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Author SHA1 Message Date
33cce32098 Release SillyHome Next 1.7.4
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2026-07-26 21:59:21 +02:00
08e41b0198 Improve learning discovery and dashboard i18n
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2026-07-26 21:57:57 +02:00
1b9db62294 Add simulation apply workflow
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2026-06-18 20:10:53 +02:00
5ca0c53f6a Reduce websocket reconnect load
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2026-06-18 19:17:50 +02:00
14 changed files with 869 additions and 55 deletions

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@@ -1,5 +1,23 @@
# Changelog # 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 ## 1.7.0 - 2026-06-18
- Produktiv-Ausbau fuer Schaltvertrauen: persistente Entscheidungs-Timeline, - Produktiv-Ausbau fuer Schaltvertrauen: persistente Entscheidungs-Timeline,
Event-Latenzmessungen und Dry-run pro Aktor. Event-Latenzmessungen und Dry-run pro Aktor.

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@@ -1,5 +1,5 @@
name: SillyHome Next name: SillyHome Next
version: "1.7.1" version: "1.7.4"
slug: sillyhome_next slug: sillyhome_next
description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren
url: http://192.168.6.31:3000/pino/sillyhome-next url: http://192.168.6.31:3000/pino/sillyhome-next

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@@ -46,6 +46,8 @@ _STOPWORDS = frozenset(
"entity", "entity",
"humidity", "humidity",
"illuminance", "illuminance",
"led",
"lidl",
"light", "light",
"licht", "licht",
"lichtschalter", "lichtschalter",
@@ -138,6 +140,33 @@ _AUTO_CONTEXT_CLASSES = frozenset({
"presence", "presence",
"window", "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: class ActuatorReconciliationService:
@@ -864,6 +893,18 @@ def _has_context_relationship(actuator: HaEntitySummary, entity: HaEntitySummary
return True return True
if _metadata_tokens(actuator).intersection(_metadata_tokens(entity)): if _metadata_tokens(actuator).intersection(_metadata_tokens(entity)):
return True 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) entity_tokens = _metadata_tokens(entity, include_stopwords=True)
return bool( return bool(
entity_tokens.intersection(_OUTDOOR_TOKENS) entity_tokens.intersection(_OUTDOOR_TOKENS)
@@ -878,6 +919,18 @@ def _eligible_for_auto_context(
device_class = candidate.device_class or "" device_class = candidate.device_class or ""
if device_class in _AUTO_CONTEXT_CLASSES: if device_class in _AUTO_CONTEXT_CLASSES:
return True 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 ( if (
actuator.device_name actuator.device_name
and candidate.device_name and candidate.device_name
@@ -899,6 +952,7 @@ def _score_candidate(
score = 0.0 score = 0.0
actuator_tokens = _metadata_tokens(actuator) actuator_tokens = _metadata_tokens(actuator)
entity_tokens = _metadata_tokens(entity) entity_tokens = _metadata_tokens(entity)
full_entity_tokens = _metadata_tokens(entity, include_stopwords=True)
overlap = sorted(actuator_tokens.intersection(entity_tokens)) overlap = sorted(actuator_tokens.intersection(entity_tokens))
if overlap: if overlap:
score += min(0.4, 0.1 * len(overlap)) score += min(0.4, 0.1 * len(overlap))
@@ -924,6 +978,31 @@ def _score_candidate(
if entity.device_class in preferred_device_classes: if entity.device_class in preferred_device_classes:
score += 0.2 score += 0.2
evidence.append(f"Passende device_class: {entity.device_class}") 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": if not context and actuator.domain == "light" and entity.device_class == "illuminance":
score += 0.2 score += 0.2
evidence.append("Beleuchtungsstärke wird für Lichtaktoren bevorzugt.") 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: for value in raw_values:
if value is None: if value is None:
continue continue
for token in _TOKEN_PATTERN.findall(value.lower().replace("_", " ")): for token in _TOKEN_PATTERN.findall(_normalize_text(value)):
if len(token) < 3 or (not include_stopwords and token in _STOPWORDS): if (len(token) < 3 and token != "wc") or (not include_stopwords and token in _STOPWORDS):
continue continue
tokens.add(token) 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: def _history_signature(sensor_id: str, points: list[NumericHistoryPoint]) -> str:

