Add autopilot light workflow

addon/Dockerfile

@@ -2,7 +2,7 @@ FROM python:3.13-slim
 
 WORKDIR /app
 
-ARG SILLYHOME_FUTURE_REF=v2.0.0-alpha.9
+ARG SILLYHOME_FUTURE_REF=v2.0.0-alpha.13
 RUN python -m pip install --no-cache-dir \
     "http://192.168.6.31:3000/Otto/sillyhome-future/archive/${SILLYHOME_FUTURE_REF}.tar.gz"
 

addon/config.yaml

@@ -1,12 +1,12 @@
 name: SillyHome Future
-version: "2.0.0-alpha.9"
+version: "2.0.0-alpha.13"
 slug: sillyhome_future
 description: Event-first SillyHome v2 test controller
 url: http://192.168.6.31:3000/Otto/sillyhome-future
 arch:
   - amd64
 startup: application
-boot: manual
+boot: auto
 watchdog: http://[HOST]:[PORT:8099]/health
 init: false
 ingress: true

app/core/ha_client.py

@@ -72,6 +72,46 @@ class FutureHaClient:
             )
             response.raise_for_status()
 
+    def read_history(
+        self,
+        entity_ids: list[str],
+        start_time: datetime,
+        end_time: datetime,
+    ) -> dict[str, list[StateEvent]]:
+        params = {
+            "filter_entity_id": ",".join(entity_ids),
+            "end_time": end_time.isoformat(),
+            "minimal_response": "1",
+        }
+        with httpx.Client(timeout=self._config.timeout_seconds) as client:
+            response = client.get(
+                f"{self._core_url}/api/history/period/{start_time.isoformat()}",
+                headers=self._headers,
+                params=params,
+            )
+            response.raise_for_status()
+            payload = response.json()
+        result: dict[str, list[StateEvent]] = {entity_id: [] for entity_id in entity_ids}
+        for series in payload if isinstance(payload, list) else []:
+            if not isinstance(series, list):
+                continue
+            for item in series:
+                if not isinstance(item, dict):
+                    continue
+                entity_id = item.get("entity_id")
+                if not isinstance(entity_id, str):
+                    continue
+                result.setdefault(entity_id, []).append(
+                    StateEvent(
+                        entity_id=entity_id,
+                        new_state=item.get("state") if isinstance(item.get("state"), str) else None,
+                        changed_at=_parse_datetime(
+                            item.get("last_changed") or item.get("last_updated")
+                        ),
+                    )
+                )
+        return result
+
     async def listen_state_events(self) -> AsyncIterator[StateEvent]:
         websocket_url = self._config.websocket_url or _default_websocket_url(self._core_url)
         async with websockets.connect(websocket_url, ping_interval=None) as websocket:
@@ -160,4 +200,3 @@ def _parse_datetime(value: object) -> datetime:
     if parsed.tzinfo is None:
         return parsed.replace(tzinfo=timezone.utc)
     return parsed
-

app/core/models.py

@@ -21,6 +21,25 @@ class SafetyStage(StrEnum):
     BLOCKED = "blocked"
 
 
+class ProposalStatus(StrEnum):
+    DRAFT = "draft"
+    APPROVED = "approved"
+    REJECTED = "rejected"
+
+
+class JobStatus(StrEnum):
+    QUEUED = "queued"
+    RUNNING = "running"
+    SUCCEEDED = "succeeded"
+    FAILED = "failed"
+
+
+class CandidateStatus(StrEnum):
+    PROPOSED = "proposed"
+    ACCEPTED = "accepted"
+    DISMISSED = "dismissed"
+
+
 class EntityState(BaseModel):
     entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
     domain: str
@@ -89,6 +108,88 @@ class SceneProfile(BaseModel):
     confidence: float = Field(default=0.0, ge=0.0, le=1.0)
 
 
+class AutomationProposal(BaseModel):
+    proposal_id: str
+    name: str
+    trigger_entity_id: str
+    trigger_state: str | None = None
+    actuator_entity_id: str
+    target_state: str
+    status: ProposalStatus = ProposalStatus.DRAFT
+    revision: int = 1
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    decided_at: datetime | None = None
+
+
+class ModelRecord(BaseModel):
+    model_id: str
+    actuator_entity_id: str
+    pattern_count: int
+    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
+    trained_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+class ModelEvaluation(BaseModel):
+    evaluation_id: str
+    model_id: str
+    actuator_entity_id: str
+    score: float = Field(default=0.0, ge=0.0, le=1.0)
+    coverage: float = Field(default=0.0, ge=0.0, le=1.0)
+    dry_run_success_rate: float = Field(default=0.0, ge=0.0, le=1.0)
+    feedback_score: float = Field(default=0.0, ge=0.0, le=1.0)
+    verdict: str
+    reasons: list[str] = Field(default_factory=list)
+    evaluated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+class JobQueueItem(BaseModel):
+    job_id: str
+    kind: str
+    status: JobStatus = JobStatus.QUEUED
+    message: str = ""
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    finished_at: datetime | None = None
+
+
+class SensorWeightOverride(BaseModel):
+    actuator_entity_id: str
+    sensor_weights: dict[str, float] = Field(default_factory=dict)
+    note: str | None = Field(default=None, max_length=500)
+    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+class AutopilotSettings(BaseModel):
+    enabled: bool = True
+    interval_seconds: int = Field(default=3600, ge=300)
+    min_candidate_confidence: float = Field(default=0.65, ge=0.0, le=1.0)
+    auto_train: bool = True
+    auto_evaluate: bool = True
+    auto_activate: bool = False
+    last_run_at: datetime | None = None
+
+
+class CandidateRecommendation(BaseModel):
+    candidate_id: str
+    actuator_entity_id: str
+    trigger_entity_id: str
+    trigger_state: str | None = None
+    target_state: str
+    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
+    reason: str
+    status: CandidateStatus = CandidateStatus.PROPOSED
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+class HistoryAnalysis(BaseModel):
+    entity_id: str
+    samples: int
+    last_state: str | None = None
+    changed_at: datetime | None = None
+    unique_states: int = 0
+    transitions: int = 0
+    recommendation: str
+
+
 class Decision(BaseModel):
     actuator_entity_id: str
     target_state: str | None = None
@@ -121,11 +222,18 @@ class LearningState(BaseModel):
     profiles: dict[str, LearningProfile] = Field(default_factory=dict)
     rooms: dict[str, RoomProfile] = Field(default_factory=dict)
     scenes: dict[str, SceneProfile] = Field(default_factory=dict)
+    automation_proposals: dict[str, AutomationProposal] = Field(default_factory=dict)
+    models: dict[str, ModelRecord] = Field(default_factory=dict)
+    model_evaluations: dict[str, ModelEvaluation] = Field(default_factory=dict)
+    jobs: dict[str, JobQueueItem] = Field(default_factory=dict)
+    weight_overrides: dict[str, SensorWeightOverride] = Field(default_factory=dict)
+    candidates: dict[str, CandidateRecommendation] = Field(default_factory=dict)
 
