harden delivery pipeline and production runtime

app/ha/client.py

@@ -31,10 +31,13 @@ class HaClient:
             "Content-Type": "application/json",
         })
 
+    def close(self) -> None:
+        self._session.close()
+
     def list_entities(self) -> list[dict[str, object]]:
         try:
             response = self._session.get(
-                f"{self._settings.url}/api/states",
+                f"{self._settings.url.rstrip('/')}/api/states",
                 timeout=self._settings.timeout_seconds,
             )
         except requests.Timeout as exc:
@@ -71,4 +74,4 @@ class HaClient:
                 "Antwort von Home Assistant hat unerwartetes Format."
             )
 
-        return payload
+        return payload

app/ha/reader.py

@@ -15,9 +15,10 @@ class HaReader:
         entities = self._client.list_entities()
         summaries: list[HaEntitySummary] = []
         for item in entities:
-            entity_id = item.get("entity_id", "")
-            if "." not in entity_id:
+            raw_entity_id = item.get("entity_id")
+            if not isinstance(raw_entity_id, str) or "." not in raw_entity_id:
                 continue
+            entity_id = raw_entity_id
             domain = entity_id.split(".", 1)[0]
             raw_attributes = item.get("attributes") or {}
             attributes: dict[str, Any] = raw_attributes if isinstance(raw_attributes, dict) else {}

app/main.py

@@ -1,4 +1,6 @@
 from contextlib import asynccontextmanager
+from collections.abc import AsyncIterator
+from typing import cast
 
 from fastapi import FastAPI
 
@@ -7,23 +9,30 @@ from app.config import load_settings
 from app.core.exception_handlers import register_exception_handlers
 from app.ha.client import HaClient, HaClientSettings
 from app.ha.reader import HaReader
+from app.ml.registry.model_registry import ModelRegistry
 from backend.routes.ml import init_ml_routes
 
 
 @asynccontextmanager
-async def lifespan(app: FastAPI):  # type: ignore[no-untyped-def]
+async def lifespan(app: FastAPI) -> AsyncIterator[None]:
     settings = app.state.settings
+    client: HaClient | None = None
+    app.state.registry = ModelRegistry(settings.model_store)
     if hasattr(app.state, "ha_reader"):
         del app.state.ha_reader
     if settings.ha_configured:
         client = HaClient(
             settings=HaClientSettings(
-                url=settings.ha_url,
-                token=settings.ha_token,
+                url=cast(str, settings.ha_url),
+                token=cast(str, settings.ha_token),
             )
         )
         app.state.ha_reader = HaReader(client=client)
-    yield
+    try:
+        yield
+    finally:
+        if client is not None:
+            client.close()
 
 
 app = FastAPI(

app/ml/__init__.py

@@ -1,5 +1,5 @@
 
 """Machine-Learning-Grundbausteine für SillyHome Next."""
-__all__ = ["FeatureStore", "FeatureVector"]
+__all__ = ["FeatureStore", "FeatureVector", "TrainedArtifact", "TrainingPipeline"]
 from app.ml.feature_store import FeatureStore, FeatureVector
 from app.ml.training import TrainedArtifact, TrainingPipeline

app/ml/feature_store.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 
 from collections import defaultdict
-from dataclasses import dataclass, field
-from typing import Iterable
+from collections.abc import Iterable
+from dataclasses import dataclass
 
 
 @dataclass(frozen=True)
@@ -28,4 +28,4 @@ class FeatureStore:
         return series[-1] if series else None
 
     def all(self) -> list[FeatureVector]:
-        return [vector for vectors in self._vectors.values() for vector in vectors]
+        return [vector for vectors in self._vectors.values() for vector in vectors]

app/ml/registry/model_registry.py

@@ -23,8 +23,8 @@ class ModelRegistry:
 
     def register(self, artifact: TrainedArtifact) -> TrainedArtifact:
         self._validate_artifact_id(artifact.artifact_id)
-        self._artifacts[artifact.artifact_id] = artifact
         self._persist(artifact)
+        self._artifacts[artifact.artifact_id] = artifact
         return artifact
 
     def load_artifact(self, artifact_id: str) -> TrainedArtifact:

app/ml/training.py

@@ -2,9 +2,8 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass
-from typing import Sequence
 
-from app.ml.feature_store import FeatureVector, FeatureStore
+from app.ml.feature_store import FeatureStore
 
 logger = logging.getLogger(__name__)
 
@@ -34,4 +33,4 @@ class TrainingPipeline:
     def export(self, artifact_id: str) -> TrainedArtifact:
         if artifact_id not in self._artifacts:
             raise KeyError(f"Artifact '{artifact_id}' nicht gefunden.")
-        return self._artifacts[artifact_id]
+        return self._artifacts[artifact_id]