View File

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

View File

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

View File

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

View File

@@ -21,6 +21,7 @@ class EntityHistorySeries(BaseModel):
class StateHistoryPoint(BaseModel): class StateHistoryPoint(BaseModel):
timestamp: datetime timestamp: datetime
state: str state: str
attributes: dict[str, object] = {}
class StateHistorySeries(BaseModel): class StateHistorySeries(BaseModel):
@@ -81,8 +82,22 @@ def normalize_state_history_payload(payload: object) -> list[StateHistorySeries]
timestamp = _parse_timestamp( timestamp = _parse_timestamp(
raw_entry.get("last_changed") or raw_entry.get("last_updated") raw_entry.get("last_changed") or raw_entry.get("last_updated")
) )
if not points or points[-1].state != raw_state: attributes = raw_entry.get("attributes")
points.append(StateHistoryPoint(timestamp=timestamp, state=raw_state)) 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: if entity_id is not None and points:
points.sort(key=lambda point: point.timestamp) points.sort(key=lambda point: point.timestamp)
normalized.append(StateHistorySeries(entity_id=entity_id, points=points)) 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 == "": if value is None or value == "":
return None return None
return str(value) 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}

View File

@@ -117,7 +117,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
app = FastAPI( app = FastAPI(
title="SillyHome Next API", title="SillyHome Next API",
description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.", description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
version="1.7.1", version="1.7.4",
lifespan=lifespan, lifespan=lifespan,
) )
app.state.settings = load_settings() app.state.settings = load_settings()
@@ -255,6 +255,9 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
ws_url = ha_url.replace("http://", "ws://").replace("https://", "wss://") + "/api/websocket" ws_url = ha_url.replace("http://", "ws://").replace("https://", "wss://") + "/api/websocket"
auth_token = cast(str, settings.ha_token) auth_token = cast(str, settings.ha_token)
ws_status = getattr(app.state, "ws_status", None) ws_status = getattr(app.state, "ws_status", None)
reconnect_delay = 1.0
relevant_entity_ids: set[str] = set()
relevant_loaded_at = 0.0
while True: while True:
if ws_status is not None: if ws_status is not None:
ws_status.status = "connecting" ws_status.status = "connecting"
@@ -283,6 +286,9 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
logger.info("WebSocket-Verbindung zu Home Assistant hergestellt") logger.info("WebSocket-Verbindung zu Home Assistant hergestellt")
state_cache = await asyncio.to_thread(_load_ha_state_cache, ha_reader) state_cache = await asyncio.to_thread(_load_ha_state_cache, ha_reader)
relevant_entity_ids = await asyncio.to_thread(_relevant_entity_ids, store)
relevant_loaded_at = asyncio.get_running_loop().time()
reconnect_delay = 1.0
if ws_status is not None: if ws_status is not None:
ws_status.status = "connected" ws_status.status = "connected"
ws_status.error = None ws_status.error = None
@@ -309,6 +315,12 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
