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Add Future parity feature blocks

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Otto
2026-06-18 16:20:30 +02:00
parent e4b860571a
commit 07e3e96c30
7 changed files with 324 additions and 14 deletions
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62
app/core/models.py
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@@ -21,6 +21,19 @@ class SafetyStage(StrEnum):
BLOCKED = "blocked"
class ProposalStatus(StrEnum):
DRAFT = "draft"
APPROVED = "approved"
REJECTED = "rejected"
class JobStatus(StrEnum):
QUEUED = "queued"
RUNNING = "running"
SUCCEEDED = "succeeded"
FAILED = "failed"
class EntityState(BaseModel):
entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
domain: str
@@ -89,6 +102,51 @@ class SceneProfile(BaseModel):
confidence: float = Field(default=0.0, ge=0.0, le=1.0)
class AutomationProposal(BaseModel):
proposal_id: str
name: str
trigger_entity_id: str
trigger_state: str | None = None
actuator_entity_id: str
target_state: str
status: ProposalStatus = ProposalStatus.DRAFT
revision: int = 1
created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
decided_at: datetime | None = None
class ModelRecord(BaseModel):
model_id: str
actuator_entity_id: str
pattern_count: int
confidence: float = Field(default=0.0, ge=0.0, le=1.0)
trained_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
class JobQueueItem(BaseModel):
job_id: str
kind: str
status: JobStatus = JobStatus.QUEUED
message: str = ""
created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
finished_at: datetime | None = None
class SensorWeightOverride(BaseModel):
actuator_entity_id: str
sensor_weights: dict[str, float] = Field(default_factory=dict)
note: str | None = Field(default=None, max_length=500)
updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
class HistoryAnalysis(BaseModel):
entity_id: str
samples: int
last_state: str | None = None
changed_at: datetime | None = None
recommendation: str
class Decision(BaseModel):
actuator_entity_id: str
target_state: str | None = None
@@ -121,6 +179,10 @@ class LearningState(BaseModel):
profiles: dict[str, LearningProfile] = Field(default_factory=dict)
rooms: dict[str, RoomProfile] = Field(default_factory=dict)
scenes: dict[str, SceneProfile] = Field(default_factory=dict)
automation_proposals: dict[str, AutomationProposal] = Field(default_factory=dict)
models: dict[str, ModelRecord] = Field(default_factory=dict)
jobs: dict[str, JobQueueItem] = Field(default_factory=dict)
weight_overrides: dict[str, SensorWeightOverride] = Field(default_factory=dict)
class ControlState(BaseModel):

227
app/main.py
View File

@@ -4,6 +4,7 @@ import asyncio
import os
from collections.abc import AsyncIterator
from contextlib import asynccontextmanager, suppress
from datetime import datetime, timezone
from fastapi import FastAPI
from fastapi.responses import HTMLResponse
@@ -15,16 +16,23 @@ from app.core.ha_client import FutureHaClient, HaClientConfig, service_for_state
from app.core.handoff import HandoffMatrix
from app.core.models import (
AuditEvent,
AutomationProposal,
BackupBundle,
BehaviorPatternV2,
ControlProfile,
ControlState,
EntityState,
HistoryAnalysis,
HandoffMode,
JobQueueItem,
JobStatus,
LearningProfile,
LearningState,
ModelRecord,
ProposalStatus,
RuntimeState,
SafetyStage,
SensorWeightOverride,
StateEvent,
)
from app.core.stores import FutureStores
@@ -86,6 +94,27 @@ class ActuatorSummary(BaseModel):
feedback_negative: int
class AutomationProposalRequest(BaseModel):
name: str = Field(max_length=120)
trigger_entity_id: str
trigger_state: str | None = None
actuator_entity_id: str
target_state: str
class HistoryAnalysisRequest(BaseModel):
entity_ids: list[str] = Field(default_factory=list)
class TrainModelRequest(BaseModel):
actuator_entity_id: str | None = None
class WeightOverrideRequest(BaseModel):
sensor_weights: dict[str, float] = Field(default_factory=dict)
