Release SillyHome Next 1.7.4

CHANGELOG.md

@@ -1,5 +1,23 @@
 # Changelog
 
+## 1.7.4 - 2026-07-26
+- Dashboard-Sprachumschaltung aktualisiert statische Texte, Labels,
+  Platzhalter und wichtige Laufzeittexte direkt beim Wechsel.
+- Verhaltenslernen speichert Lichtattribute wie Helligkeit und Farbwerte aus
+  der Home-Assistant-Historie und gibt sie bei Lichtvorhersagen an den
+  `light.turn_on` Service weiter.
+- Kontext-Discovery erkennt Umlaute/Raumvarianten robuster, ignoriert
+  Markenwörter wie `lidl` als falsche Gemeinsamkeit und bevorzugt Raum-
+  Präsenzsensoren für Lidl-/Treppenlichter.
+- Lüftungen bevorzugen Luftfeuchte und Belegungs-/Präsenzkontext; gelernte
+  Sensorwechsel dürfen jetzt eine Verzögerung haben, z. B. WC besetzt -> nach
+  2-3 Minuten Lüftung an.
+- Briefkasten-Reset-Buttons können Schrank-/Entnahme-Türen als Kontext
+  erkennen; Button-Aktoren können im aktiven Modus per `press` ausgeführt
+  werden.
+- PV-/Akku-/Verbrauchssensoren werden als Energiemanagement-Kontext stärker
+  einsortiert.
+
 ## 1.7.0 - 2026-06-18
 - Produktiv-Ausbau fuer Schaltvertrauen: persistente Entscheidungs-Timeline,
   Event-Latenzmessungen und Dry-run pro Aktor.

addon/config.yaml

@@ -1,5 +1,5 @@
 name: SillyHome Next
-version: "1.7.3"
+version: "1.7.4"
 slug: sillyhome_next
 description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren
 url: http://192.168.6.31:3000/pino/sillyhome-next

app/actuators/lifecycle.py

@@ -46,6 +46,8 @@ _STOPWORDS = frozenset(
         "entity",
         "humidity",
         "illuminance",
+        "led",
+        "lidl",
         "light",
         "licht",
         "lichtschalter",
@@ -138,6 +140,33 @@ _AUTO_CONTEXT_CLASSES = frozenset({
     "presence",
     "window",
 })
+_PRESENCE_TOKENS = frozenset({
+    "besetzt",
+    "occupied",
+    "occupancy",
+    "presence",
+    "prasenz",
+    "praesenz",
+    "motion",
+    "bewegung",
+    "bewegungsmelder",
+})
+_MAILBOX_TOKENS = frozenset({"briefkasten", "mailbox", "post"})
+_CABINET_TOKENS = frozenset({"schrank", "cabinet"})
+_PV_TOKENS = frozenset({
+    "pv",
+    "solar",
+    "photovoltaik",
+    "akku",
+    "batterie",
+    "battery",
+    "einspeisung",
+    "wechselrichter",
+    "inverter",
+    "netzbezug",
+    "grid",
+    "verbrauch",
+})
 
