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| import time | |
| from tqdm import tqdm | |
| import pandas as pd | |
| from pluralizer import Pluralizer | |
| from similarity_fast import SimilarityFast | |
| from food_nonfood import classify_as_food_nonfood, pessimistic_food_nonfood_score | |
| from utils import clean_word | |
| from db.db_utils import store_mapping_to_db, get_mapping_from_db, get_dictionary_data_from_db | |
| from ask_gpt import query_gpt | |
| from multi_food_item_detector import extract_items | |
| similarity_threshold = 0.75 | |
| class Algo: | |
| def __init__(self, db_conn, enable_csv=False): | |
| self.db_conn = db_conn | |
| self.enable_csv = enable_csv | |
| self.db_cursor = db_conn.cursor() | |
| self.similarity_fast = SimilarityFast(self.db_cursor) | |
| # self.similarity_slow = SimilaritySlow(self.db_cursor, self.db_conn) | |
| self.pluralizer = Pluralizer() | |
| def save_to_csv(self, results): | |
| if not self.enable_csv: | |
| return | |
| output_file_path = f'./results/{int(time.time())}.csv' | |
| df_results = pd.DataFrame(results, columns=[ | |
| 'date', | |
| "input_word", | |
| "dictionary_word", | |
| "is_food", | |
| 'wweia_category', | |
| 'dry_matter_content', | |
| "water_content", | |
| 'carbon_credits', | |
| 'weight', | |
| 'weight_metric_tonnes', | |
| 'donor', | |
| "similarity_score", | |
| "food_nonfood_score", | |
| 'distance', | |
| 'ef', | |
| 'mt_lb_mile', | |
| 'baseline_emissions', | |
| 'leakage_emissions', | |
| 'project_emissions', | |
| 'total_emissions_reduction', | |
| ]) | |
| df_results.to_csv(output_file_path, index=False) | |
| def perform_mapping(self, input_word, attempts=0): | |
| # if the input word is a USDA food item, we can skip the similarity check | |
| # this is a special case because the USDA food items are Government Donation (Not Counted) items | |
| if 'usda' in input_word.lower(): | |
| return { | |
| 'input_word': input_word, | |
| 'cleaned_word': clean_word(input_word), | |
| 'matching_word': 'USDA Food Item', | |
| 'dictionary_word': 'USDA Food Item', | |
| 'similarity_score': 1.0, | |
| 'confidence_score': 1.0, | |
| 'similar_words': None, | |
| 'is_food': True, | |
| 'food_nonfood_score': 1.0 | |
| } | |
| mapping = self.similarity_fast.find_most_similar_word(input_word) | |
| # skip slow mapping for now | |
| # if mapping['similarity_score'] < similarity_threshold: | |
| # print("Attempting slow mapping") | |
| # slow_mapping = self.similarity_slow.find_most_similar_word(input_word) | |
| # print(f" - Slow: {slow_mapping}") | |
| # if slow_mapping['similarity_score'] > mapping['similarity_score']: | |
| # mapping = slow_mapping | |
| # if mapping['similarity_score'] < similarity_threshold and len(input_word.split(' ')) > 1: | |
| # print(" - Attempting reverse mapping") | |
| # reversed_input_word = ' '.join(input_word.split(' ')[::-1]) | |
| # reversed_mapping = self.similarity_fast.find_most_similar_word(reversed_input_word) | |
| # if reversed_mapping['similarity_score'] > mapping['similarity_score']: | |
| # reversed_mapping.update( | |
| # { | |
| # 'input_word': input_word, | |
| # 'cleaned_word': mapping['cleaned_word'] | |
| # } | |
| # ) | |
| # mapping = reversed_mapping | |
| # check if the cleaned_word is a substring of the matching_word | |
| is_substring = mapping['cleaned_word'] in mapping['matching_word'] | |
| if mapping['similarity_score'] < similarity_threshold and not is_substring: | |
| print(" - Attempting GPT mapping") | |
| try: | |
| gpt_recommended_word = query_gpt(input_word) | |
| if gpt_recommended_word: | |
| if gpt_recommended_word == 'Non-Food Item': | |
| mapping.update( | |
| { | |
| 'similarity_score': 1.0, | |
| 'confidence_score': 1.0, | |
| 'is_food': False, | |
| 'food_nonfood_score': 1.0 | |
| } | |
| ) | |
| return mapping | |
| elif gpt_recommended_word == 'Mixed Food Items': | |
| mapping.update( | |
| { | |
| 'matching_word': 'Mixed Food Items', | |
| 'dictionary_word': 'Mixed Food Items', 'similarity_score': 1.0, | |
| 'confidence_score': 1.0 | |
| } | |
| ) | |
| return mapping | |
| else: | |
| gpt_mapping = self.similarity_fast.find_most_similar_word(gpt_recommended_word) | |
| if gpt_mapping['similarity_score'] > mapping['similarity_score']: | |
| gpt_mapping.update( | |
| { | |
| 'input_word': input_word, | |
| 'cleaned_word': mapping['cleaned_word'] | |
| } | |
| ) | |
| mapping = gpt_mapping | |
| except Exception as e: | |
| print(f" - Error querying GPT: {e}") | |
| return mapping | |
| def handle_multi_item(self, input_word): | |
| # The input word has a comma or a slash in it | |
| # If it has more commas, its comma-delimited | |
| # If it has more slashes, its slash-delimited | |