 
 class ControlState(BaseModel):
     profiles: dict[str, ControlProfile] = Field(default_factory=dict)
     global_enabled: bool = True
+    autopilot: AutopilotSettings = Field(default_factory=AutopilotSettings)
 
 
 class BackupBundle(BaseModel):

app/main.py

@@ -4,6 +4,7 @@ import asyncio
 import os
 from collections.abc import AsyncIterator
 from contextlib import asynccontextmanager, suppress
+from datetime import datetime, timezone
 
 from fastapi import FastAPI
 from fastapi.responses import HTMLResponse
@@ -15,16 +16,27 @@ from app.core.ha_client import FutureHaClient, HaClientConfig, service_for_state
 from app.core.handoff import HandoffMatrix
 from app.core.models import (
     AuditEvent,
+    AutopilotSettings,
+    AutomationProposal,
     BackupBundle,
     BehaviorPatternV2,
+    CandidateRecommendation,
+    CandidateStatus,
     ControlProfile,
     ControlState,
     EntityState,
+    HistoryAnalysis,
     HandoffMode,
+    JobQueueItem,
+    JobStatus,
     LearningProfile,
     LearningState,
+    ModelEvaluation,
+    ModelRecord,
+    ProposalStatus,
     RuntimeState,
     SafetyStage,
+    SensorWeightOverride,
     StateEvent,
 )
 from app.core.stores import FutureStores
@@ -70,27 +82,80 @@ class ActiveReadiness(BaseModel):
     dry_run_failures: int
 
 
+class FeedbackRequest(BaseModel):
+    kind: str = Field(default="correct", max_length=40)
+    expected_state: str | None = Field(default=None, max_length=100)
+    note: str | None = Field(default=None, max_length=500)
+
+
+class ActuatorSummary(BaseModel):
+    actuator_entity_id: str
+    stage: SafetyStage
+    handoff_mode: HandoffMode
+    active_ready: bool
+    pattern_count: int
+    feedback_positive: int
+    feedback_negative: int
+
+
+class AutomationProposalRequest(BaseModel):
+    name: str = Field(max_length=120)
+    trigger_entity_id: str
+    trigger_state: str | None = None
+    actuator_entity_id: str
+    target_state: str
+
+
+class HistoryAnalysisRequest(BaseModel):
+    entity_ids: list[str] = Field(default_factory=list)
+    start_time: datetime | None = None
+    end_time: datetime | None = None
+
+
+class TrainModelRequest(BaseModel):
+    actuator_entity_id: str | None = None
+
+
+class EvaluateModelRequest(BaseModel):
+    model_id: str | None = None
+    actuator_entity_id: str | None = None
+
+
+class WeightOverrideRequest(BaseModel):
+    sensor_weights: dict[str, float] = Field(default_factory=dict)
+    note: str | None = Field(default=None, max_length=500)
+
+
+class AutopilotRunResult(BaseModel):
+    candidates: list[CandidateRecommendation]
+    trained_models: list[ModelRecord]
+    evaluations: list[ModelEvaluation]
+
+
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncIterator[None]:
     global ha_client
     listener_task: asyncio.Task[None] | None = None
+    autopilot_task: asyncio.Task[None] | None = None
     ha_client = _ha_client_from_env()
     if ha_client is not None:
         _load_initial_ha_states(ha_client)
         listener_task = asyncio.create_task(_ha_listener_loop(ha_client))
+    autopilot_task = asyncio.create_task(_autopilot_loop())
     try:
         yield
     finally:
-        if listener_task is not None:
-            listener_task.cancel()
-            with suppress(asyncio.CancelledError):
-                await listener_task
+        for task in (listener_task, autopilot_task):
+            if task is not None:
+                task.cancel()
+                with suppress(asyncio.CancelledError):
+                    await task
 
 
 app = FastAPI(
     title="SillyHome Future API",
     description="SillyHome v2 event-core side project.",
-    version="2.0.0-alpha.9",
+    version="2.0.0-alpha.13",
     lifespan=lifespan,
 )
 
@@ -143,12 +208,275 @@ def dashboard_data() -> dict[str, object]:
         "global_enabled": control.global_enabled,
         "learning_profiles": len(learning.profiles),
         "control_profiles": len(control.profiles),
+        "automation_proposals": len(learning.automation_proposals),
+        "models": len(learning.models),
+        "jobs": len(learning.jobs),
+        "candidates": len(learning.candidates),
+        "autopilot_enabled": control.autopilot.enabled,
+        "autopilot_last_run_at": control.autopilot.last_run_at,
         "rooms": list(learning.rooms.values()),
         "scenes": list(learning.scenes.values()),
         "audit": latest_audit,
     }
 