 
 class ActuatorReconciliationService:
@@ -864,6 +893,18 @@ def _has_context_relationship(actuator: HaEntitySummary, entity: HaEntitySummary
         return True
     if _metadata_tokens(actuator).intersection(_metadata_tokens(entity)):
         return True
+    actuator_tokens = _metadata_tokens(actuator, include_stopwords=True)
+    entity_tokens = _metadata_tokens(entity, include_stopwords=True)
+    if _is_mailbox_reset_candidate(actuator_tokens, entity_tokens, entity):
+        return True
+    if actuator.domain in {"fan", "humidifier"} and (
+        _is_presence_context(entity) or entity.device_class in {"humidity", "moisture"}
+    ):
+        return True
+    if actuator.domain in {"climate", "cover", "fan", "humidifier", "light", "switch"} and (
+        entity_tokens.intersection(_PV_TOKENS)
+    ):
+        return True
     entity_tokens = _metadata_tokens(entity, include_stopwords=True)
     return bool(
         entity_tokens.intersection(_OUTDOOR_TOKENS)
@@ -878,6 +919,18 @@ def _eligible_for_auto_context(
     device_class = candidate.device_class or ""
     if device_class in _AUTO_CONTEXT_CLASSES:
         return True
+    if actuator.domain in {"fan", "humidifier"} and device_class in {
+        "humidity",
+        "moisture",
+        "temperature",
+    }:
+        return True
+    if actuator.domain in {"fan", "humidifier", "light", "switch"} and _is_presence_candidate(candidate):
+        return True
+    actuator_tokens = _metadata_tokens(actuator, include_stopwords=True)
+    candidate_tokens = _candidate_tokens(candidate, include_stopwords=True)
+    if _is_mailbox_reset_candidate(actuator_tokens, candidate_tokens, candidate):
+        return True
     if (
         actuator.device_name
         and candidate.device_name
@@ -899,6 +952,7 @@ def _score_candidate(
     score = 0.0
     actuator_tokens = _metadata_tokens(actuator)
     entity_tokens = _metadata_tokens(entity)
+    full_entity_tokens = _metadata_tokens(entity, include_stopwords=True)
     overlap = sorted(actuator_tokens.intersection(entity_tokens))
     if overlap:
         score += min(0.4, 0.1 * len(overlap))
@@ -924,6 +978,31 @@ def _score_candidate(
     if entity.device_class in preferred_device_classes:
         score += 0.2
         evidence.append(f"Passende device_class: {entity.device_class}")
+    if context and actuator.domain in {"fan", "humidifier"} and entity.device_class in {
+        "humidity",
+        "moisture",
+    }:
+        score += 0.3
+        evidence.append("Luftfeuchtigkeit ist primärer Kontext für Lüftung.")
+    if not context and actuator.domain in {"fan", "humidifier"} and entity.device_class in {
+        "humidity",
+        "moisture",
+    }:
+        score += 0.3
+        evidence.append("Luftfeuchtigkeit ist primärer Messwert für Lüftung.")
+    if context and actuator.domain in {"fan", "humidifier", "light", "switch"} and _is_presence_context(entity):
+        score += 0.3
+        evidence.append("Anwesenheit/Belegung ist primärer Schaltkontext.")
+    if context and _is_mailbox_reset_candidate(
+        _metadata_tokens(actuator, include_stopwords=True),
+        _metadata_tokens(entity, include_stopwords=True),
+        entity,
+    ):
+        score += 0.45
+        evidence.append("Briefkasten-Reset passt zur Schrank-/Entnahme-Tür.")
+    if full_entity_tokens.intersection(_PV_TOKENS):
+        score += 0.12 if context else 0.18
+        evidence.append("PV-/Akku-/Verbrauchswert ist als Energiemanagement-Kontext relevant.")
     if not context and actuator.domain == "light" and entity.device_class == "illuminance":
         score += 0.2
         evidence.append("Beleuchtungsstärke wird für Lichtaktoren bevorzugt.")
@@ -1068,11 +1147,83 @@ def _metadata_tokens(entity: HaEntitySummary, *, include_stopwords: bool = False
     for value in raw_values:
         if value is None:
             continue
-        for token in _TOKEN_PATTERN.findall(value.lower().replace("_", " ")):
-            if len(token) < 3 or (not include_stopwords and token in _STOPWORDS):
+        for token in _TOKEN_PATTERN.findall(_normalize_text(value)):
+            if (len(token) < 3 and token != "wc") or (not include_stopwords and token in _STOPWORDS):
                 continue
             tokens.add(token)
-    return tokens
+    return _expand_room_tokens(tokens)
+
+
+def _candidate_tokens(
+    candidate: AssignmentCandidate,
+    *,
+    include_stopwords: bool = False,
+) -> set[str]:
+    raw_values = [
+        candidate.entity_id,
+        candidate.friendly_name,
+        candidate.area_name,
+        candidate.device_name,
+    ]
+    tokens: set[str] = set()
+    for value in raw_values:
+        if value is None:
+            continue
+        for token in _TOKEN_PATTERN.findall(_normalize_text(value)):
+            if (len(token) < 3 and token != "wc") or (not include_stopwords and token in _STOPWORDS):
+                continue
+            tokens.add(token)
+    return _expand_room_tokens(tokens)
+
+
+def _expand_room_tokens(tokens: set[str]) -> set[str]:
+    expanded = set(tokens)
+    if "gaste" in expanded:
+        expanded.add("gaeste")
+    if {"gaste", "wc"}.issubset(expanded) or {"gaeste", "wc"}.issubset(expanded):
+        expanded.add("gaestewc")
+    if {"gaeste", "zimmer"}.issubset(expanded):
+        expanded.add("gaestezimmer")
+    return expanded
+
+
+def _normalize_text(value: str) -> str:
+    return (
+        value.lower()
+        .replace("_", " ")
+        .replace("ä", "ae")
+        .replace("ö", "oe")
+        .replace("ü", "ue")
+        .replace("ß", "ss")
+    )
+
+
+def _is_presence_context(entity: HaEntitySummary) -> bool:
+    if entity.device_class in {"motion", "occupancy", "presence"}:
+        return True
+    return bool(_metadata_tokens(entity, include_stopwords=True).intersection(_PRESENCE_TOKENS))
+
+
+def _is_presence_candidate(candidate: AssignmentCandidate) -> bool:
+    if candidate.device_class in {"motion", "occupancy", "presence"}:
+        return True
+    return bool(_candidate_tokens(candidate, include_stopwords=True).intersection(_PRESENCE_TOKENS))
+
+
+def _is_mailbox_reset_candidate(
+    actuator_tokens: set[str],
+    context_tokens: set[str],
+    entity: HaEntitySummary | AssignmentCandidate,
+) -> bool:
+    if not actuator_tokens.intersection(_MAILBOX_TOKENS):
+        return False
+    if not context_tokens.intersection(_CABINET_TOKENS):
+        return False
+    return entity.domain == "binary_sensor" and entity.device_class in {
+        "door",
+        "garage_door",
+        "opening",
+    }
 
 
 def _history_signature(sensor_id: str, points: list[NumericHistoryPoint]) -> str:

app/actuators/models.py

@@ -123,12 +123,14 @@ class ModelLifecycleState(BaseModel):
 
 class BehaviorPattern(BaseModel):
     target_state: str = Field(min_length=1, max_length=100)
+    target_attributes: dict[str, object] = Field(default_factory=dict)
     minute_of_day: int = Field(ge=0, le=1439)
     weekday: int = Field(ge=0, le=6)
     context_states: dict[str, str] = Field(default_factory=dict)
     trigger_entity_id: str | None = None
     trigger_from_state: str | None = None
     trigger_to_state: str | None = None
+    trigger_delay_seconds: int | None = Field(default=None, ge=0)
     source: str = Field(default="observed", max_length=40)
     weight: float = Field(default=1.0, ge=0.1, le=1.0)
     observed_at: datetime
@@ -136,6 +138,7 @@ class BehaviorPattern(BaseModel):
 
 class BehaviorPrediction(BaseModel):
     target_state: str
+    target_attributes: dict[str, object] = Field(default_factory=dict)
     confidence: float = Field(ge=0.0, le=1.0)
     generated_at: datetime
     reason: str

