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Otto aaf319ff14
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harden delivery pipeline and production runtime
2026-06-11 21:14:07 +02:00

44 lines
1.3 KiB
Python

from collections.abc import AsyncIterator
from contextlib import asynccontextmanager
from fastapi import FastAPI
from starlette.datastructures import State
from backend.routes.ml import init_ml_routes
from app.ml.registry.model_registry import ModelRegistry
from app.ml.training import TrainingPipeline
from app.ml.feature_store import FeatureStore, FeatureVector
@asynccontextmanager
async def lifespan(application: FastAPI) -> AsyncIterator[None]:
application.state.registry = ModelRegistry(application.state.model_store)
_seed_default_model(application.state)
yield
def create_app() -> FastAPI:
application = FastAPI(title="SillyHome Next ML", lifespan=lifespan)
init_ml_routes(application)
return application
def _seed_default_model(state: State) -> None:
registry = getattr(state, "registry", None)
if registry is None:
registry = ModelRegistry(".model_store")
state.registry = registry
if list(registry.list_models()):
return
store = FeatureStore()
store.add(FeatureVector(sensor_id="sensor.front_door", values={"contact": 1.0}))
store.add(FeatureVector(sensor_id="sensor.living_room", values={"temperature": 21.0}))
pipeline = TrainingPipeline(store)
artifact = pipeline.run("default")
registry.register(artifact)
app = create_app()