entity_id = event_data.get("entity_id") entity_id = event_data.get("entity_id")
if not entity_id: if not entity_id:
continue continue
loop_time = asyncio.get_running_loop().time()
if loop_time - relevant_loaded_at >= 10:
relevant_entity_ids = await asyncio.to_thread(_relevant_entity_ids, store)
relevant_loaded_at = loop_time
if entity_id not in relevant_entity_ids:
continue
new_state = event_data.get("new_state") new_state = event_data.get("new_state")
_update_ha_state_cache(state_cache, entity_id, new_state) _update_ha_state_cache(state_cache, entity_id, new_state)
# Prüfe, ob Entity ein Aktor oder relevanter Kontext ist # Prüfe, ob Entity ein Aktor oder relevanter Kontext ist
@@ -328,17 +340,24 @@ async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
websockets.exceptions.InvalidStatus, websockets.exceptions.InvalidStatus,
OSError, OSError,
) as exc: ) as exc:
logger.warning("WebSocket-Verbindung unterbrochen: %s. Wiederholung in 1s...", exc) delay = reconnect_delay
logger.warning(
"WebSocket-Verbindung unterbrochen: %s. Wiederholung in %.0fs...",
exc,
delay,
)
if ws_status is not None: if ws_status is not None:
ws_status.status = "reconnecting" ws_status.status = "reconnecting"
ws_status.error = str(exc) ws_status.error = str(exc)
await asyncio.sleep(1) await asyncio.sleep(delay)
reconnect_delay = min(reconnect_delay * 2, 60.0)
except Exception as exc: except Exception as exc:
logger.exception("Unerwarteter Fehler im Event-Listener: %s", exc) logger.exception("Unerwarteter Fehler im Event-Listener: %s", exc)
if ws_status is not None: if ws_status is not None:
ws_status.status = "error" ws_status.status = "error"
ws_status.error = str(exc) ws_status.error = str(exc)
await asyncio.sleep(1) await asyncio.sleep(reconnect_delay)
reconnect_delay = min(reconnect_delay * 2, 60.0)
# Fallback: periodische Vorhersage falls Event-Stream ausfällt # Fallback: periodische Vorhersage falls Event-Stream ausfällt
@@ -353,7 +372,7 @@ async def _fallback_prediction(app: FastAPI) -> None:
await asyncio.sleep( await asyncio.sleep(
app.state.settings.prediction_interval_seconds app.state.settings.prediction_interval_seconds
if websocket_connected if websocket_connected
else min(5, app.state.settings.prediction_interval_seconds) else max(30, app.state.settings.prediction_interval_seconds)
) )
# Nur ausführen, wenn WebSocket nicht verbunden ist # Nur ausführen, wenn WebSocket nicht verbunden ist
ws_status = getattr(app.state, "ws_status", None) ws_status = getattr(app.state, "ws_status", None)
@@ -389,15 +408,14 @@ def _update_ha_state_cache(
) )
def _is_relevant_state_change(store: ActuatorStore, entity_id: str) -> bool: def _relevant_entity_ids(store: ActuatorStore) -> set[str]:
result: set[str] = set()
for record in store.list(): for record in store.list():
if record.actuator_entity_id == entity_id: result.add(record.actuator_entity_id)
return True if record.assignment.selected_numeric_entity_id:
if record.assignment.selected_numeric_entity_id == entity_id: result.add(record.assignment.selected_numeric_entity_id)
return True result.update(record.assignment.selected_context_entity_ids)
if entity_id in record.assignment.selected_context_entity_ids: return result
return True
return False
def _ha_entity_from_event( def _ha_entity_from_event(