app/behavior/engine.py

@@ -48,10 +48,27 @@ _MAX_DECISION_TRACES = 30
 _MAX_LATENCY_MEASUREMENTS = 50
 _MAX_FEEDBACK_LOG = 50
 _ACTION_LOGBOOK_TOLERANCE = timedelta(seconds=10)
-_CONTEXT_TRIGGER_TOLERANCE = timedelta(seconds=3)
+_CONTEXT_TRIGGER_TOLERANCE = timedelta(minutes=4)
 _OWN_ACTION_TOLERANCE = timedelta(seconds=20)
-_SAFE_ACTIVE_DOMAINS = frozenset({"cover", "fan", "humidifier", "light", "switch"})
+_SAFE_ACTIVE_DOMAINS = frozenset({
+    "button",
+    "cover",
+    "fan",
+    "humidifier",
+    "input_button",
+    "light",
+    "switch",
+})
 _AUTOMATION_CONTEXT_DOMAINS = frozenset({"automation", "script"})
+_LIGHT_TARGET_ATTRIBUTES = frozenset({
+    "brightness",
+    "color_temp",
+    "color_temp_kelvin",
+    "effect",
+    "hs_color",
+    "rgb_color",
+    "xy_color",
+})
 logger = logging.getLogger(__name__)
 
 
@@ -339,7 +356,7 @@ class BehaviorEngine:
             now=now,
             min_support=self._settings.min_behavior_actions,
             window_minutes=self._settings.prediction_window_minutes,
-            causal_window_seconds=self._settings.prediction_interval_seconds * 2,
+            causal_window_seconds=max(self._settings.prediction_interval_seconds * 2, 240),
             timezone_name=self._settings.timezone,
         )
         if prediction is not None:
@@ -439,7 +456,11 @@ class BehaviorEngine:
                     self._ha_reader.call_service(
                         domain,
                         service,
-                        {"entity_id": actuator_entity_id},
+                        _service_data_for_prediction(
+                            actuator_entity_id,
+                            domain,
+                            prediction,
+                        ),
                     )
                     decision_to_service_ms = _elapsed_ms(service_started_perf)
                 except (HaClientError, ValueError) as exc:
@@ -1107,7 +1128,7 @@ class BehaviorEngine:
             blockers.append(
                 f"Sicherheit {prediction.confidence:.0%} liegt unter der Schwelle {threshold:.0%}."
             )
-        if current_state == prediction.target_state:
+        if _target_reached(record.actuator_entity_id, current_state, prediction):
             blockers.append("Zielzustand ist bereits erreicht.")
         if not self._cooldown_elapsed(
             record.behavior,
@@ -1150,12 +1171,16 @@ class BehaviorEngine:
             patterns.append(
                 BehaviorPattern(
                     target_state=point.state,
+                    target_attributes=_target_attributes_for(point),
                     minute_of_day=local.hour * 60 + local.minute,
                     weekday=local.weekday(),
                     context_states=contexts,
-                    trigger_entity_id=trigger[0] if trigger else None,
-                    trigger_from_state=trigger[1] if trigger else None,
-                    trigger_to_state=trigger[2] if trigger else None,
+                    trigger_entity_id=trigger[1] if trigger else None,
+                    trigger_from_state=trigger[2] if trigger else None,
+                    trigger_to_state=trigger[3] if trigger else None,
+                    trigger_delay_seconds=(
+                        int(trigger[0].total_seconds()) if trigger else None
+                    ),
                     source=source,
                     weight=weight,
                     observed_at=point.timestamp,
@@ -1941,6 +1966,7 @@ def predict_behavior(
     minute_of_day = local.hour * 60 + local.minute
     changed_at = current_context_changed_at or {}
     by_state: dict[str, list[float]] = {}
+    attributes_by_state: dict[str, list[tuple[float, dict[str, object]]]] = {}
     causal_support_by_state: dict[str, int] = {}
     for pattern in patterns:
         if pattern.trigger_entity_id and pattern.trigger_to_state:
@@ -1954,7 +1980,11 @@ def predict_behavior(
                 current_context.get(pattern.trigger_entity_id)
                 == pattern.trigger_to_state
                 and trigger_age is not None
-                and 0 <= trigger_age <= causal_window_seconds
+                and _trigger_age_matches(
+                    trigger_age,
+                    pattern.trigger_delay_seconds,
+                    causal_window_seconds,
+                )
             ):
                 continue
             comparable = [
@@ -1969,6 +1999,9 @@ def predict_behavior(
             )
             score = pattern.weight * (0.85 + 0.15 * context_score)
             by_state.setdefault(pattern.target_state, []).append(score)
+            attributes_by_state.setdefault(pattern.target_state, []).append(
+                (score, pattern.target_attributes)
+            )
             causal_support_by_state[pattern.target_state] = (
                 causal_support_by_state.get(pattern.target_state, 0) + 1
             )
@@ -1998,6 +2031,9 @@ def predict_behavior(
             0.45 * time_score + 0.45 * context_score + 0.10 * weekday_score
         )
         by_state.setdefault(pattern.target_state, []).append(score)
+        attributes_by_state.setdefault(pattern.target_state, []).append(
+            (score, pattern.target_attributes)
+        )
     if not by_state:
         return None
     target_state, scores = max(
@@ -2011,6 +2047,9 @@ def predict_behavior(
         return None
     return BehaviorPrediction(
         target_state=target_state,
+        target_attributes=_aggregate_target_attributes(
+            attributes_by_state.get(target_state, [])
+        ),
         confidence=round(confidence, 4),
         generated_at=now,
         matching_patterns=support,
@@ -2044,9 +2083,87 @@ def _weighted_context_score(
     return matched_weight / total_weight
 
