
+    payload["trigger"] = {"entity_id": "sensor.hall_illuminance"}
+    with TestClient(app) as client:
+        app.state.automation_store = AutomationStore(tmp_path)
+        response = client.post("/v1/automations/proposals", json=payload)
+    assert response.status_code == 422

tests/api/test_ml_routes.py

@@ -87,12 +87,16 @@ def test_retrain_creates_and_replaces_persisted_model(tmp_path: Path) -> None:
     assert created.json() == {
         "model_id": "home-model",
         "supported_sensors": ["sensor.kitchen"],
+        "trained_features": 1,
+        "model_type": "statistical_baseline",
         "replaced": False,
     }
     assert replaced.status_code == 200
     assert replaced.json() == {
         "model_id": "home-model",
         "supported_sensors": ["sensor.bedroom"],
+        "trained_features": 1,
+        "model_type": "statistical_baseline",
         "replaced": True,
     }
     restarted = ModelRegistry(tmp_path)
@@ -107,3 +111,74 @@ def test_retrain_rejects_empty_samples() -> None:
         )
 
     assert response.status_code == 422
+
+
+def test_predict_returns_numeric_forecast_and_confidence(tmp_path: Path) -> None:
+    from app.ml.feature_store import FeatureStore, FeatureVector
+    from app.ml.registry.model_registry import ModelRegistry
+    from app.ml.training import TrainingPipeline
+
+    store = FeatureStore()
+    store.add_batch(
+        [
+            FeatureVector("sensor.kitchen", {"temperature": 19.0}),
+            FeatureVector("sensor.kitchen", {"temperature": 20.0}),
+        ]
+    )
+    registry = ModelRegistry(tmp_path)
+    registry.register(TrainingPipeline(store).run("home-model"))
+
+    with TestClient(app) as client:
+        app.state.registry = registry
+        response = client.post(
+            "/ml/predict",
+            json={
+                "modelId": "home-model",
+                "sensor_id": "sensor.kitchen",
+                "values": {"temperature": 21.0},
+            },
+        )
+
+    assert response.status_code == 200
+    assert response.json()["predictions"] == {"temperature": 22.0}
+    assert 0.0 < response.json()["confidence"] <= 1.0
+    assert response.json()["model_type"] == "statistical_baseline"
+    explanation = response.json()["explanations"]["temperature"]
+    assert explanation["direction"] == "steigend"
+    assert explanation["change"] == 1.0
+    assert explanation["sample_count"] == 2
+
+
+def test_evaluate_returns_real_error_metrics(tmp_path: Path) -> None:
+    from app.ml.feature_store import FeatureStore, FeatureVector
+    from app.ml.registry.model_registry import ModelRegistry
+    from app.ml.training import TrainingPipeline
+
+    store = FeatureStore()
+    store.add_batch(
+        [
+            FeatureVector("sensor.kitchen", {"temperature": 19.0}),
+            FeatureVector("sensor.kitchen", {"temperature": 20.0}),
+        ]
+    )
+    registry = ModelRegistry(tmp_path)
+    registry.register(TrainingPipeline(store).run("home-model"))
+
+    with TestClient(app) as client:
+        app.state.registry = registry
+        response = client.post(
+            "/ml/evaluate",
+            json={
+                "modelId": "home-model",
+                "samples": [
+                    {
+                        "sensor_id": "sensor.kitchen",
+                        "values": {"temperature": 21.0},
+                    }
+                ],
+            },
+        )
+
+    assert response.status_code == 200
+    metrics = {metric["name"]: metric["value"] for metric in response.json()["metrics"]}
+    assert metrics == {"mae": 1.0, "rmse": 1.0, "coverage": 1.0}

tests/automations/test_store.py

@@ -0,0 +1,53 @@
+from pathlib import Path
+
+import pytest
+
+from app.automations.models import (
+    AutomationProposal,
+    NumericStateTrigger,
+    ProposalStatus,
+    ServiceAction,
+)
+from app.automations.store import AutomationStore
+
+
+def proposal() -> AutomationProposal:
+    return AutomationProposal(
+        alias="Wohnzimmer bei Kälte heizen",
+        description="Aktiviert den Heizmodus unter 18 Grad.",
+        trigger=NumericStateTrigger(entity_id="sensor.living_room_temperature", below=18.0),
+        action=ServiceAction(
+            service="climate.set_temperature",
+            entity_id="climate.living_room",
+            data={"temperature": 21.0},
+        ),
+    )
+
+
+def test_store_persists_approval_and_exports_yaml(tmp_path: Path) -> None:
+    store = AutomationStore(tmp_path)
+    created = store.create(proposal())
+    approved = store.decide(created.proposal_id, ProposalStatus.APPROVED, 1)
+    yaml = AutomationStore(tmp_path).export_yaml(created.proposal_id)
+    assert approved.status is ProposalStatus.APPROVED
+    assert approved.revision == 2
+    assert "platform: numeric_state" in yaml
+    assert "service: climate.set_temperature" in yaml
+    assert "temperature: 21.0" in yaml
+
+
+def test_store_requires_approval_and_current_revision(tmp_path: Path) -> None:
+    store = AutomationStore(tmp_path)
+    created = store.create(proposal())
+    with pytest.raises(ValueError, match="freigegebene"):
+        store.export_yaml(created.proposal_id)
+    with pytest.raises(ValueError, match="Revision"):
+        store.decide(created.proposal_id, ProposalStatus.APPROVED, 2)
+
+
+def test_store_allows_only_one_decision(tmp_path: Path) -> None:
+    store = AutomationStore(tmp_path)
+    created = store.create(proposal())
+    store.decide(created.proposal_id, ProposalStatus.REJECTED, 1)
+    with pytest.raises(ValueError, match="bereits entschieden"):
+        store.decide(created.proposal_id, ProposalStatus.APPROVED, 2)