View File

@@ -281,12 +281,76 @@ let discoveryLoadPromise = null;
let overviewLoadPromise = null; let overviewLoadPromise = null;
let systemLoadPromise = null; let systemLoadPromise = null;
let currentSensorWeightGroups = []; let currentSensorWeightGroups = [];
let latestSimulationResults = new Map();
let visibleActuatorLimit = 24; let visibleActuatorLimit = 24;
const ACTUATOR_RESULT_LIMIT = 50; const ACTUATOR_RESULT_LIMIT = 50;
const STATUS_TIMEOUT_MS = 2000; const STATUS_TIMEOUT_MS = 2000;
const DASHBOARD_TIMEOUT_MS = 3000; const DASHBOARD_TIMEOUT_MS = 3000;
const I18N = { const I18N = {
de: { 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: { safety_stage: {
observe: "Nur beobachten", observe: "Nur beobachten",
suggest: "Vorschläge anzeigen", suggest: "Vorschläge anzeigen",
@@ -357,6 +421,69 @@ const I18N = {
}, },
}, },
en: { 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: { safety_stage: {
observe: "Observe only", observe: "Observe only",
suggest: "Show suggestions", suggest: "Show suggestions",
@@ -467,11 +594,15 @@ function showView(viewId) {
function setLanguage(language) { function setLanguage(language) {
uiLang = I18N[language] ? language : "de"; uiLang = I18N[language] ? language : "de";
localStorage.setItem("sillyhome.ui.language", uiLang); localStorage.setItem("sillyhome.ui.language", uiLang);
document.documentElement.lang = uiLang;
applyStaticTranslations();
syncSettingsView(); syncSettingsView();
renderActuatorSelect();
if (cachedActuators) renderConfiguredActuators(); if (cachedActuators) renderConfiguredActuators();
if (cachedSystemOverview) renderDashboardStatus(cachedSystemOverview); if (cachedSystemOverview) renderDashboardStatus(cachedSystemOverview);
if (currentActuatorId && cachedDetailHtml.has(currentActuatorId)) { if (currentActuatorId) {
document.getElementById("actuator-detail").innerHTML = cachedDetailHtml.get(currentActuatorId); cachedDetailHtml.delete(currentActuatorId);
void showActuator(currentActuatorId);
} }
} }
@@ -481,16 +612,95 @@ function syncSettingsView() {
} }
function translate(group, value, fallback = "") { 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); 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) { function formatDateTime(value) {
if (!value) return "noch offen"; if (!value) return ui("open");
const parsed = new Date(value); const parsed = new Date(value);
return Number.isNaN(parsed.getTime()) return Number.isNaN(parsed.getTime())
? String(value) ? String(value)
: parsed.toLocaleString("de-DE"); : parsed.toLocaleString(uiLang === "en" ? "en-US" : "de-DE");
} }
function uniqueValues(values) { function uniqueValues(values) {
@@ -531,7 +741,7 @@ async function apiWithTimeout(path, timeoutMs = STATUS_TIMEOUT_MS) {
function lifecycleLabel(record) { function lifecycleLabel(record) {
const behaviorStatus = record.behavior_status || record.behavior?.status; const behaviorStatus = record.behavior_status || record.behavior?.status;
const lifecycleStatus = record.lifecycle_status || record.lifecycle?.status; const lifecycleStatus = record.lifecycle_status || record.lifecycle?.status;
if (behaviorStatus === "trained") return "Kontext erkannt"; if (behaviorStatus === "trained") return ui("context_detected");
const labels = { const labels = {
trained: translate("lifecycle_status", "trained"), trained: translate("lifecycle_status", "trained"),
pending_history: translate("lifecycle_status", "pending_history"), pending_history: translate("lifecycle_status", "pending_history"),
@@ -555,10 +765,10 @@ function statusClass(record) {
function behaviorLabel(record) { function behaviorLabel(record) {
const mode = record.behavior_mode || record.behavior?.mode; const mode = record.behavior_mode || record.behavior?.mode;
const status = record.behavior_status || record.behavior?.status; const status = record.behavior_status || record.behavior?.status;
if (mode === "active") return "aktiv freigegeben"; if (mode === "active") return ui("active_approved");
if (status === "trained") return "Prüfmodus mit Vorhersage"; if (status === "trained") return ui("shadow_prediction");
if (status === "blocked") return "Lernen blockiert"; if (status === "blocked") return ui("learning_blocked");
return "sammelt Handlungen"; return ui("collecting_actions");
} }
function predictionLabel(record) { function predictionLabel(record) {