 
+@app.get("/v2/feature-parity")
+def feature_parity() -> dict[str, object]:
+    return {
+        "baseline": "sillyhome-next",
+        "policy": "Future implementiert eigene v2-Funktionen, kein next-Code.",
+        "implemented": [
+            "HA REST/WebSocket",
+            "Service-Ausfuehrung",
+            "Dashboard",
+            "Aktor-Control",
+            "Lernmuster",
+            "Dry-run",
+            "Safety-Gates",
+            "Feedback",
+            "Backup/Restore",
+            "Raeume/Szenen",
+            "Not-Aus",
+            "Simulation",
+            "Audit",
+            "Health",
+            "Automation-Proposals",
+            "YAML-Export",
+            "History-Analyse",
+            "lokales Modelltraining",
+            "Sensor-Gewichte",
+            "Job-Queue",
+            "Autopilot light",
+            "Kandidaten-Vorschlaege",
+            "periodisches Training/Bewertung",
+        ],
+        "next_to_expand": ["Dashboard-Flaechen fuer Autopilot/History/Modelle"],
+    }
+
+
+@app.get("/v2/autopilot/settings", response_model=AutopilotSettings)
+def get_autopilot_settings() -> AutopilotSettings:
+    return stores.control().autopilot
+
+
+@app.put("/v2/autopilot/settings", response_model=AutopilotSettings)
+def put_autopilot_settings(settings: AutopilotSettings) -> AutopilotSettings:
+    control = stores.control().model_copy(update={"autopilot": settings})
+    stores.save_control(control)
+    _append_audit(
+        "autopilot",
+        None,
+        "Autopilot light aktiviert." if settings.enabled else "Autopilot light deaktiviert.",
+    )
+    return settings
+
+
+@app.post("/v2/autopilot/run", response_model=AutopilotRunResult)
+def run_autopilot() -> AutopilotRunResult:
+    return _run_autopilot_once()
+
+
+@app.get("/v2/autopilot/candidates", response_model=list[CandidateRecommendation])
+def list_candidates() -> list[CandidateRecommendation]:
+    return list(stores.learning().candidates.values())
+
+
+@app.post("/v2/autopilot/candidates/{candidate_id}/accept", response_model=LearningProfile)
+def accept_candidate(candidate_id: str) -> LearningProfile:
+    state = stores.learning()
+    candidate = state.candidates[candidate_id].model_copy(
+        update={"status": CandidateStatus.ACCEPTED}
+    )
+    state.candidates[candidate_id] = candidate
+    profile = state.profiles.get(
+        candidate.actuator_entity_id,
+        LearningProfile(actuator_entity_id=candidate.actuator_entity_id),
+    )
+    profile = profile.model_copy(
+        update={
+            "patterns": [
+                *profile.patterns,
+                BehaviorPatternV2(
+                    actuator_entity_id=candidate.actuator_entity_id,
+                    target_state=candidate.target_state,
+                    trigger_entity_id=candidate.trigger_entity_id,
+                    trigger_state=candidate.trigger_state,
+                    support=3,
+                    confidence=candidate.confidence,
+                    source="autopilot",
+                ),
+            ],
+            "model_version": "autopilot-light",
+        }
+    )
+    state.profiles[candidate.actuator_entity_id] = profile
+    stores.save_learning(state)
+    _append_job("autopilot_candidate", f"Kandidat {candidate_id} akzeptiert.")
+    return profile
+
+
+@app.post("/v2/autopilot/candidates/{candidate_id}/dismiss", response_model=CandidateRecommendation)
+def dismiss_candidate(candidate_id: str) -> CandidateRecommendation:
+    state = stores.learning()
+    candidate = state.candidates[candidate_id].model_copy(
+        update={"status": CandidateStatus.DISMISSED}
+    )
+    state.candidates[candidate_id] = candidate
+    stores.save_learning(state)
+    _append_job("autopilot_candidate", f"Kandidat {candidate_id} verworfen.")
+    return candidate
+
+
+@app.post("/v2/automations/proposals", response_model=AutomationProposal)
+def create_automation_proposal(payload: AutomationProposalRequest) -> AutomationProposal:
+    state = stores.learning()
+    proposal_id = f"proposal-{len(state.automation_proposals) + 1}"
+    proposal = AutomationProposal(
+        proposal_id=proposal_id,
+        name=payload.name,
+        trigger_entity_id=payload.trigger_entity_id,
+        trigger_state=payload.trigger_state,
+        actuator_entity_id=payload.actuator_entity_id,
+        target_state=payload.target_state,
+    )
+    state.automation_proposals[proposal_id] = proposal
+    stores.save_learning(state)
+    _append_job("automation_proposal", f"Automation-Vorschlag {proposal_id} angelegt.")
+    return proposal
+
+
+@app.get("/v2/automations/proposals", response_model=list[AutomationProposal])
+def list_automation_proposals() -> list[AutomationProposal]:
+    return list(stores.learning().automation_proposals.values())
+
+
+@app.post("/v2/automations/proposals/{proposal_id}/approve", response_model=AutomationProposal)
+def approve_automation_proposal(proposal_id: str) -> AutomationProposal:
+    return _decide_automation_proposal(proposal_id, ProposalStatus.APPROVED)
+
+
+@app.post("/v2/automations/proposals/{proposal_id}/reject", response_model=AutomationProposal)
+def reject_automation_proposal(proposal_id: str) -> AutomationProposal:
+    return _decide_automation_proposal(proposal_id, ProposalStatus.REJECTED)
+
+
+@app.get("/v2/automations/proposals/{proposal_id}/yaml")
+def automation_proposal_yaml(proposal_id: str) -> str:
+    proposal = stores.learning().automation_proposals[proposal_id]
+    return "\n".join(
+        [
+            f"alias: {proposal.name}",
+            "trigger:",
+            "  - platform: state",
+            f"    entity_id: {proposal.trigger_entity_id}",
+            f"    to: {proposal.trigger_state or ''}",
+            "action:",
+            "  - service: homeassistant.turn_on",
+            "    target:",
+            f"      entity_id: {proposal.actuator_entity_id}",
+            "    data:",
+            f"      target_state: {proposal.target_state}",
+            "mode: single",
+            "",
+        ]
+    )
+
+
+@app.post("/v2/history/analyze", response_model=list[HistoryAnalysis])
+def analyze_history(request: HistoryAnalysisRequest) -> list[HistoryAnalysis]:
+    runtime = stores.runtime()
+    ids = request.entity_ids or list(runtime.entities)[:50]
+    history: dict[str, list[StateEvent]] = {}
+    if ha_client is not None and request.start_time is not None and request.end_time is not None:
+        history = ha_client.read_history(ids, request.start_time, request.end_time)
+    result: list[HistoryAnalysis] = []
+    for entity_id in ids:
+        entity = runtime.entities.get(entity_id)
+        samples = history.get(entity_id, [])
+        matching_audit = [item for item in runtime.audit if item.entity_id == entity_id]
+        states = [sample.new_state for sample in samples if sample.new_state is not None]
+        result.append(
+            HistoryAnalysis(
+                entity_id=entity_id,
+                samples=max(1, len(samples) or len(matching_audit)),
+                last_state=states[-1] if states else (entity.state if entity else None),
+                changed_at=samples[-1].changed_at if samples else (entity.changed_at if entity else None),
+                unique_states=len(set(states)) if states else int(entity is not None),
+                transitions=_count_transitions(states),
+                recommendation=(
+                    "Als Trigger geeignet."
+                    if entity is not None and entity.domain in {"binary_sensor", "sensor"}
+                    else "Als Aktor oder Kontext pruefen."
+                ),
+            )
+        )
+    _append_job("history_analysis", f"History-Analyse fuer {len(result)} Entities erstellt.")
+    return result
+
+
+@app.post("/v2/models/train", response_model=list[ModelRecord])
+def train_models(request: TrainModelRequest) -> list[ModelRecord]:
+    state = stores.learning()
+    actuator_ids = [request.actuator_entity_id] if request.actuator_entity_id else list(state.profiles)
+    trained: list[ModelRecord] = []
+    for actuator_id in actuator_ids:
+        if actuator_id is None:
+            continue
+        profile = state.profiles.get(actuator_id)
+        pattern_count = len(profile.patterns) if profile else 0
+        confidence = (
+            sum(pattern.confidence for pattern in profile.patterns) / pattern_count
+            if profile and pattern_count
+            else 0.0
+        )
+        model = ModelRecord(
+            model_id=f"model-{actuator_id}-{len(state.models) + 1}",
+            actuator_entity_id=actuator_id,
+            pattern_count=pattern_count,
+            confidence=confidence,
+        )
+        state.models[model.model_id] = model
+        trained.append(model)
+    stores.save_learning(state)
+    _append_job("model_training", f"{len(trained)} lokale Modelle trainiert.")
+    return trained
+
+
+@app.get("/v2/models", response_model=list[ModelRecord])
+def list_models() -> list[ModelRecord]:
+    return list(stores.learning().models.values())
+
+
+@app.post("/v2/models/evaluate", response_model=list[ModelEvaluation])
+def evaluate_models(request: EvaluateModelRequest) -> list[ModelEvaluation]:
+    state = stores.learning()
+    control = stores.control()
+    models = list(state.models.values())
+    if request.model_id:
+        models = [model for model in models if model.model_id == request.model_id]
+    if request.actuator_entity_id:
+        models = [model for model in models if model.actuator_entity_id == request.actuator_entity_id]
+    evaluations = [
+        _evaluate_model(model, state, control)
+        for model in models
+    ]
+    for evaluation in evaluations:
+        state.model_evaluations[evaluation.evaluation_id] = evaluation
+    stores.save_learning(state)
+    _append_job("model_evaluation", f"{len(evaluations)} Modelle bewertet.")
+    return evaluations
+
+
+@app.get("/v2/models/evaluations", response_model=list[ModelEvaluation])
+def list_model_evaluations() -> list[ModelEvaluation]:
+    return list(stores.learning().model_evaluations.values())[-100:]
+
+
+@app.get("/v2/jobs", response_model=list[JobQueueItem])
+def list_jobs() -> list[JobQueueItem]:
+    return list(stores.learning().jobs.values())[-100:]
+
+
 @app.post("/v2/events/state", response_model=list[AuditEvent])
 def ingest_state_event(event: StateEvent) -> list[AuditEvent]:
     return event_core.process_state_event(event, execute=_execute_ha_decision)
@@ -203,6 +531,45 @@ def get_control() -> ControlState:
     return stores.control()
 