tests/ml/test_evaluation.py

@@ -24,14 +24,15 @@ def test_evaluate_returns_report_with_metrics() -> None:
     report = evaluator.evaluate(
         "artifact_v1",
         [
-            "artifact_v1:sensor.kitchen:{'temperature': 21.0}",
-            "artifact_v1:sensor.bedroom:{'temperature': 18.5}",
+            _vector("sensor.kitchen", 21.0),
+            _vector("sensor.bedroom", 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 {metric.name for metric in report.metrics} == {"mae", "rmse", "coverage"}
     assert next(metric.value for metric in report.metrics if metric.name == "coverage") == 1.0
+    assert next(metric.value for metric in report.metrics if metric.name == "mae") == 0.0
 
 
 def test_evaluate_without_training_raises_value_error() -> None:
@@ -40,16 +41,16 @@ def test_evaluate_without_training_raises_value_error() -> None:
         evaluator.evaluate("artifact_v1", [])
 
 
-def test_coverage_is_bounded_and_requires_exact_sensor_match() -> None:
+def test_coverage_counts_only_supported_sensor_features() -> None:
     evaluator = evaluator_factory()
     report = evaluator.evaluate(
         "artifact_v1",
         [
-            "artifact_v1:sensor.kitchen:{'note': 'sensor.bedroom'}",
-            "artifact_v1:sensor.kitchen_extra:{}",
-            "malformed",
+            _vector("sensor.kitchen", 21.0),
+            FeatureVector(sensor_id="sensor.kitchen", values={"humidity": 50.0}),
+            _vector("sensor.kitchen_extra", 20.0),
         ],
     )
 
     metrics = {metric.name: metric.value for metric in report.metrics}
-    assert metrics == {"coverage": pytest.approx(1 / 3), "unknown_rate": pytest.approx(2 / 3)}
+    assert metrics["coverage"] == pytest.approx(1 / 3)

tests/ml/test_explanation.py

@@ -0,0 +1,34 @@
+from __future__ import annotations
+
+from app.ml.explanation import explain_feature
+from app.ml.training import FeatureModel
+
+
+def _model(slope: float) -> FeatureModel:
+    return FeatureModel(
+        sample_count=4,
+        mean=20.0,
+        standard_deviation=1.0,
+        minimum=18.0,
+        maximum=22.0,
+        slope=slope,
+        intercept=18.5,
+    )
+
+
+def test_explain_feature_describes_rising_forecast() -> None:
+    explanation = explain_feature("temperature", 21.0, 21.5, _model(0.5))
+
+    assert explanation.direction == "steigend"
+    assert explanation.change == 0.5
+    assert explanation.historical_range == (18.0, 22.0)
+    assert "4 Messwerte" in explanation.summary
+    assert "Trend +0.500" in explanation.summary
+
+
+def test_explain_feature_describes_stable_and_falling_forecasts() -> None:
+    stable = explain_feature("humidity", 50.0, 50.0, _model(0.0))
+    falling = explain_feature("temperature", 21.0, 20.5, _model(-0.5))
+
+    assert stable.direction == "stabil"
+    assert falling.direction == "fallend"

tests/ml/test_model_registry.py

@@ -19,6 +19,24 @@ def test_registry_loads_persisted_artifacts_after_restart(tmp_path: Path) -> Non
     assert restarted.load_artifact("model-v1") == artifact
 