@@ -566,11 +776,11 @@ function predictionLabel(record) {
const confidence = record.prediction_confidence ?? record.behavior?.prediction?.confidence; const confidence = record.prediction_confidence ?? record.behavior?.prediction?.confidence;
return target return target
? `${target} (${Math.round(confidence * 100)} %)` ? `${target} (${Math.round(confidence * 100)} %)`
: "Keine fällige Aktion"; : ui("no_prediction");
} }
function entityLabel(entity) { 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; const name = entity.friendly_name || entity.entity_id;
return `${area} - ${name} (${entity.entity_id})`; return `${area} - ${name} (${entity.entity_id})`;
} }
@@ -654,9 +864,9 @@ async function loadOverview() {
async function doLoadOverview() { async function doLoadOverview() {
const startedAt = performance.now(); const startedAt = performance.now();
const budget = document.getElementById("load-budget"); const budget = document.getElementById("load-budget");
if (budget) budget.textContent = "Startdaten laden ..."; if (budget) budget.textContent = ui("loading_start");
if (!cachedActuators) { if (!cachedActuators) {
document.getElementById("configured-actuators").innerHTML = "<p class='muted'>Beobachtete Geräte werden geladen ...</p>"; document.getElementById("configured-actuators").innerHTML = `<p class='muted'>${escapeHtml(ui("loading_devices"))}</p>`;
} }
try { try {
const dashboard = await api("v1/actuators/dashboard/start"); const dashboard = await api("v1/actuators/dashboard/start");
@@ -674,12 +884,12 @@ async function doLoadOverview() {
} }
} catch (error) { } catch (error) {
document.getElementById("configured-actuators").innerHTML = `<p class="bad">${escapeHtml(error.message)}</p>`; document.getElementById("configured-actuators").innerHTML = `<p class="bad">${escapeHtml(error.message)}</p>`;
if (budget) budget.textContent = "Startdaten verzögert"; if (budget) budget.textContent = ui("delayed_start");
try { try {
await loadSummaryData(); await loadSummaryData();
renderConfiguredActuators(); renderConfiguredActuators();
} catch (_) { } catch (_) {
document.getElementById("configured-actuators").innerHTML = "<div class='empty-state'>Startdaten sind gerade nicht verfügbar.</div>"; document.getElementById("configured-actuators").innerHTML = `<div class='empty-state'>${escapeHtml(ui("unavailable_start"))}</div>`;
} }
} }
scheduleDashboardExtras(); scheduleDashboardExtras();
@@ -696,7 +906,7 @@ async function loadSystemOverview() {
async function doLoadSystemOverview() { async function doLoadSystemOverview() {
const startedAt = performance.now(); const startedAt = performance.now();
const budget = document.getElementById("load-budget"); const budget = document.getElementById("load-budget");
if (budget) budget.textContent = "Systemübersicht lädt ..."; if (budget) budget.textContent = ui("system_loading");
try { try {
const dashboard = await api("v1/actuators/dashboard/system"); const dashboard = await api("v1/actuators/dashboard/system");
dashboard._load_elapsed_ms = Math.round(performance.now() - startedAt); dashboard._load_elapsed_ms = Math.round(performance.now() - startedAt);
@@ -712,7 +922,7 @@ async function doLoadSystemOverview() {
scheduleDashboardExtras(); scheduleDashboardExtras();
} catch (error) { } catch (error) {
document.getElementById("status").innerHTML = `<p class="warn">Systemübersicht verzögert: ${escapeHtml(error.message)}</p>`; 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");
} }
} }
@@ -1255,7 +1465,7 @@ async function showActuator(actuatorId, evaluationMessage = "") {
const simulationControls = weightedCandidates.length ? ` const simulationControls = weightedCandidates.length ? `
<details class="manual-context" open> <details class="manual-context" open>
<summary>Aktor-Simulation</summary> <summary>Aktor-Simulation</summary>
<p class="muted">Teste Sensorzustände und Gewichtungen, ohne Home Assistant zu schalten.</p> <p class="muted">Teste Sensorzustände und Gewichtungen, ohne Home Assistant zu schalten. Danach kannst du die beste Gewichtung übernehmen oder direkt in den Dry-run wechseln.</p>
<div class="card-list"> <div class="card-list">
${weightedCandidates.map(candidate => { ${weightedCandidates.map(candidate => {
const effective = Math.round((candidate.effective_weight ?? 1) * 100); const effective = Math.round((candidate.effective_weight ?? 1) * 100);