 
+def _trigger_age_matches(
+    trigger_age_seconds: float,
+    expected_delay_seconds: int | None,
+    causal_window_seconds: int,
+) -> bool:
+    if trigger_age_seconds < 0:
+        return False
+    if expected_delay_seconds is None or expected_delay_seconds <= 10:
+        return trigger_age_seconds <= causal_window_seconds
+    tolerance = max(30, min(90, causal_window_seconds // 2))
+    return abs(trigger_age_seconds - expected_delay_seconds) <= tolerance
+
+
+def _aggregate_target_attributes(
+    weighted_attributes: list[tuple[float, dict[str, object]]],
+) -> dict[str, object]:
+    if not weighted_attributes:
+        return {}
+    result: dict[str, object] = {}
+    numeric_values: dict[str, list[tuple[float, float]]] = {}
+    categorical_values: dict[str, dict[str, float]] = {}
+    for score, attributes in weighted_attributes:
+        for key, value in attributes.items():
+            if key not in _LIGHT_TARGET_ATTRIBUTES:
+                continue
+            if isinstance(value, bool) or value is None:
+                continue
+            if isinstance(value, (int, float)):
+                numeric_values.setdefault(key, []).append((score, float(value)))
+            else:
+                categorical_values.setdefault(key, {}).setdefault(str(value), 0.0)
+                categorical_values[key][str(value)] += score
+    for key, values in numeric_values.items():
+        total_weight = sum(score for score, _ in values)
+        if total_weight <= 0:
+            continue
+        result[key] = round(sum(score * value for score, value in values) / total_weight)
+    for key, values in categorical_values.items():
+        if key in result:
+            continue
+        result[key] = max(values.items(), key=lambda item: (item[1], item[0]))[0]
+    return result
+
+
+def _target_attributes_for(point: StateHistoryPoint) -> dict[str, object]:
+    if point.state != "on":
+        return {}
+    return {
+        key: value
+        for key, value in point.attributes.items()
+        if key in _LIGHT_TARGET_ATTRIBUTES and value is not None
+    }
+
+
+def _service_data_for_prediction(
+    actuator_entity_id: str,
+    domain: str,
+    prediction: BehaviorPrediction,
+) -> dict[str, object]:
+    data: dict[str, object] = {"entity_id": actuator_entity_id}
+    if domain == "light" and prediction.target_state == "on":
+        data.update(prediction.target_attributes)
+    return data
+
+
+def _target_reached(
+    actuator_entity_id: str,
+    current_state: str,
+    prediction: BehaviorPrediction,
+) -> bool:
+    domain = actuator_entity_id.split(".", 1)[0]
+    if domain == "light" and prediction.target_state == "on" and prediction.target_attributes:
+        return False
+    return current_state == prediction.target_state
+
+
 def service_for_state(domain: str, target_state: str) -> str | None:
     if domain in {"fan", "humidifier", "light", "media_player", "remote", "switch"}:
         return {"on": "turn_on", "off": "turn_off"}.get(target_state)
+    if domain in {"button", "input_button"}:
+        return "press"
     if domain == "scene":
         return "turn_on" if target_state == "on" else None
     if domain == "cover":
@@ -2112,7 +2229,7 @@ def _recent_context_transition(
     history: dict[str, StateHistorySeries],
     context_ids: list[str],
     timestamp: datetime,
-) -> tuple[str, str, str] | None:
+) -> tuple[timedelta, str, str, str] | None:
     nearest: tuple[timedelta, str, str, str] | None = None
     for entity_id in context_ids:
         series = history.get(entity_id)
@@ -2131,7 +2248,7 @@ def _recent_context_transition(
             previous_state = point.state
     if nearest is None:
         return None
-    return nearest[1], nearest[2], nearest[3]
+    return nearest
 
 
 def _circular_minute_distance(left: int, right: int) -> int:

app/ha/client.py

@@ -78,7 +78,6 @@ class HaClient:
                 "filter_entity_id": ",".join(entity_ids),
                 "end_time": end_time.isoformat(),
                 "minimal_response": "1",
-                "no_attributes": "1",
             },
         )
         if not isinstance(payload, list):

app/ha/history.py

@@ -21,6 +21,7 @@ class EntityHistorySeries(BaseModel):
 class StateHistoryPoint(BaseModel):
     timestamp: datetime
     state: str
+    attributes: dict[str, object] = {}
 
 
 class StateHistorySeries(BaseModel):
@@ -81,8 +82,22 @@ def normalize_state_history_payload(payload: object) -> list[StateHistorySeries]
             timestamp = _parse_timestamp(
                 raw_entry.get("last_changed") or raw_entry.get("last_updated")
             )
-            if not points or points[-1].state != raw_state:
-                points.append(StateHistoryPoint(timestamp=timestamp, state=raw_state))
+            attributes = raw_entry.get("attributes")
+            if not isinstance(attributes, dict):
+                attributes = {}
+            if (
+                not points
+                or points[-1].state != raw_state
+                or _relevant_state_attributes(points[-1].attributes)
+                != _relevant_state_attributes(attributes)
+            ):
+                points.append(
+                    StateHistoryPoint(
+                        timestamp=timestamp,
+                        state=raw_state,
+                        attributes=_relevant_state_attributes(attributes),
+                    )
+                )
         if entity_id is not None and points:
             points.sort(key=lambda point: point.timestamp)
             normalized.append(StateHistorySeries(entity_id=entity_id, points=points))
@@ -176,3 +191,16 @@ def _optional_string(value: object) -> str | None:
     if value is None or value == "":
         return None
     return str(value)
+
+
+def _relevant_state_attributes(attributes: dict[str, object]) -> dict[str, object]:
+    keys = {
+        "brightness",
+        "color_temp",
+        "color_temp_kelvin",
+        "effect",
+        "hs_color",
+        "rgb_color",
+        "xy_color",
+    }
+    return {key: attributes[key] for key in keys if key in attributes}

app/main.py

@@ -117,7 +117,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
     title="SillyHome Next API",
     description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
-    version="1.7.3",
+    version="1.7.4",
     lifespan=lifespan,
 )
 app.state.settings = load_settings()