 
+@app.get("/v2/actuators/summary", response_model=list[ActuatorSummary])
+def actuator_summary() -> list[ActuatorSummary]:
+    learning = stores.learning()
+    control = stores.control()
+    ids = sorted(set(learning.profiles) | set(control.profiles))
+    return [
+        ActuatorSummary(
+            actuator_entity_id=actuator_id,
+            stage=control.profiles.get(
+                actuator_id,
+                ControlProfile(actuator_entity_id=actuator_id),
+            ).stage,
+            handoff_mode=control.profiles.get(
+                actuator_id,
+                ControlProfile(actuator_entity_id=actuator_id),
+            ).handoff_mode,
+            active_ready=control.profiles.get(
+                actuator_id,
+                ControlProfile(actuator_entity_id=actuator_id),
+            ).active_ready,
+            pattern_count=len(
+                learning.profiles.get(
+                    actuator_id,
+                    LearningProfile(actuator_entity_id=actuator_id),
+                ).patterns
+            ),
+            feedback_positive=learning.profiles.get(
+                actuator_id,
+                LearningProfile(actuator_entity_id=actuator_id),
+            ).feedback_positive,
+            feedback_negative=learning.profiles.get(
+                actuator_id,
+                LearningProfile(actuator_entity_id=actuator_id),
+            ).feedback_negative,
+        )
+        for actuator_id in ids
+    ]
+
+
 @app.post("/v2/control/global", response_model=ControlState)
 def set_global_control(update: GlobalControlUpdate) -> ControlState:
     state = stores.control().model_copy(update={"global_enabled": update.enabled})
@@ -305,6 +672,118 @@ def create_learning_pattern(
     return updated
 