@@ -1677,6 +1887,7 @@ async function simulateActuator(actuatorId) {
max_results: 6, max_results: 6,
}), }),
}); });
latestSimulationResults.set(actuatorId, results);
box.innerHTML = results.length ? results.map((result, index) => { box.innerHTML = results.length ? results.map((result, index) => {
const prediction = result.prediction; const prediction = result.prediction;
const factors = result.decision_factors || []; const factors = result.decision_factors || [];
@@ -1691,6 +1902,10 @@ async function simulateActuator(actuatorId) {
<p class="muted">Gewichtung: ${Object.entries(result.sensor_weights || {}).map(([entity, weight]) => `${escapeHtml(entity)}=${Math.round(weight * 100)} %`).join(", ") || "Standard"}</p> <p class="muted">Gewichtung: ${Object.entries(result.sensor_weights || {}).map(([entity, weight]) => `${escapeHtml(entity)}=${Math.round(weight * 100)} %`).join(", ") || "Standard"}</p>
${result.blockers?.length ? `<p class="warn">${result.blockers.map(escapeHtml).join(" ")}</p>` : "<p class='ok'>Würde nach Sicherheitsprüfung schalten.</p>"} ${result.blockers?.length ? `<p class="warn">${result.blockers.map(escapeHtml).join(" ")}</p>` : "<p class='ok'>Würde nach Sicherheitsprüfung schalten.</p>"}
${factors.length ? `<ul>${factors.slice(0, 4).map(factor => `<li>${escapeHtml(factor.label)}: ${Math.round((factor.contribution || 0) * 100)} % Beitrag</li>`).join("")}</ul>` : ""} ${factors.length ? `<ul>${factors.slice(0, 4).map(factor => `<li>${escapeHtml(factor.label)}: ${Math.round((factor.contribution || 0) * 100)} % Beitrag</li>`).join("")}</ul>` : ""}
<div class="actions">
<button class="secondary compact" onclick="applySimulationWeights('${escapeHtml(actuatorId)}', '${escapeHtml(result.scenario_id)}', false)">Gewichtung übernehmen</button>
<button class="compact" onclick="applySimulationWeights('${escapeHtml(actuatorId)}', '${escapeHtml(result.scenario_id)}', true)">Übernehmen + Dry-run starten</button>
</div>
</div> </div>
`; `;
}).join("") : "<p class='muted'>Keine Simulationsergebnisse.</p>"; }).join("") : "<p class='muted'>Keine Simulationsergebnisse.</p>";
@@ -1699,6 +1914,41 @@ async function simulateActuator(actuatorId) {
} }
} }
async function applySimulationWeights(actuatorId, scenarioId, startDryRun) {
const result = (latestSimulationResults.get(actuatorId) || [])
.find(item => item.scenario_id === scenarioId);
if (!result) {
alert("Simulationsergebnis ist nicht mehr verfügbar. Bitte neu simulieren.");
return;
}
try {
await api(`v1/actuators/${encodeURIComponent(actuatorId)}/weights`, {
method: "POST",
body: JSON.stringify({
sensor_weights: result.sensor_weights || {},
sensor_weight_groups: currentSensorWeightGroups,
note: `Aus Simulation ${scenarioId} übernommen`,
}),
});
if (startDryRun) {
await api(`v1/actuators/${encodeURIComponent(actuatorId)}/dry-run`, {
method: "POST",
body: JSON.stringify({enabled: true}),
});
}
invalidateDashboardCache();
await loadConfiguredActuators();
await showActuator(
actuatorId,
startDryRun
? "Simulation übernommen und Dry-run gestartet."
: "Simulation übernommen.",
);
} catch (error) {
alert(error.message);
}
}
async function saveManualAssignment(actuatorId) { async function saveManualAssignment(actuatorId) {
const numericEntityId = document.getElementById("manual-numeric-select").value || null; const numericEntityId = document.getElementById("manual-numeric-select").value || null;
const selectedContextIds = Array.from( const selectedContextIds = Array.from(
@@ -1909,9 +2159,11 @@ async function removeActuator(actuatorId) {
} }
async function startDashboard() { async function startDashboard() {
document.getElementById("status").innerHTML = "<p class='muted'>Status lädt nach ...</p>"; document.documentElement.lang = uiLang;
document.getElementById("configured-actuators").innerHTML = "<div class='empty-state'>Öffne „Lernen“, um Geräte zu laden.</div>"; applyStaticTranslations();
document.getElementById("actuator-detail").innerHTML = "<div class='empty-state'>Wähle später ein Gerät aus der Übersicht.</div>"; 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(); syncSettingsView();
const initialView = localStorage.getItem("sillyhome.ui.view") === "detail" const initialView = localStorage.getItem("sillyhome.ui.view") === "detail"
? "observed" ? "observed"