app/static/index.html

@@ -288,6 +288,69 @@ const STATUS_TIMEOUT_MS = 2000;
 const DASHBOARD_TIMEOUT_MS = 3000;
 const I18N = {
   de: {
+    ui: {
+      tagline: "Geräte, Lernen, Freigaben und Systemzustand.",
+      menu: "Menü",
+      nav_status: "Startseite / System",
+      nav_learning: "Lernen",
+      nav_discovery: "Discovery & Einrichtung",
+      nav_settings: "Einstellungen",
+      page_ready: "Seite bereit, Status folgt ...",
+      discovery_title: "Discovery & Einrichtung",
+      entity_id: "Entity-ID",
+      actuator_placeholder: "z. B. light.licht_abstellraum",
+      type: "Typ",
+      all_actuators: "Alle steuerbaren Typen",
+      lights: "Lichter",
+      switches: "Schalter / Helper",
+      buttons: "Buttons",
+      helper_buttons: "Helper-Buttons",
+      helper_switches: "Helper-Schalter",
+      covers: "Rollläden / Cover",
+      climate: "Heizungen / Klima",
+      locks: "Schlösser",
+      fans: "Lüftung / Ventilatoren",
+      humidifiers: "Befeuchter / Entfeuchter",
+      media: "TV / Medien",
+      remotes: "Fernbedienungen",
+      scenes: "Szenen",
+      numbers: "Numerische Helper",
+      valves: "Ventile",
+      search_list: "Liste durchsuchen",
+      search_placeholder: "Raum, Gerät oder Entity",
+      device_list: "Geräteliste",
+      device_list_lazy: "Geräteliste bei Bedarf laden",
+      add_device: "Gerät hinzufügen und Beobachtung starten",
+      load_device_list: "Geräteliste laden",
+      ready: "Bereit.",
+      show_suggestions: "Vorschläge anzeigen",
+      load_suggestions: "Vorschläge laden",
+      observed_devices: "Beobachtete Geräte",
+      refresh: "Aktualisieren",
+      details: "Details",
+      back: "Zurück",
+      detail_empty: "Öffne bei einem beobachteten Gerät die Details.",
+      system_cache: "System & Cache",
+      check_status: "Status prüfen",
+      checking: "Prüfung läuft ...",
+      settings: "Einstellungen",
+      settings_hint: "Sprache und Standardwerte für die Bedienoberfläche.",
+      language: "Sprache",
+      no_prediction: "Keine fällige Aktion",
+      open: "offen",
+      no_area: "Ohne Bereich",
+      loading_start: "Startdaten laden ...",
+      loading_devices: "Beobachtete Geräte werden geladen ...",
+      delayed_start: "Startdaten verzögert",
+      unavailable_start: "Startdaten sind gerade nicht verfügbar.",
+      system_loading: "Systemübersicht lädt ...",
+      system_delayed: "Systemübersicht verzögert",
+      context_detected: "Kontext erkannt",
+      active_approved: "aktiv freigegeben",
+      shadow_prediction: "Prüfmodus mit Vorhersage",
+      learning_blocked: "Lernen blockiert",
+      collecting_actions: "sammelt Handlungen",
+    },
     safety_stage: {
       observe: "Nur beobachten",
       suggest: "Vorschläge anzeigen",
@@ -358,6 +421,69 @@ const I18N = {
     },
   },
   en: {
+    ui: {
+      tagline: "Devices, learning, approvals, and system health.",
+      menu: "Menu",
+      nav_status: "Home / System",
+      nav_learning: "Learning",
+      nav_discovery: "Discovery & setup",
+      nav_settings: "Settings",
+      page_ready: "Page ready, status pending ...",
+      discovery_title: "Discovery & setup",
+      entity_id: "Entity ID",
+      actuator_placeholder: "e.g. light.storage_room",
+      type: "Type",
+      all_actuators: "All controllable types",
+      lights: "Lights",
+      switches: "Switches / helpers",
+      buttons: "Buttons",
+      helper_buttons: "Helper buttons",
+      helper_switches: "Helper switches",
+      covers: "Shutters / covers",
+      climate: "Heating / climate",
+      locks: "Locks",
+      fans: "Ventilation / fans",
+      humidifiers: "Humidifiers / dehumidifiers",
+      media: "TV / media",
+      remotes: "Remotes",
+      scenes: "Scenes",
+      numbers: "Numeric helpers",
+      valves: "Valves",
+      search_list: "Search list",
+      search_placeholder: "Room, device, or entity",
+      device_list: "Device list",
+      device_list_lazy: "Load device list when needed",
+      add_device: "Add device and start observing",
+      load_device_list: "Load device list",
+      ready: "Ready.",
+      show_suggestions: "Show suggestions",
+      load_suggestions: "Load suggestions",
+      observed_devices: "Observed devices",
+      refresh: "Refresh",
+      details: "Details",
+      back: "Back",
+      detail_empty: "Open details from an observed device.",
+      system_cache: "System & cache",
+      check_status: "Check status",
+      checking: "Checking ...",
+      settings: "Settings",
+      settings_hint: "Language and UI defaults.",
+      language: "Language",
+      no_prediction: "No due action",
+      open: "open",
+      no_area: "No area",
+      loading_start: "Loading start data ...",
+      loading_devices: "Loading observed devices ...",
+      delayed_start: "Start data delayed",
+      unavailable_start: "Start data is currently unavailable.",
+      system_loading: "Loading system overview ...",
+      system_delayed: "System overview delayed",
+      context_detected: "Context detected",
+      active_approved: "actively approved",
+      shadow_prediction: "Review mode with prediction",
+      learning_blocked: "Learning blocked",
+      collecting_actions: "collecting actions",
+    },
     safety_stage: {
       observe: "Observe only",
       suggest: "Show suggestions",
@@ -468,11 +594,15 @@ function showView(viewId) {
 function setLanguage(language) {
   uiLang = I18N[language] ? language : "de";
   localStorage.setItem("sillyhome.ui.language", uiLang);
+  document.documentElement.lang = uiLang;
+  applyStaticTranslations();
   syncSettingsView();
+  renderActuatorSelect();
   if (cachedActuators) renderConfiguredActuators();
   if (cachedSystemOverview) renderDashboardStatus(cachedSystemOverview);
-  if (currentActuatorId && cachedDetailHtml.has(currentActuatorId)) {
-    document.getElementById("actuator-detail").innerHTML = cachedDetailHtml.get(currentActuatorId);
+  if (currentActuatorId) {
+    cachedDetailHtml.delete(currentActuatorId);
+    void showActuator(currentActuatorId);
   }
 }
 