 
+@app.post("/v2/control/{actuator_entity_id}/feedback", response_model=LearningProfile)
+def record_feedback(actuator_entity_id: str, feedback: FeedbackRequest) -> LearningProfile:
+    state = stores.learning()
+    profile = state.profiles.get(
+        actuator_entity_id,
+        LearningProfile(actuator_entity_id=actuator_entity_id),
+    )
+    positive_kinds = {"correct", "too_late_fixed", "too_early_fixed"}
+    negative_kinds = {"wrong", "too_early", "too_late", "never_automate"}
+    updated = profile.model_copy(
+        update={
+            "feedback_positive": profile.feedback_positive
+            + int(feedback.kind in positive_kinds),
+            "feedback_negative": profile.feedback_negative
+            + int(feedback.kind in negative_kinds),
+        }
+    )
+    state.profiles[actuator_entity_id] = updated
+    stores.save_learning(state)
+    if feedback.kind == "never_automate":
+        control = stores.control()
+        control.profiles[actuator_entity_id] = control.profiles.get(
+            actuator_entity_id,
+            ControlProfile(actuator_entity_id=actuator_entity_id),
+        ).model_copy(update={"manual_block": True, "stage": SafetyStage.BLOCKED})
+        stores.save_control(control)
+    _append_audit("feedback", actuator_entity_id, f"Feedback: {feedback.kind}")
+    return updated
+
+
+@app.post("/v2/control/{actuator_entity_id}/weights", response_model=SensorWeightOverride)
+def set_weight_override(
+    actuator_entity_id: str,
+    payload: WeightOverrideRequest,
+) -> SensorWeightOverride:
+    state = stores.learning()
+    override = SensorWeightOverride(
+        actuator_entity_id=actuator_entity_id,
+        sensor_weights=payload.sensor_weights,
+        note=payload.note,
+    )
+    state.weight_overrides[actuator_entity_id] = override
+    stores.save_learning(state)
+    _append_audit("weights", actuator_entity_id, "Sensor-Gewichte aktualisiert.")
+    return override
+
+
+@app.get("/v2/control/{actuator_entity_id}/weights", response_model=SensorWeightOverride)
+def get_weight_override(actuator_entity_id: str) -> SensorWeightOverride:
+    return stores.learning().weight_overrides.get(
+        actuator_entity_id,
+        SensorWeightOverride(actuator_entity_id=actuator_entity_id),
+    )
+
+
+@app.post("/v2/planning/refresh", response_model=LearningState)
+def refresh_planning() -> LearningState:
+    state = stores.learning()
+    runtime = stores.runtime()
+    rooms = dict(state.rooms)
+    scenes = dict(state.scenes)
+    for entity in runtime.entities.values():
+        room_name = entity.area_name
+        if not room_name:
+            continue
+        room_id = room_name.lower().replace(" ", "_")
+        room = rooms.get(room_id)
+        actuator_ids = []
+        context_ids = []
+        if entity.domain in {"light", "switch", "fan", "cover", "humidifier"}:
+            actuator_ids.append(entity.entity_id)
+        else:
+            context_ids.append(entity.entity_id)
+        if room is None:
+            from app.core.models import RoomProfile
+
+            room = RoomProfile(room_id=room_id, name=room_name)
+        rooms[room_id] = room.model_copy(
+            update={
+                "actuator_entity_ids": sorted(
+                    set(room.actuator_entity_ids) | set(actuator_ids)
+                ),
+                "context_entity_ids": sorted(set(room.context_entity_ids) | set(context_ids)),
+            }
+        )
+    state = state.model_copy(update={"rooms": rooms, "scenes": scenes})
+    stores.save_learning(state)
+    _append_audit("planning", None, "Planung aktualisiert.")
+    return state
+
+
+@app.get("/v2/anomalies")
+def anomalies() -> list[dict[str, object]]:
+    runtime = stores.runtime()
+    result: list[dict[str, object]] = []
+    unavailable = [
+        entity.entity_id
+        for entity in runtime.entities.values()
+        if entity.state in {"unavailable", "unknown"}
+    ][:100]
+    if unavailable:
+        result.append(
+            {
+                "kind": "unavailable_entities",
+                "severity": "warning",
+                "count": len(unavailable),
+                "entities": unavailable,
+            }
+        )
+    return result
+
+
 @app.post("/v2/simulate")
 def simulate_decision(request: SimulationRequest) -> dict[str, object]:
     runtime = stores.runtime()
@@ -457,6 +936,98 @@ async def _ha_listener_loop(client: FutureHaClient) -> None:
             await asyncio.sleep(2)
 
