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| import re | |
| import spacy | |
| import logging | |
| # Load the spaCy model | |
| nlp = spacy.load("en_core_web_trf") | |
| def analyze_text(text): | |
| # Replace different delimiters with a uniform delimiter (comma) | |
| normalized_text = re.sub(r'[\/,]', ',', text) | |
| # an ampersand with spaces on both sides is a delimiter | |
| normalized_text = re.sub(r'\s*&\s*', ',', normalized_text) | |
| # the word 'and' with spaces on both sides is a delimiter | |
| normalized_text = re.sub(r'\s+and\s+', ',', normalized_text) | |
| doc = nlp(normalized_text) | |
| # Print tokens with their attributes | |
| for token in doc: | |
| logging.info(f"Text: {token.text}, POS: {token.pos_}, Dep: {token.dep_}, Head: {token.head.text}") | |
| items = [] | |
| current_item = [] | |
| for token in doc: | |
| # If the token is punctuation, finalize the current item | |
| if token.pos_ == 'PUNCT' and token.text == ',': | |
| if current_item: | |
| items.append(" ".join(current_item)) | |
| current_item = [] | |
| else: | |
| # If token is part of a compound noun or an adjective, add to the current item | |
| if token.dep_ in ('compound', 'amod'): | |
| current_item.append(token.text) | |
| elif token.dep_ in ('ROOT', 'appos'): | |
| if current_item: | |
| current_item.append(token.text) | |
| else: | |
| current_item = [token.text] | |
| if token.head.dep_ == 'ROOT': | |
| items.append(" ".join(current_item)) | |
| current_item = [] | |
| else: | |
| current_item.append(token.text) | |
| # Add the last item if it exists | |
| if current_item: | |
| items.append(" ".join(current_item)) | |
| # Determine if the text is a single noun phrase or multiple items | |
| is_single_noun_phrase = len(items) == 1 | |
| delimiter = determine_delimiter(text) | |
| items = [item.strip() for item in items] | |
| # remove empty strings | |
| items = [item for item in items if item] | |
| return is_single_noun_phrase, delimiter, items | |
| def determine_delimiter(text): | |
| number_of_slashes = text.count('/') | |
| number_of_commas = text.count(',') | |
| number_of_ampersands = text.count(' & ') | |
| number_of_ands = text.count(' and ') | |
| number_of_spaces = text.count(' ') | |
| if number_of_slashes > 0 and number_of_slashes >= number_of_commas: | |
| # prefer slash over comma, since its rarer | |
| return '/' | |
| elif number_of_commas > 0: | |
| return ',' | |
| elif number_of_ampersands > 0: | |
| return '&' | |
| elif number_of_ands > 0: | |
| return 'and' | |
| else: | |
| return ' ' | |
| def has_delimiters(text): | |
| return determine_delimiter(text) != ' ' | |
| def extract_items(text): | |
| is_single_noun_phrase, delimiter, _ = analyze_text(text) | |
| if is_single_noun_phrase: | |
| return [text] | |
| else: | |
| items = text.split(delimiter) | |
| # remove empty strings | |
| items = [item.strip() for item in items if item] | |
| # if the word starts with "& " or "and ", remove it | |
| items = [re.sub(r'^& ', '', item) for item in items] | |
| items = [re.sub(r'^and ', '', item) for item in items] | |
| # trim any leading or trailing spaces | |
| items = [item.strip() for item in items] | |
| return items | |