@@ -482,16 +612,95 @@ function syncSettingsView() {
 }
 
 function translate(group, value, fallback = "") {
-  if (value == null || value === "") return fallback || "offen";
+  if (value == null || value === "") return fallback || ui("open");
   return I18N[uiLang]?.[group]?.[value] || fallback || String(value);
 }
 
+function ui(key) {
+  return I18N[uiLang]?.ui?.[key] || I18N.de.ui[key] || key;
+}
+
+function setText(selector, key) {
+  const element = document.querySelector(selector);
+  if (element) element.textContent = ui(key);
+}
+
+function setPlaceholder(selector, key) {
+  const element = document.querySelector(selector);
+  if (element) element.placeholder = ui(key);
+}
+
+function applyStaticTranslations() {
+  setText("header .brand p", "tagline");
+  setText("label[for='section-jump']", "menu");
+  const navOptions = document.querySelectorAll("#section-jump option");
+  [
+    "nav_status",
+    "nav_learning",
+    "nav_discovery",
+    "nav_settings",
+  ].forEach((key, index) => {
+    if (navOptions[index]) navOptions[index].textContent = ui(key);
+  });
+  setText("#load-budget", "page_ready");
+  setText("#choose h2", "discovery_title");
+  setText("label[for='actuator-input']", "entity_id");
+  setPlaceholder("#actuator-input", "actuator_placeholder");
+  setText("label[for='actuator-domain-filter']", "type");
+  const domainOptions = document.querySelectorAll("#actuator-domain-filter option");
+  [
+    "all_actuators",
+    "lights",
+    "switches",
+    "buttons",
+    "helper_buttons",
+    "helper_switches",
+    "covers",
+    "climate",
+    "locks",
+    "fans",
+    "humidifiers",
+    "media",
+    "remotes",
+    "scenes",
+    "numbers",
+    "valves",
+  ].forEach((key, index) => {
+    if (domainOptions[index]) domainOptions[index].textContent = ui(key);
+  });
+  setText("label[for='actuator-search']", "search_list");
+  setPlaceholder("#actuator-search", "search_placeholder");
+  setText("label[for='actuator-select']", "device_list");
+  const lazyOption = document.querySelector("#actuator-select option[value='']");
+  if (lazyOption) lazyOption.textContent = ui("device_list_lazy");
+  const chooseButtons = document.querySelectorAll("#choose > button");
+  if (chooseButtons[0]) chooseButtons[0].textContent = ui("add_device");
+  if (chooseButtons[1]) chooseButtons[1].textContent = ui("load_device_list");
+  setText("#actuator-config-result", "ready");
+  setText("#choose .manual-context summary", "show_suggestions");
+  const suggestionButton = document.querySelector("#choose .manual-context button");
+  if (suggestionButton) suggestionButton.textContent = ui("load_suggestions");
+  setText("#observed h2", "observed_devices");
+  const refreshButton = document.querySelector("#observed .panel-title button");
+  if (refreshButton) refreshButton.textContent = ui("refresh");
+  setText("#detail h2", "details");
+  const backButton = document.querySelector("#detail .panel-title button");
+  if (backButton) backButton.textContent = ui("back");
+  setText("#actuator-detail", "detail_empty");
+  setText("#status-section h2", "system_cache");
+  const statusButton = document.querySelector("#status-section .panel-title button");
+  if (statusButton) statusButton.textContent = ui("check_status");
+  setText("#settings h2", "settings");
+  setText("#settings .muted", "settings_hint");
+  setText("label[for='language-select']", "language");
+}
+
 function formatDateTime(value) {
-  if (!value) return "noch offen";
+  if (!value) return ui("open");
   const parsed = new Date(value);
   return Number.isNaN(parsed.getTime())
     ? String(value)
-    : parsed.toLocaleString("de-DE");
+    : parsed.toLocaleString(uiLang === "en" ? "en-US" : "de-DE");
 }
 
 function uniqueValues(values) {
@@ -532,7 +741,7 @@ async function apiWithTimeout(path, timeoutMs = STATUS_TIMEOUT_MS) {
 function lifecycleLabel(record) {
   const behaviorStatus = record.behavior_status || record.behavior?.status;
   const lifecycleStatus = record.lifecycle_status || record.lifecycle?.status;
-  if (behaviorStatus === "trained") return "Kontext erkannt";
+  if (behaviorStatus === "trained") return ui("context_detected");
   const labels = {
     trained: translate("lifecycle_status", "trained"),
     pending_history: translate("lifecycle_status", "pending_history"),
@@ -556,10 +765,10 @@ function statusClass(record) {
 function behaviorLabel(record) {
   const mode = record.behavior_mode || record.behavior?.mode;
   const status = record.behavior_status || record.behavior?.status;
-  if (mode === "active") return "aktiv freigegeben";
-  if (status === "trained") return "Prüfmodus mit Vorhersage";
-  if (status === "blocked") return "Lernen blockiert";
-  return "sammelt Handlungen";
+  if (mode === "active") return ui("active_approved");
+  if (status === "trained") return ui("shadow_prediction");
+  if (status === "blocked") return ui("learning_blocked");
+  return ui("collecting_actions");
 }
 
