mitai-jinkendo/backend/tests/test_food_knowledge.py
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fix: Zuordnungen zuverlässig speichern und Mengen-Varianten zusammenfassen
Mapping bleibt erhalten, auch wenn der Nährwert-Rebuild länger dauert. Offene Liste führt 1 ml/2 ml Olivenöl als ein Lebensmittel. Dazu JSON-Sicherung, eigener Katalogeintrag und Gramm-pro-Einheit.

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-09-12 15:49:14 +02:00

43 lines
1.1 KiB
Python

from data_layer.food_knowledge import (
BUNDLE_FORMAT,
parse_food_knowledge_bundle,
portable_mapping,
)
def test_parse_rejects_unknown_format():
try:
parse_food_knowledge_bundle({"format": "other", "version": 1})
except ValueError as e:
assert "Mitai-Zuordnungsdatei" in str(e)
else:
raise AssertionError("expected ValueError")
def test_parse_accepts_bundle():
data = parse_food_knowledge_bundle({
"format": BUNDLE_FORMAT,
"version": 1,
"mappings": [],
"recipes": [],
})
assert data["version"] == 1
def test_portable_mapping_drops_ids():
row = portable_mapping({
"id": 99,
"food_id": "uuid-here",
"source_system": "fddb",
"source_name_raw": "Haferflocken, Großblatt",
"source_name_normalized": "haferflocken großblatt",
"bls_code": "C131000",
"food_name_de": "Hafer roh",
"catalog_kind": "official_bls",
"external_key": None,
})
assert "id" not in row
assert "food_id" not in row
assert row["bls_code"] == "C131000"
assert row["source_name_raw"] == "Haferflocken, Großblatt"