 
+def test_registry_persists_statistical_parameters(tmp_path: Path) -> None:
+    from app.ml.feature_store import FeatureStore, FeatureVector
+    from app.ml.training import TrainingPipeline
+
+    store = FeatureStore()
+    store.add_batch(
+        [
+            FeatureVector("sensor.kitchen", {"temperature": 19.0}),
+            FeatureVector("sensor.kitchen", {"temperature": 20.0}),
+        ]
+    )
+    artifact = TrainingPipeline(store).run("model-v1")
+
+    ModelRegistry(tmp_path).register(artifact)
+
+    assert ModelRegistry(tmp_path).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",)))

tests/ml/test_predictor.py

@@ -13,16 +13,31 @@ def _vector(sensor_id: str, temperature: float, label: str | None = None) -> Fea
 
 def predictor() -> Predictor:
     store = FeatureStore()
-    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
+    store.add_batch(
+        [
+            _vector("sensor.kitchen", 19.0),
+            _vector("sensor.kitchen", 20.0),
+            _vector("sensor.bedroom", 18.5),
+        ]
+    )
     pipeline = TrainingPipeline(store)
     pipeline.run("artifact_v1")
     return Predictor(pipeline)
 
 
-def test_predict_returns_expected_format() -> None:
+def test_predict_returns_statistical_forecast() -> None:
     p = predictor()
     result = p.predict("artifact_v1", _vector("sensor.kitchen", 21.0))
-    assert result == "artifact_v1:sensor.kitchen:{'temperature': 21.0}"
+    assert result.artifact_id == "artifact_v1"
+    assert result.sensor_id == "sensor.kitchen"
+    assert result.predictions == {"temperature": 22.0}
+    assert 0.0 < result.confidence <= 1.0
+    assert result.model_type == "statistical_baseline"
+    explanation = result.explanations["temperature"]
+    assert explanation.direction == "steigend"
+    assert explanation.current_value == 21.0
+    assert explanation.predicted_value == 22.0
+    assert explanation.sample_count == 2
 
 
 def test_predict_rejects_unknown_sensor() -> None:
@@ -45,4 +60,4 @@ def test_default_artifact_returns_last_registered() -> None:
     pipeline = TrainingPipeline(store)
     pipeline.run("first")
     pipeline.run("second")
-    assert Predictor.default_artifact(pipeline).artifact_id == "second"
+    assert Predictor.default_artifact(pipeline).artifact_id == "second"

tests/ml/test_training.py

@@ -27,6 +27,11 @@ def test_run_returns_trained_artifact() -> None:
     artifact = pipeline.run("artifact_v1")
     assert artifact.artifact_id == "artifact_v1"
     assert artifact.supported_sensors == ("sensor.bedroom", "sensor.kitchen")
+    kitchen = artifact.feature_models["sensor.kitchen"]["temperature"]
+    assert kitchen.sample_count == 2
+    assert kitchen.mean == 19.5
+    assert kitchen.slope == 1.0
+    assert kitchen.forecast() == 21.0
 
 
 def test_run_without_data_raises_value_error() -> None:

tests/ml/test_training_evaluation.py

@@ -16,18 +16,18 @@ def test_end_to_end_training_then_evaluation() -> None:
     artifact = pipeline.run("artifact_v1")
 
     evaluator = Evaluator(pipeline)
-    predictions = [
-        "artifact_v1:sensor.kitchen:{'temperature': 21.0}",
-        "artifact_v1:sensor.bedroom:{'temperature': 18.5}",
+    samples = [
+        _vector("sensor.kitchen", 21.0),
+        _vector("sensor.bedroom", 18.5),
     ]
-    report = evaluator.evaluate(artifact.artifact_id, predictions)
+    report = evaluator.evaluate(artifact.artifact_id, samples)
     assert isinstance(report, EvalReport)
-    assert report.sample_size == len(predictions)
+    assert report.sample_size == len(samples)
     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"
+    metric = Metric(name="mae", value=0.85, threshold=1.0)
+    assert metric.name == "mae"
     assert metric.value == 0.85
-    assert metric.threshold == 0.8
+    assert metric.threshold == 1.0

tests/test_config.py

@@ -9,10 +9,12 @@ def test_load_settings_reads_documented_environment(monkeypatch: MonkeyPatch) ->
     monkeypatch.setenv("SILLYHOME_HA_URL", "http://ha.local:8123")
     monkeypatch.setenv("SILLYHOME_HA_TOKEN", "secret")
     monkeypatch.setenv("SILLYHOME_MODEL_STORE", "/tmp/models")
+    monkeypatch.setenv("SILLYHOME_AUTOMATION_STORE", "/tmp/automations")
 
     settings = load_settings()
 
     assert settings.ha_url == "http://ha.local:8123"
     assert settings.ha_token == "secret"
     assert settings.model_store == "/tmp/models"
+    assert settings.automation_store == "/tmp/automations"
     assert settings.ha_configured

tests/test_dashboard.py

@@ -0,0 +1,12 @@
+from fastapi.testclient import TestClient
+
+from app.main import app
+
+
+def test_dashboard_is_served_at_root() -> None:
+    with TestClient(app) as client:
+        response = client.get("/")
+
+    assert response.status_code == 200
+    assert "SillyHome Next" in response.text
+    assert "Automation-Entwurf" in response.text