 
+async def _autopilot_loop() -> None:
+    while True:
+        control = stores.control()
+        settings = control.autopilot
+        if settings.enabled:
+            await asyncio.to_thread(_run_autopilot_once)
+        await asyncio.sleep(stores.control().autopilot.interval_seconds)
+
+
+def _run_autopilot_once() -> AutopilotRunResult:
+    control = stores.control()
+    settings = control.autopilot
+    if not settings.enabled:
+        return AutopilotRunResult(candidates=[], trained_models=[], evaluations=[])
+    learning = stores.learning()
+    candidates = _generate_candidates(settings)
+    for candidate in candidates:
+        learning.candidates[candidate.candidate_id] = candidate
+    stores.save_learning(learning)
+    updated_settings = settings.model_copy(update={"last_run_at": datetime.now(timezone.utc)})
+    stores.save_control(control.model_copy(update={"autopilot": updated_settings}))
+    trained = train_models(TrainModelRequest()) if settings.auto_train else []
+    evaluations = evaluate_models(EvaluateModelRequest()) if settings.auto_evaluate else []
+    _append_job("autopilot", f"Autopilot light: {len(candidates)} Kandidaten erzeugt.")
+    return AutopilotRunResult(candidates=candidates, trained_models=trained, evaluations=evaluations)
+
+
+def _generate_candidates(settings: AutopilotSettings) -> list[CandidateRecommendation]:
+    runtime = stores.runtime()
+    existing = stores.learning().candidates
+    actuators = [
+        entity
+        for entity in runtime.entities.values()
+        if entity.domain in {"light", "switch", "fan", "cover", "humidifier"}
+    ][:150]
+    triggers = [
+        entity
+        for entity in runtime.entities.values()
+        if entity.domain in {"binary_sensor", "sensor"}
+    ][:500]
+    result: list[CandidateRecommendation] = []
+    for actuator in actuators:
+        best_trigger: EntityState | None = None
+        best_score = 0.0
+        best_reason = ""
+        for trigger in triggers:
+            score, reason = _candidate_score(actuator, trigger)
+            if score > best_score:
+                best_score = score
+                best_reason = reason
+                best_trigger = trigger
+        if best_trigger is None or best_score < settings.min_candidate_confidence:
+            continue
+        candidate_id = f"{actuator.entity_id}:{best_trigger.entity_id}:on"
+        if candidate_id in existing:
+            continue
+        result.append(
+            CandidateRecommendation(
+                candidate_id=candidate_id,
+                actuator_entity_id=actuator.entity_id,
+                trigger_entity_id=best_trigger.entity_id,
+                trigger_state="on" if best_trigger.domain == "binary_sensor" else None,
+                target_state="open" if actuator.domain == "cover" else "on",
+                confidence=round(best_score, 2),
+                reason=best_reason,
+            )
+        )
+        if len(result) >= 20:
+            break
+    return result
+
+
+def _candidate_score(actuator: EntityState, trigger: EntityState) -> tuple[float, str]:
+    if actuator.area_name and actuator.area_name == trigger.area_name:
+        return 0.82, f"Gleicher Raum: {actuator.area_name}"
+    actuator_tokens = _entity_tokens(actuator)
+    trigger_tokens = _entity_tokens(trigger)
+    overlap = actuator_tokens & trigger_tokens
+    if overlap:
+        return min(0.78, 0.55 + (0.08 * len(overlap))), (
+            "Aehnliche Namen: " + ", ".join(sorted(overlap)[:4])
+        )
+    if trigger.domain == "binary_sensor" and actuator.domain in {"light", "switch"}:
+        return 0.62, "Binary-Sensor passt grundsaetzlich zu Licht/Schalter."
+    return 0.0, ""
+
+
+def _entity_tokens(entity: EntityState) -> set[str]:
+    raw = f"{entity.entity_id} {entity.friendly_name or ''}".lower()
+    return {part for part in raw.replace(".", "_").split("_") if len(part) >= 4}
+
+
 def _set_websocket_status(status: str) -> None:
     runtime = stores.runtime()
     if runtime.websocket_status == status:
@@ -503,6 +1074,97 @@ def _append_audit(kind: str, entity_id: str | None, message: str) -> None:
     stores.save_runtime(runtime)
 
