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| import os | |
| import re | |
| import pickle | |
| import pandas as pd | |
| from tqdm import tqdm | |
| from sentence_transformers import SentenceTransformer, util | |
| from db.db_utils import get_connection, initialize_db, store_mapping_to_db | |
| from food_nonfood import classify_as_food_nonfood | |
| from utils import generate_embedding, clean_word, cosine_similarity, calculate_confidence_and_similar_words_str, generate_embedded_dictionary | |
| # model_name = 'sentence-transformers/all-MiniLM-L6-v2' | |
| model_name = 'sentence-transformers/all-mpnet-base-v2' | |
| filename = model_name.replace('/', '-') | |
| pickle_file_path = f'./embeddings/fast/{filename}.pkl' | |
| class SimilarityFast: | |
| def __init__(self, db_cursor): | |
| self.db_cursor = db_cursor | |
| self.model = SentenceTransformer(model_name) | |
| self.db_cursor.execute("SELECT description FROM dictionary") | |
| dictionary = self.db_cursor.fetchall() | |
| dictionary = [item[0] for item in dictionary] | |
| self.dictionary_embeddings = self.load_dictionary_embeddings(dictionary) | |
| def preprocess_dictionary_word(self, text): | |
| text = text.strip().lower() | |
| text = text.replace(", raw", "").replace(" raw", "") | |
| text = text.replace(", nfs", "").replace(" nfs", "") | |
| if ',' in text: | |
| parts = [part.strip() for part in text.split(',')] | |
| text = ' '.join(reversed(parts)) | |
| text = text.strip() # strip again in case there were multiple commas | |
| return text | |
| def load_dictionary_embeddings(self, dictionary): | |
| if os.path.exists(pickle_file_path): | |
| with open(pickle_file_path, 'rb') as f: | |
| return pickle.load(f) | |
| else: | |
| url = "https://s3.amazonaws.com/www.brianweinreich.com/tmp/sentence-transformers-all-mpnet-base-v2.pkl" | |
| response = requests.get(url) | |
| with open(pickle_file_path, 'wb') as f: | |
| f.write(response.content) | |
| with open(pickle_file_path, 'rb') as f: | |
| return pickle.load(f) | |
| # dont generate the embeddings on demand anymore | |
| # dictionary_embeddings = generate_embedded_dictionary(dictionary, self.model, self.preprocess_dictionary_word) | |
| # with open(pickle_file_path, 'wb') as f: | |
| # pickle.dump(dictionary_embeddings, f) | |
| # return dictionary_embeddings | |
| def calculate_similarity_score(self, input_word_clean): | |
| input_embedding = generate_embedding(self.model, input_word_clean) | |
| similarities = [] | |
| contains_cooked = 'cooked' in input_word_clean.lower() | |
| for key, val in self.dictionary_embeddings.items(): | |
| similarity_score = cosine_similarity(input_embedding, val['v']) | |
| if contains_cooked and 'cooked' in key.lower(): | |
| similarity_score *= 1.05 | |
| similarities.append((key, val['d'], similarity_score)) | |
| most_similar_word, dictionary_word, highest_score = max(similarities, key=lambda x: x[2]) | |
| confidence_score, similar_words_str = calculate_confidence_and_similar_words_str(similarities, highest_score) | |
| return most_similar_word, dictionary_word, highest_score, confidence_score, similar_words_str | |
| def find_most_similar_word(self, input_word): | |
| if not isinstance(input_word, str) or not input_word: | |
| return None | |
| input_word_clean = clean_word(input_word) | |
| most_similar_word, dictionary_word, highest_score, confidence_score, similar_words_str = self.calculate_similarity_score(input_word_clean) | |
| mapping = { | |
| 'input_word': input_word, | |
| 'cleaned_word': input_word_clean, | |
| 'matching_word': most_similar_word, | |
| 'dictionary_word': dictionary_word, | |
| 'similarity_score': highest_score, | |
| 'confidence_score': confidence_score, | |
| 'similar_words': similar_words_str, | |
| } | |
| return mapping | |