mitai-jinkendo/backend/tests/test_food_mapping.py
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feat: BLS-Stammdaten, FDDB-Mapping und Item-Tagebuch (#106)
Katalog, lernendes Mapping ohne KI, optionale Items und Import-Policy.
Playwright-Smoke und Issue-Audit um Ernährung/Zuordnen/API ergänzt.

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-09-12 14:35:57 +02:00

19 lines
797 B
Python

from data_layer.food_mapping import normalize_food_name, parse_quantity_g
from data_layer.nutrition_items import macros_differ
def test_normalize_strips_leading_quantity():
assert normalize_food_name("50 g Hähnchen") == "hähnchen"
assert normalize_food_name(" Vollmilch 3,5% ") == "vollmilch 3.5%"
def test_parse_quantity_g():
assert parse_quantity_g("150 g") == 150.0
assert parse_quantity_g("150") == 150.0
assert parse_quantity_g("1 Stück") is None
def test_macros_differ_rounds():
assert not macros_differ({"kcal": 1.04, "protein_g": 0, "fat_g": 0, "carbs_g": 0}, {"kcal": 1.0, "protein_g": 0, "fat_g": 0, "carbs_g": 0})
assert macros_differ({"kcal": 10, "protein_g": 0, "fat_g": 0, "carbs_g": 0}, {"kcal": 11, "protein_g": 0, "fat_g": 0, "carbs_g": 0})