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.12
+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

@@ -1,5 +1,5 @@
 name: SillyHome Future
-version: "2.0.0-alpha.12"
+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

app/core/models.py

@@ -34,6 +34,12 @@ class JobStatus(StrEnum):
     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
@@ -152,6 +158,28 @@ class SensorWeightOverride(BaseModel):
     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
@@ -199,11 +227,13 @@ class LearningState(BaseModel):
     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

@@ -16,9 +16,12 @@ 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,
@@ -123,27 +126,36 @@ class WeightOverrideRequest(BaseModel):
     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:
+        for task in (listener_task, autopilot_task):
-            listener_task.cancel()
+            if task is not None:
+                task.cancel()
                 with suppress(asyncio.CancelledError):
-                await listener_task
+                    await task
 
 
 app = FastAPI(
     title="SillyHome Future API",
     description="SillyHome v2 event-core side project.",
-    version="2.0.0-alpha.12",
+    version="2.0.0-alpha.13",
     lifespan=lifespan,
 )
 
@@ -199,6 +211,9 @@ def dashboard_data() -> dict[str, object]:
         "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,
@@ -231,11 +246,87 @@ def feature_parity() -> dict[str, object]:
             "lokales Modelltraining",
             "Sensor-Gewichte",
             "Job-Queue",
+            "Autopilot light",
+            "Kandidaten-Vorschlaege",
+            "periodisches Training/Bewertung",
         ],
-        "next_to_expand": ["Dashboard-Flaechen fuer History/Modelle"],
+        "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()
@@ -845,6 +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:
@@ -1167,6 +1350,8 @@ def _dashboard_html() -> str:
         ['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],
       ];

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-future"
-version = "2.0.0-alpha.12"
+version = "2.0.0-alpha.13"
 description = "SillyHome v2 event-core prototype"
 requires-python = ">=3.11"
 dependencies = [

tests/test_addon_config.py

@@ -9,7 +9,7 @@ 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.12"
+    assert config["version"] == "2.0.0-alpha.13"
     assert config["ingress"] is True
     assert config["ingress_port"] == 8099
     assert config["homeassistant_api"] is True

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.12"
+    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",
                 ),
             }
         )
@@ -104,6 +111,11 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
     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")
 
@@ -111,6 +123,7 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
     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"
@@ -147,8 +160,12 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
     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