note: str | None = Field(default=None, max_length=500)
@asynccontextmanager
async def lifespan(app: FastAPI) -> AsyncIterator[None]:
global ha_client
@@ -106,7 +135,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
app = FastAPI(
title="SillyHome Future API",
description="SillyHome v2 event-core side project.",
version="2.0.0-alpha.10",
version="2.0.0-alpha.11",
lifespan=lifespan,
)
@@ -159,6 +188,9 @@ def dashboard_data() -> dict[str, object]:
"global_enabled": control.global_enabled,
"learning_profiles": len(learning.profiles),
"control_profiles": len(control.profiles),
"automation_proposals": len(learning.automation_proposals),
"models": len(learning.models),
"jobs": len(learning.jobs),
"rooms": list(learning.rooms.values()),
"scenes": list(learning.scenes.values()),
"audit": latest_audit,
@@ -185,17 +217,135 @@ def feature_parity() -> dict[str, object]:
"Simulation",
"Audit",
"Health",
"Automation-Proposals",
"YAML-Export",
"History-Analyse",
"lokales Modelltraining",
"Sensor-Gewichte",
"Job-Queue",
],
"next_to_expand": [
"automations-proposals",
"history-analysis",
"model-training",
"weight-overrides",
"job-queue",
],
"next_to_expand": ["echte Langzeit-History aus HA", "fortgeschrittene Modellbewertung"],
}
@app.post("/v2/automations/proposals", response_model=AutomationProposal)
def create_automation_proposal(payload: AutomationProposalRequest) -> AutomationProposal:
state = stores.learning()
proposal_id = f"proposal-{len(state.automation_proposals) + 1}"
proposal = AutomationProposal(
proposal_id=proposal_id,
name=payload.name,
trigger_entity_id=payload.trigger_entity_id,
trigger_state=payload.trigger_state,
actuator_entity_id=payload.actuator_entity_id,
target_state=payload.target_state,
)
state.automation_proposals[proposal_id] = proposal
stores.save_learning(state)
_append_job("automation_proposal", f"Automation-Vorschlag {proposal_id} angelegt.")
return proposal
@app.get("/v2/automations/proposals", response_model=list[AutomationProposal])
def list_automation_proposals() -> list[AutomationProposal]:
return list(stores.learning().automation_proposals.values())
@app.post("/v2/automations/proposals/{proposal_id}/approve", response_model=AutomationProposal)
def approve_automation_proposal(proposal_id: str) -> AutomationProposal:
return _decide_automation_proposal(proposal_id, ProposalStatus.APPROVED)
@app.post("/v2/automations/proposals/{proposal_id}/reject", response_model=AutomationProposal)
def reject_automation_proposal(proposal_id: str) -> AutomationProposal:
return _decide_automation_proposal(proposal_id, ProposalStatus.REJECTED)
@app.get("/v2/automations/proposals/{proposal_id}/yaml")
def automation_proposal_yaml(proposal_id: str) -> str:
proposal = stores.learning().automation_proposals[proposal_id]
return "\n".join(
[
f"alias: {proposal.name}",
"trigger:",
" - platform: state",
f" entity_id: {proposal.trigger_entity_id}",
f" to: {proposal.trigger_state or ''}",
"action:",
" - service: homeassistant.turn_on",
" target:",
f" entity_id: {proposal.actuator_entity_id}",
" data:",
f" target_state: {proposal.target_state}",
"mode: single",
"",
]
)
@app.post("/v2/history/analyze", response_model=list[HistoryAnalysis])
def analyze_history(request: HistoryAnalysisRequest) -> list[HistoryAnalysis]:
runtime = stores.runtime()
ids = request.entity_ids or list(runtime.entities)[:50]
result: list[HistoryAnalysis] = []
for entity_id in ids:
entity = runtime.entities.get(entity_id)
matching_audit = [item for item in runtime.audit if item.entity_id == entity_id]
result.append(
HistoryAnalysis(
entity_id=entity_id,
samples=max(1, len(matching_audit)),
last_state=entity.state if entity else None,
changed_at=entity.changed_at if entity else None,
recommendation=(
"Als Trigger geeignet."
if entity is not None and entity.domain in {"binary_sensor", "sensor"}
else "Als Aktor oder Kontext pruefen."