View File

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project] [project]
name = "sillyhome-next" name = "sillyhome-next"
version = "1.7.1" version = "1.7.4"
description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant" description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
requires-python = ">=3.11" requires-python = ">=3.11"
dependencies = [ dependencies = [

View File

@@ -87,7 +87,7 @@ def _service(
def test_reconciliation_auto_assigns_and_trains_numeric_model(tmp_path: Path) -> None: 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 = [ entities = [
HaEntitySummary( HaEntitySummary(
entity_id="light.abstellkammer", 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" 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: def test_manual_assignment_persists_and_wins_over_automatic_mapping(tmp_path: Path) -> None:
start = datetime(2026, 6, 1, tzinfo=timezone.utc) start = datetime(2026, 6, 1, tzinfo=timezone.utc)
entities = [ entities = [

View File

@@ -646,6 +646,81 @@ def test_prediction_ignores_stale_causal_context_state() -> None:
) is 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( def test_state_change_uses_websocket_context_state_for_immediate_action(
tmp_path: Path, tmp_path: Path,
) -> None: ) -> None:

View File

@@ -120,6 +120,28 @@ def test_normalize_state_history_keeps_categorical_changes() -> None:
assert [point.state for point in result[0].points] == ["off", "on"] 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: def test_normalize_logbook_preserves_action_origin() -> None:
result = normalize_logbook_payload( result = normalize_logbook_payload(
[ [

View File

@@ -126,6 +126,43 @@ def test_ha_event_listener_processes_state_change(tmp_path: Path) -> None:
assert mock_app.state.ws_status.error is None assert mock_app.state.ws_status.error is None
def test_ha_event_listener_skips_unrelated_state_change(tmp_path: Path) -> None:
async def run_test() -> None:
fake_ws = _FakeWebSocket(
[
'{"type":"auth_required"}',
'{"type":"auth_ok"}',
(
'{"type":"event","event":{"event_type":"state_changed",'
'"data":{"entity_id":"sensor.unused","new_state":{"state":"on"}}}}'
),
asyncio.CancelledError(),
]
)
with patch("websockets.connect", return_value=fake_ws):
try:
await _ha_event_listener(mock_app, mock_client)
except asyncio.CancelledError:
pass
mock_app = MagicMock()
mock_app.state.settings = MagicMock()
mock_app.state.settings.ha_url = "http://homeassistant:8123"
mock_app.state.settings.ha_token = "test-token"
mock_app.state.ws_status = MagicMock()
mock_engine = _RecordingBehaviorEngine(tmp_path)
mock_app.state.behavior_engine = mock_engine
mock_app.state.ha_reader = _FakeHaReader()
mock_store = ActuatorStore(tmp_path / "store")
mock_store.configure("light.test")
mock_app.state.actuator_store = mock_store
mock_client = MagicMock()
anyio.run(run_test)
assert mock_engine.state_changes == []
def test_lifespan_skips_event_listener_without_ha_config() -> None: def test_lifespan_skips_event_listener_without_ha_config() -> None:
app = FastAPI() app = FastAPI()
app.state.settings = MagicMock() app.state.settings = MagicMock()