 
+def _append_job(kind: str, message: str, status: JobStatus = JobStatus.SUCCEEDED) -> JobQueueItem:
+    state = stores.learning()
+    job = JobQueueItem(
+        job_id=f"job-{len(state.jobs) + 1}",
+        kind=kind,
+        status=status,
+        message=message,
+        finished_at=datetime.now(timezone.utc) if status is JobStatus.SUCCEEDED else None,
+    )
+    state.jobs[job.job_id] = job
+    stores.save_learning(state)
+    return job
+
+
+def _decide_automation_proposal(
+    proposal_id: str,
+    status: ProposalStatus,
+) -> AutomationProposal:
+    state = stores.learning()
+    proposal = state.automation_proposals[proposal_id]
+    updated = proposal.model_copy(
+        update={
+            "status": status,
+            "revision": proposal.revision + 1,
+            "decided_at": datetime.now(timezone.utc),
+        }
+    )
+    state.automation_proposals[proposal_id] = updated
+    stores.save_learning(state)
+    _append_job("automation_proposal", f"Automation-Vorschlag {proposal_id}: {status}.")
+    return updated
+
+
+def _count_transitions(states: list[str]) -> int:
+    if not states:
+        return 0
+    transitions = 0
+    previous = states[0]
+    for state in states[1:]:
+        transitions += int(state != previous)
+        previous = state
+    return transitions
+
+
+def _evaluate_model(
+    model: ModelRecord,
+    learning: LearningState,
+    control: ControlState,
+) -> ModelEvaluation:
+    profile = learning.profiles.get(
+        model.actuator_entity_id,
+        LearningProfile(actuator_entity_id=model.actuator_entity_id),
+    )
+    control_profile = control.profiles.get(
+        model.actuator_entity_id,
+        ControlProfile(actuator_entity_id=model.actuator_entity_id),
+    )
+    coverage = min(1.0, model.pattern_count / 5)
+    dry_run_events = max(1, control_profile.dry_run_events)
+    dry_run_success_rate = control_profile.dry_run_successes / dry_run_events
+    feedback_total = profile.feedback_positive + profile.feedback_negative
+    feedback_score = (
+        profile.feedback_positive / feedback_total if feedback_total else 0.5
+    )
+    score = round(
+        (model.confidence * 0.35)
+        + (coverage * 0.2)
+        + (dry_run_success_rate * 0.3)
+        + (feedback_score * 0.15),
+        4,
+    )
+    reasons = [
+        f"Modell-Confidence {model.confidence:.0%}",
+        f"Pattern-Abdeckung {coverage:.0%}",
+        f"Dry-run-Erfolg {dry_run_success_rate:.0%}",
+        f"Feedback-Score {feedback_score:.0%}",
+    ]
+    verdict = "bereit" if score >= 0.82 and control_profile.active_ready else "weiter testen"
+    return ModelEvaluation(
+        evaluation_id=f"eval-{model.model_id}-{len(learning.model_evaluations) + 1}",
+        model_id=model.model_id,
+        actuator_entity_id=model.actuator_entity_id,
+        score=score,
+        coverage=coverage,
+        dry_run_success_rate=dry_run_success_rate,
+        feedback_score=feedback_score,
+        verdict=verdict,
+        reasons=reasons,
+    )
+
+
 def _execute_ha_decision(decision: object) -> bool:
     if ha_client is None or not hasattr(decision, "actuator_entity_id"):
         return False
@@ -554,37 +1216,37 @@ def _dashboard_html() -> str:
     <section class="grid" id="metrics"></section>
     <section class="split">
       <div class="card">
-        <h2>Control</h2>
+        <h2>Steuerung</h2>
         <label for="entitySearch">Suche</label>
         <input id="entitySearch" placeholder="light., switch., sensor..." autocomplete="off">
         <label for="actuator">Aktor</label>
         <select id="actuator"></select>
         <div class="row">
           <div>
-            <label for="minConfidence">Min. Confidence</label>
+            <label for="minConfidence">Mindest-Sicherheit</label>
             <input id="minConfidence" type="number" min="0" max="1" step="0.01" value="0.82">
           </div>
           <div>
-            <label for="cooldown">Cooldown Sekunden</label>
+            <label for="cooldown">Sperrzeit in Sekunden</label>
             <input id="cooldown" type="number" min="0" step="30" value="900">
           </div>
         </div>
         <label><input id="manualBlock" type="checkbox" style="width:auto;margin-right:6px"> Manuell blockieren</label>
         <div class="stages" id="stages"></div>
-        <button class="primary" id="saveControl">Control speichern</button>
+        <button class="primary" id="saveControl">Steuerung speichern</button>
         <button id="globalToggle">Globaler Not-Aus</button>
         <div class="status" id="readiness">Ready-Status wird geladen...</div>
         <div class="status" id="controlStatus"></div>
       </div>
       <div class="card">
-        <h2>Learning Pattern</h2>
+        <h2>Lernmuster</h2>
         <div class="row">
           <div>
             <label for="trigger">Trigger</label>
             <select id="trigger"></select>
           </div>
           <div>
-            <label for="triggerState">Trigger State</label>
+            <label for="triggerState">Trigger-Zustand</label>
             <input id="triggerState" placeholder="on, off, open...">
           </div>
         </div>
@@ -594,22 +1256,28 @@ def _dashboard_html() -> str:
             <input id="targetState" value="on">
           </div>
           <div>
-            <label for="patternConfidence">Confidence</label>
+            <label for="patternConfidence">Sicherheit</label>
             <input id="patternConfidence" type="number" min="0" max="1" step="0.01" value="0.9">
           </div>
         </div>
-        <button class="primary" id="addPattern">Pattern anlegen</button>
-        <button id="simulatePattern">Pattern simulieren</button>
+        <button class="primary" id="addPattern">Lernmuster anlegen</button>
+        <button id="simulatePattern">Lernmuster simulieren</button>
         <div class="status" id="patternStatus"></div>
         <h2>Aktuelles Profil</h2>
         <pre id="profile">Lade...</pre>
       </div>
     </section>
-    <h2>Audit</h2>
+    <h2>Prüfprotokoll</h2>
     <pre id="audit">Lade...</pre>
   </main>
   <script>
     const stages = ['observe', 'dry_run', 'active', 'blocked'];
+    const stageLabels = {
+      observe: 'Beobachten',
+      dry_run: 'Testlauf',
+      active: 'Aktiv',
+      blocked: 'Gesperrt',
+    };
     const state = { entities: [], control: {}, learning: {}, selectedStage: 'observe' };
 
     function optionText(entity) {
@@ -625,7 +1293,7 @@ def _dashboard_html() -> str:
 
     function renderStages() {
       document.getElementById('stages').innerHTML = stages.map(stage =>
-        `<button type="button" data-stage="${stage}" class="${stage === state.selectedStage ? 'active' : ''}">${stage}</button>`
+        `<button type="button" data-stage="${stage}" class="${stage === state.selectedStage ? 'active' : ''}">${stageLabels[stage]}</button>`
       ).join('');
       document.querySelectorAll('[data-stage]').forEach(button => {
         button.onclick = () => { state.selectedStage = button.dataset.stage; renderStages(); };
@@ -674,13 +1342,18 @@ def _dashboard_html() -> str:
       state.learning = await learningResponse.json();
       const metrics = [
         ['WebSocket', data.websocket_status],
-        ['Entities', data.entity_count],
-        ['Actuators', data.actuator_count],
-        ['Global', data.global_enabled ? 'on' : 'off'],
-        ['Learning', data.learning_profiles],
-        ['Control', data.control_profiles],
-        ['Rooms', data.rooms.length],
-        ['Scenes', data.scenes.length],
+        ['Entitäten', data.entity_count],
+        ['Aktoren', data.actuator_count],
+        ['Not-Aus', data.global_enabled ? 'frei' : 'aktiv'],
+        ['Lernen', data.learning_profiles],
+        ['Steuerung', data.control_profiles],
+        ['Vorschlaege', data.automation_proposals],
+        ['Modelle', data.models],
+        ['Jobs', data.jobs],
+        ['Kandidaten', data.candidates],
+        ['Autopilot', data.autopilot_enabled ? 'aktiv' : 'aus'],
+        ['Räume', data.rooms.length],
+        ['Szenen', data.scenes.length],
       ];
       document.getElementById('metrics').innerHTML = metrics.map(([label, value]) =>
         `<div class="card"><div class="label">${label}</div><div class="value">${value}</div></div>`
@@ -696,7 +1369,7 @@ def _dashboard_html() -> str:
       const response = await fetch(`/v2/control/${encodeURIComponent(id)}/readiness`);
       const data = await response.json();
       document.getElementById('readiness').textContent =
-        `${data.ready ? 'ready for active' : 'nicht active-ready'} - ${data.reason}`;
+        `${data.ready ? 'bereit fuer Aktiv' : 'nicht bereit fuer Aktiv'} - ${data.reason}`;
     }
 
     document.getElementById('entitySearch').oninput = renderEntities;