),
)
)
_append_job("history_analysis", f"History-Analyse fuer {len(result)} Entities erstellt.")
return result
@app.post("/v2/models/train", response_model=list[ModelRecord])
def train_models(request: TrainModelRequest) -> list[ModelRecord]:
state = stores.learning()
actuator_ids = [request.actuator_entity_id] if request.actuator_entity_id else list(state.profiles)
trained: list[ModelRecord] = []
for actuator_id in actuator_ids:
if actuator_id is None:
continue
profile = state.profiles.get(actuator_id)
pattern_count = len(profile.patterns) if profile else 0
confidence = (
sum(pattern.confidence for pattern in profile.patterns) / pattern_count
if profile and pattern_count
else 0.0
)
model = ModelRecord(
model_id=f"model-{actuator_id}-{len(state.models) + 1}",
actuator_entity_id=actuator_id,
pattern_count=pattern_count,
confidence=confidence,
)
state.models[model.model_id] = model
trained.append(model)
stores.save_learning(state)
_append_job("model_training", f"{len(trained)} lokale Modelle trainiert.")
return trained
@app.get("/v2/models", response_model=list[ModelRecord])
def list_models() -> list[ModelRecord]:
return list(stores.learning().models.values())
@app.get("/v2/jobs", response_model=list[JobQueueItem])
def list_jobs() -> list[JobQueueItem]:
return list(stores.learning().jobs.values())[-100:]
@app.post("/v2/events/state", response_model=list[AuditEvent])
def ingest_state_event(event: StateEvent) -> list[AuditEvent]:
return event_core.process_state_event(event, execute=_execute_ha_decision)
@@ -421,6 +571,31 @@ def record_feedback(actuator_entity_id: str, feedback: FeedbackRequest) -> Learn
return updated
@app.post("/v2/control/{actuator_entity_id}/weights", response_model=SensorWeightOverride)
def set_weight_override(
actuator_entity_id: str,
payload: WeightOverrideRequest,
) -> SensorWeightOverride:
state = stores.learning()
override = SensorWeightOverride(
actuator_entity_id=actuator_entity_id,
sensor_weights=payload.sensor_weights,
note=payload.note,
)
state.weight_overrides[actuator_entity_id] = override
stores.save_learning(state)
_append_audit("weights", actuator_entity_id, "Sensor-Gewichte aktualisiert.")
return override
@app.get("/v2/control/{actuator_entity_id}/weights", response_model=SensorWeightOverride)
def get_weight_override(actuator_entity_id: str) -> SensorWeightOverride:
return stores.learning().weight_overrides.get(
actuator_entity_id,
SensorWeightOverride(actuator_entity_id=actuator_entity_id),
)
@app.post("/v2/planning/refresh", response_model=LearningState)
def refresh_planning() -> LearningState:
state = stores.learning()
@@ -676,6 +851,39 @@ def _append_audit(kind: str, entity_id: str | None, message: str) -> None:
stores.save_runtime(runtime)
def _append_job(kind: str, message: str, status: JobStatus = JobStatus.SUCCEEDED) -> JobQueueItem:
state = stores.learning()
job = JobQueueItem(
job_id=f"job-{len(state.jobs) + 1}",
kind=kind,
status=status,
message=message,
finished_at=datetime.now(timezone.utc) if status is JobStatus.SUCCEEDED else None,
)
state.jobs[job.job_id] = job
stores.save_learning(state)
return job
def _decide_automation_proposal(
proposal_id: str,
status: ProposalStatus,
) -> AutomationProposal:
state = stores.learning()
proposal = state.automation_proposals[proposal_id]
updated = proposal.model_copy(
update={
"status": status,
"revision": proposal.revision + 1,
"decided_at": datetime.now(timezone.utc),
}
)
state.automation_proposals[proposal_id] = updated
stores.save_learning(state)
_append_job("automation_proposal", f"Automation-Vorschlag {proposal_id}: {status}.")
return updated
def _execute_ha_decision(decision: object) -> bool:
if ha_client is None or not hasattr(decision, "actuator_entity_id"):
return False
@@ -858,6 +1066,9 @@ def _dashboard_html() -> str:
['Not-Aus', data.global_enabled ? 'frei' : 'aktiv'],
['Lernen', data.learning_profiles],
['Steuerung', data.control_profiles],
['Vorschlaege', data.automation_proposals],
['Modelle', data.models],
['Jobs', data.jobs],
['Räume', data.rooms.length],
['Szenen', data.scenes.length],
];