 function predictionLabel(record) {
@@ -567,11 +776,11 @@ function predictionLabel(record) {
   const confidence = record.prediction_confidence ?? record.behavior?.prediction?.confidence;
   return target
     ? `${target} (${Math.round(confidence * 100)} %)`
-    : "Keine fällige Aktion";
+    : ui("no_prediction");
 }
 
 function entityLabel(entity) {
-  const area = entity.area_name || "Ohne Bereich";
+  const area = entity.area_name || ui("no_area");
   const name = entity.friendly_name || entity.entity_id;
   return `${area} - ${name} (${entity.entity_id})`;
 }
@@ -655,9 +864,9 @@ async function loadOverview() {
 async function doLoadOverview() {
   const startedAt = performance.now();
   const budget = document.getElementById("load-budget");
-  if (budget) budget.textContent = "Startdaten laden ...";
+  if (budget) budget.textContent = ui("loading_start");
   if (!cachedActuators) {
-    document.getElementById("configured-actuators").innerHTML = "<p class='muted'>Beobachtete Geräte werden geladen ...</p>";
+    document.getElementById("configured-actuators").innerHTML = `<p class='muted'>${escapeHtml(ui("loading_devices"))}</p>`;
   }
   try {
     const dashboard = await api("v1/actuators/dashboard/start");
@@ -675,12 +884,12 @@ async function doLoadOverview() {
     }
   } catch (error) {
     document.getElementById("configured-actuators").innerHTML = `<p class="bad">${escapeHtml(error.message)}</p>`;
-    if (budget) budget.textContent = "Startdaten verzögert";
+    if (budget) budget.textContent = ui("delayed_start");
     try {
       await loadSummaryData();
       renderConfiguredActuators();
     } catch (_) {
-      document.getElementById("configured-actuators").innerHTML = "<div class='empty-state'>Startdaten sind gerade nicht verfügbar.</div>";
+      document.getElementById("configured-actuators").innerHTML = `<div class='empty-state'>${escapeHtml(ui("unavailable_start"))}</div>`;
     }
   }
   scheduleDashboardExtras();
@@ -697,7 +906,7 @@ async function loadSystemOverview() {
 async function doLoadSystemOverview() {
   const startedAt = performance.now();
   const budget = document.getElementById("load-budget");
-  if (budget) budget.textContent = "Systemübersicht lädt ...";
+  if (budget) budget.textContent = ui("system_loading");
   try {
     const dashboard = await api("v1/actuators/dashboard/system");
     dashboard._load_elapsed_ms = Math.round(performance.now() - startedAt);
@@ -713,7 +922,7 @@ async function doLoadSystemOverview() {
     scheduleDashboardExtras();
   } catch (error) {
     document.getElementById("status").innerHTML = `<p class="warn">Systemübersicht verzögert: ${escapeHtml(error.message)}</p>`;
-    if (budget) budget.textContent = "Systemübersicht verzögert";
+    if (budget) budget.textContent = ui("system_delayed");
   }
 }
 
@@ -1950,9 +2159,11 @@ async function removeActuator(actuatorId) {
 }
 
 async function startDashboard() {
-  document.getElementById("status").innerHTML = "<p class='muted'>Status lädt nach ...</p>";
-  document.getElementById("configured-actuators").innerHTML = "<div class='empty-state'>Öffne „Lernen“, um Geräte zu laden.</div>";
-  document.getElementById("actuator-detail").innerHTML = "<div class='empty-state'>Wähle später ein Gerät aus der Übersicht.</div>";
+  document.documentElement.lang = uiLang;
+  applyStaticTranslations();
+  document.getElementById("status").innerHTML = `<p class='muted'>${escapeHtml(uiLang === "en" ? "Status loading ..." : "Status lädt nach ...")}</p>`;
+  document.getElementById("configured-actuators").innerHTML = `<div class='empty-state'>${escapeHtml(uiLang === "en" ? "Open Learning to load devices." : "Öffne „Lernen“, um Geräte zu laden.")}</div>`;
+  document.getElementById("actuator-detail").innerHTML = `<div class='empty-state'>${escapeHtml(uiLang === "en" ? "Select a device from the overview later." : "Wähle später ein Gerät aus der Übersicht.")}</div>`;
   syncSettingsView();
   const initialView = localStorage.getItem("sillyhome.ui.view") === "detail"
     ? "observed"

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-next"
-version = "1.7.3"
+version = "1.7.4"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [

tests/actuators/test_lifecycle.py

@@ -87,7 +87,7 @@ def _service(
 
 
 def test_reconciliation_auto_assigns_and_trains_numeric_model(tmp_path: Path) -> None:
-    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    start = datetime.now(timezone.utc) - timedelta(days=1)
     entities = [
         HaEntitySummary(
             entity_id="light.abstellkammer",
@@ -352,6 +352,100 @@ def test_fan_prefers_humidity_over_power_sensor(tmp_path: Path) -> None:
     assert record.assignment.selected_numeric_entity_id == "sensor.bad_luftfeuchtigkeit"
 