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-future"
-version = "2.0.0-alpha.9"
+version = "2.0.0-alpha.13"
 description = "SillyHome v2 event-core prototype"
 requires-python = ">=3.11"
 dependencies = [

tests/test_addon_config.py

@@ -9,10 +9,12 @@ def test_addon_config_declares_future_addon() -> None:
     config = yaml.safe_load(Path("addon/config.yaml").read_text(encoding="utf-8"))
 
     assert config["slug"] == "sillyhome_future"
-    assert config["version"] == "2.0.0-alpha.9"
+    assert config["version"] == "2.0.0-alpha.13"
     assert config["ingress"] is True
     assert config["ingress_port"] == 8099
     assert config["homeassistant_api"] is True
+    assert config["boot"] == "auto"
+    assert config["watchdog"] == "http://[HOST]:[PORT:8099]/health"
 
 
 def test_repository_points_to_gitea_repo() -> None:

tests/test_future_api.py

@@ -19,7 +19,7 @@ def test_health_and_backup_roundtrip(tmp_path, monkeypatch) -> None:  # type: ig
     restore = client.post("/v2/backup/restore", json=backup.json())
 
     assert health.status_code == 200
-    assert health.json()["version"] == "2.0.0-alpha.9"
+    assert health.json()["version"] == "2.0.0-alpha.13"
     assert backup.status_code == 200
     assert restore.status_code == 200
     assert restore.json() == {"status": "restored"}
@@ -39,6 +39,13 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
                     entity_id="binary_sensor.storage_door",
                     domain="binary_sensor",
                     state="off",
+                    area_name="Storage",
+                ),
+                "light.storage_door": EntityState(
+                    entity_id="light.storage_door",
+                    domain="light",
+                    state="off",
+                    area_name="Storage",
                 ),
             }
         )
@@ -77,12 +84,46 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
     readiness = client.get("/v2/control/light.storage/readiness")
     global_control = client.post("/v2/control/global", json={"enabled": False})
     detailed_health = client.get("/v2/health")
+    feedback = client.post("/v2/control/light.storage/feedback", json={"kind": "wrong"})
+    summary = client.get("/v2/actuators/summary")
+    parity = client.get("/v2/feature-parity")
+    anomalies = client.get("/v2/anomalies")
+    proposal = client.post(
+        "/v2/automations/proposals",
+        json={
+            "name": "Storage light",
+            "trigger_entity_id": "binary_sensor.storage_door",
+            "trigger_state": "on",
+            "actuator_entity_id": "light.storage",
+            "target_state": "on",
+        },
+    )
+    proposal_yaml = client.get("/v2/automations/proposals/proposal-1/yaml")
+    approved = client.post("/v2/automations/proposals/proposal-1/approve")
+    weights = client.post(
+        "/v2/control/light.storage/weights",
+        json={"sensor_weights": {"binary_sensor.storage_door": 1.0}, "note": "door"},
+    )
+    history = client.post(
+        "/v2/history/analyze",
+        json={"entity_ids": ["binary_sensor.storage_door"]},
+    )
+    models = client.post("/v2/models/train", json={"actuator_entity_id": "light.storage"})
+    evaluation = client.post("/v2/models/evaluate", json={"actuator_entity_id": "light.storage"})
+    evaluations = client.get("/v2/models/evaluations")
+    autopilot = client.post("/v2/autopilot/run")
+    candidates = client.get("/v2/autopilot/candidates")
+    accepted = client.post(
+        f"/v2/autopilot/candidates/{autopilot.json()['candidates'][0]['candidate_id']}/accept"
+    )
+    jobs = client.get("/v2/jobs")
     dashboard = client.get("/v2/dashboard")
 
     assert entities.status_code == 200
     assert [item["entity_id"] for item in entities.json()] == [
         "binary_sensor.storage_door",
         "light.storage",
+        "light.storage_door",
     ]
     assert control.status_code == 200
     assert control.json()["stage"] == "dry_run"
@@ -96,5 +137,35 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
     assert global_control.json()["global_enabled"] is False
     assert detailed_health.status_code == 200
     assert detailed_health.json()["global_enabled"] is False
+    assert feedback.status_code == 200
+    assert feedback.json()["feedback_negative"] == 1
+    assert summary.status_code == 200
+    assert summary.json()[0]["actuator_entity_id"] == "light.storage"
+    assert parity.status_code == 200
+    assert "Job-Queue" in parity.json()["implemented"]
+    assert anomalies.status_code == 200
+    assert proposal.status_code == 200
+    assert proposal.json()["status"] == "draft"
+    assert proposal_yaml.status_code == 200
+    assert "alias: Storage light" in proposal_yaml.text
+    assert approved.status_code == 200
+    assert approved.json()["status"] == "approved"
+    assert weights.status_code == 200
+    assert weights.json()["sensor_weights"]["binary_sensor.storage_door"] == 1.0
+    assert history.status_code == 200
+    assert history.json()[0]["entity_id"] == "binary_sensor.storage_door"
+    assert models.status_code == 200
+    assert models.json()[0]["actuator_entity_id"] == "light.storage"
+    assert evaluation.status_code == 200
+    assert evaluation.json()[0]["verdict"] in {"bereit", "weiter testen"}
+    assert evaluations.status_code == 200
+    assert evaluations.json()
+    assert autopilot.status_code == 200
+    assert autopilot.json()["candidates"]
+    assert candidates.status_code == 200
+    assert accepted.status_code == 200
+    assert jobs.status_code == 200
+    assert jobs.json()
     assert dashboard.status_code == 200
-    assert dashboard.json()["actuator_count"] == 1
+    assert dashboard.json()["actuator_count"] == 2
+    assert dashboard.json()["automation_proposals"] == 1