Add Future parity feature blocks

Enable watchdog and localize dashboard
2026-06-18 16:20:30 +02:00 · 2026-06-18 14:26:26 +02:00
7 changed files with 528 additions and 26 deletions
--- a/addon/Dockerfile
+++ b/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.11
 RUN python -m pip install --no-cache-dir \
    "http://192.168.6.31:3000/Otto/sillyhome-future/archive/${SILLYHOME_FUTURE_REF}.tar.gz"

--- a/addon/config.yaml
+++ b/addon/config.yaml
@@ -1,12 +1,12 @@
 name: SillyHome Future
-version: "2.0.0-alpha.9"
+version: "2.0.0-alpha.11"
 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
--- a/app/core/models.py
+++ b/app/core/models.py
@@ -21,6 +21,19 @@ 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 EntityState(BaseModel):
    entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
    domain: str
@@ -89,6 +102,51 @@ 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 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 HistoryAnalysis(BaseModel):
+    entity_id: str
+    samples: int
+    last_state: str | None = None
+    changed_at: datetime | None = None
+    recommendation: str
+
+
 class Decision(BaseModel):
    actuator_entity_id: str
    target_state: str | None = None
@@ -121,6 +179,10 @@ 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)
+    jobs: dict[str, JobQueueItem] = Field(default_factory=dict)
+    weight_overrides: dict[str, SensorWeightOverride] = Field(default_factory=dict)


 class ControlState(BaseModel):
--- a/app/main.py
+++ b/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 +16,23 @@ from app.core.ha_client import FutureHaClient, HaClientConfig, service_for_state
 from app.core.handoff import HandoffMatrix
 from app.core.models import (
    AuditEvent,
+    AutomationProposal,
    BackupBundle,
    BehaviorPatternV2,
    ControlProfile,
    ControlState,
    EntityState,
+    HistoryAnalysis,
    HandoffMode,
+    JobQueueItem,
+    JobStatus,
    LearningProfile,
    LearningState,
+    ModelRecord,
+    ProposalStatus,
    RuntimeState,
    SafetyStage,
+    SensorWeightOverride,
    StateEvent,
 )
 from app.core.stores import FutureStores
@@ -70,6 +78,43 @@ 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)
+
+
+class TrainModelRequest(BaseModel):
+    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)
+
+
@asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncIterator[None]:
    global ha_client
@@ -90,7 +135,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
    title="SillyHome Future API",
    description="SillyHome v2 event-core side project.",
-    version="2.0.0-alpha.9",
+    version="2.0.0-alpha.11",
    lifespan=lifespan,
 )

@@ -143,12 +188,164 @@ 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),
        "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",
+        ],
+        "next_to_expand": ["echte Langzeit-History aus HA", "fortgeschrittene Modellbewertung"],
+    }
+
+
+@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]
+    result: list[HistoryAnalysis] = []
+    for entity_id in ids:
+        entity = runtime.entities.get(entity_id)
+        matching_audit = [item for item in runtime.audit if item.entity_id == entity_id]
+        result.append(
+            HistoryAnalysis(
+                entity_id=entity_id,
+                samples=max(1, len(matching_audit)),
+                last_state=entity.state if entity else None,
+                changed_at=entity.changed_at if entity else None,
+                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.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 +400,45 @@ 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 +541,118 @@ 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()
@@ -503,6 +851,39 @@ 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 _execute_ha_decision(decision: object) -> bool:
    if ha_client is None or not hasattr(decision, "actuator_entity_id"):
        return False
@@ -554,37 +935,37 @@ 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 +975,28 @@ 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 +1012,7 @@ 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 +1061,16 @@ 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],
+        ['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 +1086,7 @@ 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;
--- a/pyproject.toml
+++ b/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.11"
 description = "SillyHome v2 event-core prototype"
 requires-python = ">=3.11"
 dependencies = [
--- a/tests/test_addon_config.py
+++ b/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.11"
    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:
--- a/tests/test_future_api.py
+++ b/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.11"
    assert backup.status_code == 200
    assert restore.status_code == 200
    assert restore.json() == {"status": "restored"}
@@ -77,6 +77,32 @@ 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"})
+    jobs = client.get("/v2/jobs")
    dashboard = client.get("/v2/dashboard")

    assert entities.status_code == 200
@@ -96,5 +122,27 @@ 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 jobs.status_code == 200
+    assert jobs.json()
    assert dashboard.status_code == 200
    assert dashboard.json()["actuator_count"] == 1
+    assert dashboard.json()["automation_proposals"] == 1
Author	SHA1	Message	Date
Otto	07e3e96c30	Add Future parity feature blocks	2026-06-18 16:20:30 +02:00
Otto	e4b860571a	Enable watchdog and localize dashboard	2026-06-18 14:26:26 +02:00