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b904b161eb
* increased float rounding precision for crf parser * limited fractions to a max denominator of 32 to prevent weirdly specific values * add test cases for 1/8 and 1/32 * add rounding to avoid more digits than necessary Co-authored-by: Hayden <64056131+hay-kot@users.noreply.github.com>
80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
import shutil
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from dataclasses import dataclass
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from fractions import Fraction
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import pytest
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from mealie.services.parser_services import RegisteredParser, get_parser
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from mealie.services.parser_services.crfpp.processor import CRFIngredient, convert_list_to_crf_model
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@dataclass
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class TestIngredient:
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input: str
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quantity: float
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unit: str
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food: str
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comments: str
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def crf_exists() -> bool:
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return shutil.which("crf_test") is not None
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# TODO - add more robust test cases
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test_ingredients = [
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TestIngredient("½ cup all-purpose flour", 0.5, "cup", "all-purpose flour", ""),
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TestIngredient("1 ½ teaspoons ground black pepper", 1.5, "teaspoon", "black pepper", "ground"),
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TestIngredient("⅔ cup unsweetened flaked coconut", 0.667, "cup", "coconut", "unsweetened flaked"),
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TestIngredient("⅓ cup panko bread crumbs", 0.333, "cup", "panko bread crumbs", ""),
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# Small Fraction Tests - PR #1369
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# Reported error is was for 1/8 - new lowest expected threshold is 1/32
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TestIngredient("1/8 cup all-purpose flour", 0.125, "cup", "all-purpose flour", ""),
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TestIngredient("1/32 cup all-purpose flour", 0.03125, "cup", "all-purpose flour", ""),
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]
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@pytest.mark.skipif(not crf_exists(), reason="CRF++ not installed")
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def test_nlp_parser():
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models: list[CRFIngredient] = convert_list_to_crf_model([x.input for x in test_ingredients])
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# Itterate over mdoels and test_ingreidnets to gether
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for model, test_ingredient in zip(models, test_ingredients):
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assert float(sum(Fraction(s) for s in model.qty.split())) == test_ingredient.quantity
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assert model.comment == test_ingredient.comments
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assert model.name == test_ingredient.food
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assert model.unit == test_ingredient.unit
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def test_brute_parser():
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# input: (quantity, unit, food, comments)
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expectations = {
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# Dutch
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"1 theelepel koffie": (1, "theelepel", "koffie", ""),
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"3 theelepels koffie": (3, "theelepels", "koffie", ""),
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"1 eetlepel tarwe": (1, "eetlepel", "tarwe", ""),
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"20 eetlepels bloem": (20, "eetlepels", "bloem", ""),
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"1 mespunt kaneel": (1, "mespunt", "kaneel", ""),
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"1 snuf(je) zout": (1, "snuf(je)", "zout", ""),
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"2 tbsp minced cilantro, leaves and stems": (2, "tbsp", "minced cilantro", "leaves and stems"),
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"1 large yellow onion, coarsely chopped": (1, "large", "yellow onion", "coarsely chopped"),
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"1 1/2 tsp garam masala": (1.5, "tsp", "garam masala", ""),
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"2 cups mango chunks, (2 large mangoes) (fresh or frozen)": (
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2,
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"cups",
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"mango chunks, (2 large mangoes)",
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"fresh or frozen",
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),
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}
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parser = get_parser(RegisteredParser.brute)
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for key, val in expectations.items():
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parsed = parser.parse_one(key)
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assert parsed.ingredient.quantity == val[0]
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assert parsed.ingredient.unit.name == val[1]
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assert parsed.ingredient.food.name == val[2]
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assert parsed.ingredient.note in {val[3], None}
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