 
+def test_lidl_light_uses_room_presence_not_brand_overlap(tmp_path: Path) -> None:
+    entities = [
+        HaEntitySummary(
+            entity_id="light.lidl_kuche",
+            domain="light",
+            friendly_name="Lidl Küche",
+        ),
+        HaEntitySummary(
+            entity_id="light.lidl_wohnzimmer",
+            domain="light",
+            friendly_name="Lidl Wohnzimmer",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.pir_kuche_motion_detection",
+            domain="binary_sensor",
+            device_class="motion",
+            friendly_name="Bewegungsmelder",
+            device_name="PIR_Küche",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.pir_wohnzimmer_sensor_state_any",
+            domain="binary_sensor",
+            device_class="motion",
+            friendly_name="Bewegungsmelder",
+            device_name="PIR_Wohnzimmer",
+        ),
+    ]
+    service = _service(tmp_path, entities, {})
+
+    record = service.configure_actuator("light.lidl_kuche")
+
+    assert record.assignment.selected_context_entity_ids == [
+        "binary_sensor.pir_kuche_motion_detection"
+    ]
+
+
+def test_mailbox_reset_button_uses_cabinet_door_context(tmp_path: Path) -> None:
+    entities = [
+        HaEntitySummary(
+            entity_id="button.smart_mailbox_als_geleert_markieren",
+            domain="button",
+            friendly_name="Smart Mailbox Als geleert markieren",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.schrank_strasse_open",
+            domain="binary_sensor",
+            device_class="door",
+            friendly_name="Schrank Straße",
+        ),
+    ]
+    service = _service(tmp_path, entities, {})
+
+    record = service.configure_actuator("button.smart_mailbox_als_geleert_markieren")
+
+    assert record.assignment.selected_context_entity_ids == [
+        "binary_sensor.schrank_strasse_open"
+    ]
+    assert record.assignment.review_required is False
+
+
+def test_fan_auto_selects_humidity_and_occupancy_context(tmp_path: Path) -> None:
+    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    entities = [
+        HaEntitySummary(
+            entity_id="humidifier.gastewc_luftung",
+            domain="humidifier",
+            friendly_name="GästeWC Lüftung",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.pir_gastewc_humidity",
+            domain="sensor",
+            device_class="humidity",
+            state_class="measurement",
+            unit_of_measurement="%",
+            friendly_name="Gäste WC Luftfeuchtigkeit",
+        ),
+        HaEntitySummary(
+            entity_id="input_boolean.gaste_wc_occupied",
+            domain="input_boolean",
+            friendly_name="gaste_wc_occupied",
+        ),
+    ]
+    service = _service(
+        tmp_path,
+        entities,
+        {"sensor.pir_gastewc_humidity": _points(8, start, 55.0)},
+    )
+
+    record = service.configure_actuator("humidifier.gastewc_luftung")
+
+    assert record.assignment.selected_numeric_entity_id == "sensor.pir_gastewc_humidity"
+    assert "input_boolean.gaste_wc_occupied" in record.assignment.selected_context_entity_ids
+
+
 def test_manual_assignment_persists_and_wins_over_automatic_mapping(tmp_path: Path) -> None:
     start = datetime(2026, 6, 1, tzinfo=timezone.utc)
     entities = [

tests/behavior/test_engine.py

@@ -646,6 +646,81 @@ def test_prediction_ignores_stale_causal_context_state() -> None:
     ) is None
 
 
+def test_prediction_respects_learned_context_delay() -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    patterns = [
+        BehaviorPattern(
+            target_state="on",
+            minute_of_day=60,
+            weekday=0,
+            context_states={"input_boolean.gaste_wc_occupied": "on"},
+            trigger_entity_id="input_boolean.gaste_wc_occupied",
+            trigger_from_state="off",
+            trigger_to_state="on",
+            trigger_delay_seconds=180,
+            source="automation",
+            weight=1.0,
+            observed_at=now - timedelta(days=days_ago),
+        )
+        for days_ago in (3, 2, 1)
+    ]
+
+    early = predict_behavior(
+        patterns,
+        current_context={"input_boolean.gaste_wc_occupied": "on"},
+        current_context_changed_at={
+            "input_boolean.gaste_wc_occupied": now - timedelta(seconds=30)
+        },
+        now=now,
+        min_support=3,
+        window_minutes=30,
+        causal_window_seconds=240,
+    )
+    due = predict_behavior(
+        patterns,
+        current_context={"input_boolean.gaste_wc_occupied": "on"},
+        current_context_changed_at={
+            "input_boolean.gaste_wc_occupied": now - timedelta(seconds=185)
+        },
+        now=now,
+        min_support=3,
+        window_minutes=30,
+        causal_window_seconds=240,
+    )
+
+    assert early is None
+    assert due is not None
+    assert due.target_state == "on"
+
+
+def test_light_prediction_carries_brightness_attributes() -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    patterns = [
+        BehaviorPattern(
+            target_state="on",
+            target_attributes={"brightness": brightness},
+            minute_of_day=now.astimezone().hour * 60 + now.astimezone().minute,
+            weekday=now.astimezone().weekday(),
+            context_states={"binary_sensor.pir_kuche_motion_detection": "on"},
+            source="automation",
+            weight=1.0,
+            observed_at=now - timedelta(days=days_ago),
+        )
+        for days_ago, brightness in zip((3, 2, 1), (80, 90, 100), strict=True)
+    ]
+
+    prediction = predict_behavior(
+        patterns,
+        current_context={"binary_sensor.pir_kuche_motion_detection": "on"},
+        now=now,
+        min_support=3,
+        window_minutes=30,
+    )
+
+    assert prediction is not None
+    assert prediction.target_attributes["brightness"] == 90
+
+
 def test_state_change_uses_websocket_context_state_for_immediate_action(
     tmp_path: Path,
 ) -> None:

tests/ha/test_history.py

@@ -120,6 +120,28 @@ def test_normalize_state_history_keeps_categorical_changes() -> None:
     assert [point.state for point in result[0].points] == ["off", "on"]
 
 
+def test_normalize_state_history_keeps_light_attribute_changes() -> None:
+    result = normalize_state_history_payload(
+        [
+            [
+                {
+                    "entity_id": "light.office",
+                    "state": "on",
+                    "attributes": {"brightness": 80, "friendly_name": "Office"},
+                    "last_changed": "2026-06-01T08:00:00+00:00",
+                },
+                {
+                    "state": "on",
+                    "attributes": {"brightness": 120, "friendly_name": "Office"},
+                    "last_changed": "2026-06-01T08:05:00+00:00",
+                },
+            ]
+        ]
+    )
+
+    assert [point.attributes["brightness"] for point in result[0].points] == [80, 120]
+
+
 def test_normalize_logbook_preserves_action_origin() -> None:
     result = normalize_logbook_payload(
         [