File size: 3,195 Bytes
f75c5b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | """recipe_nlp/parser.py — spaCy NER + dependency parsing."""
from __future__ import annotations
import re
from dataclasses import dataclass, field
from typing import List
from utils.config import config, NLPConfig
from utils.logger import logger
UNIT_VOCAB = {
"cup","cups","tablespoon","tablespoons","tbsp","tbs","teaspoon","teaspoons","tsp",
"fluid ounce","fl oz","liter","liters","litre","litres","l","milliliter","milliliters","ml",
"pint","pints","quart","quarts","gallon","gallons",
"gram","grams","g","kilogram","kilograms","kg","ounce","ounces","oz","pound","pounds","lb","lbs",
"piece","pieces","slice","slices","clove","cloves","head","heads","bunch","bunches",
"handful","handfuls","can","cans","jar","jars","package","packages","pinch","dash","sprinkle",
}
@dataclass
class ParsedToken:
text: str; lemma: str; pos: str; dep: str
is_food: bool = False; is_quantity: bool = False
is_unit: bool = False; is_method: bool = False
head_text: str = ""
@dataclass
class RawIngredientMention:
food_token: str; quantity_str: str = ""; unit_str: str = ""
method_str: str = ""; sentence: str = ""
class RecipeParser:
def __init__(self, cfg: NLPConfig = None):
self.cfg = cfg or config.nlp
self._nlp = None
def _load_nlp(self):
if self._nlp is None:
import spacy
try:
self._nlp = spacy.load(self.cfg.spacy_model)
except OSError:
logger.info("Downloading spaCy model en_core_web_sm …")
from spacy.cli import download
download(self.cfg.spacy_model)
self._nlp = spacy.load(self.cfg.spacy_model)
return self._nlp
def _is_fraction(self, text: str) -> bool:
return bool(re.match(r"^\d+/\d+$", text))
def extract_raw_mentions(self, text: str) -> List[RawIngredientMention]:
nlp = self._load_nlp()
doc = nlp(text.lower())
methods_lower = {m.lower() for m in self.cfg.cooking_methods}
mentions = []
for chunk in doc.noun_chunks:
head = chunk.root
if head.pos_ not in ("NOUN", "PROPN") or head.text in UNIT_VOCAB:
continue
sent_text = next((s.text for s in doc.sents if chunk.start >= s.start and chunk.end <= s.end), "")
quantity_str = unit_str = method_str = ""
for child in head.children:
if child.dep_ in ("nummod", "quantmod") or child.like_num:
quantity_str = child.text
elif child.text in UNIT_VOCAB or child.lemma_ in UNIT_VOCAB:
unit_str = child.text
if not quantity_str:
for token in chunk:
if token.like_num or self._is_fraction(token.text):
quantity_str = token.text; break
for token in doc:
if abs(token.i - head.i) <= 10 and (token.lemma_ in methods_lower or token.text in methods_lower):
method_str = token.text; break
mentions.append(RawIngredientMention(head.text, quantity_str, unit_str, method_str, sent_text))
return mentions
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