| # If it has equal number of commas and slashes, we'll go with slashes | |
| input_word_parts = extract_items(input_word) | |
| print(f" - Extracted items: {input_word_parts}") | |
| mappings = [] | |
| for part in input_word_parts: | |
| mapping = self.handle_single_item(part) | |
| mappings.append(mapping) | |
| # look up the dictionary values for each mapping | |
| # find the wweia category | |
| # if all mappings have the same wweia category, return "homogenous", else "heterogeneous" | |
| # if is_food is False for any mappings, return "Non-Food Item" as dictionary word | |
| for mapping in mappings: | |
| if mapping['is_food'] == False: | |
| return { | |
| 'input_word': input_word, | |
| 'cleaned_word': mapping['cleaned_word'], | |
| 'matching_word': 'Non-Food Item', | |
| 'dictionary_word': 'Non-Food Item', | |
| 'similarity_score': None, | |
| 'confidence_score': None, | |
| 'similar_words': None, | |
| 'is_food': False, | |
| 'food_nonfood_score': 1.0 | |
| } | |
| break | |
| dictionary_words = [mapping['dictionary_word'] for mapping in mappings] | |
| if len(set(dictionary_words)) == 0: | |
| return { | |
| 'input_word': input_word, | |
| 'cleaned_word': None, | |
| 'matching_word': None, | |
| 'dictionary_word': None, | |
| 'similarity_score': None, | |
| 'confidence_score': None, | |
| 'similar_words': None, | |
| 'is_food': None, | |
| 'food_nonfood_score': None | |
| } | |
| # check if "heterogeneous" is in the wweia category of any of the mappings | |
| # otherwise we find the mapping with the lowest DMC value, and return that as the dictionary word, dmc, wc, and leakage values | |
| heterogeneous_exists = False | |
| most_conservative_mapping = None | |
| for mapping in mappings: | |
| if mapping['wweia_category'] == "Heterogeneous Mixture": | |
| heterogeneous_exists = True | |
| break | |
| else: | |
| dry_matter_content = mapping.get('dry_matter_content') | |
| if dry_matter_content is not None: | |
| if most_conservative_mapping is None or dry_matter_content < most_conservative_mapping['dry_matter_content']: | |
| most_conservative_mapping = mapping | |
| mixture_data = {} | |
| if heterogeneous_exists: | |
| mixture_data = { | |
| 'matching_word': 'Heterogeneous Mixture', | |
| 'dictionary_word': 'Heterogeneous Mixture', | |
| 'wweia_category': 'Heterogeneous Mixture', | |
| 'dry_matter_content': 0.27, | |
| 'water_content': 0.73, | |
| 'leakage': 0.1 | |
| } | |
| else: | |
| mixture_data = { | |
| 'matching_word': most_conservative_mapping['matching_word'], | |
| 'dictionary_word': f"{most_conservative_mapping['dictionary_word']} (Lowest DMC)", | |
| 'wweia_category': most_conservative_mapping['wweia_category'], | |
| 'dry_matter_content': most_conservative_mapping['dry_matter_content'], | |
| 'water_content': most_conservative_mapping['water_content'], | |
| 'leakage': most_conservative_mapping['leakage'] | |
| } | |
| print(f" - Mixture data: {mixture_data}") | |
| return { | |
| 'input_word': input_word, | |
| 'cleaned_word': None, | |
| 'similarity_score': None, | |
| 'confidence_score': None, | |
| 'similar_words': None, | |
| 'is_food': True, | |
| 'food_nonfood_score': 1.0, | |
| **mixture_data | |
| } | |
| def handle_single_item(self, input_word): | |
| input_word_clean = clean_word(input_word) | |
| # try the singular form of the word | |
| singular = self.pluralizer.pluralize(input_word_clean, 1) | |
| mapping = get_mapping_from_db(self.db_cursor, singular) | |
| if mapping: | |
| print(f" - Found mapping in db: {mapping}") | |
| return self.wrap_mapping_with_dictionary_data(mapping) | |
| # try the plural form of the word | |
| plural = self.pluralizer.pluralize(input_word_clean, 2) | |
| mapping = get_mapping_from_db(self.db_cursor, plural) | |
| if mapping: | |
| print(f" - Found mapping in db: {mapping}") | |
| return self.wrap_mapping_with_dictionary_data(mapping) | |
| food_nonfood = classify_as_food_nonfood(input_word) | |
| # if we're very confident that the word is non-food, let's not even classify it | |
| if food_nonfood[1] > 0.9 and food_nonfood[0] == False: | |
| mapping = { | |
| 'input_word': input_word, | |
| 'cleaned_word': input_word_clean, | |
| 'matching_word': 'Non-Food Item', | |
| 'dictionary_word': 'Non-Food Item', | |
| 'similarity_score': None, | |
| 'confidence_score': None, | |
| 'similar_words': None, | |
| 'is_food': False, | |
| 'food_nonfood_score': food_nonfood[1] | |
| } | |
| store_mapping_to_db(self.db_cursor, self.db_conn, mapping) | |
| return self.wrap_mapping_with_dictionary_data(mapping) | |
| mapping = self.perform_mapping(input_word) | |
| food_nonfood_pessimistic = pessimistic_food_nonfood_score(food_nonfood, mapping['similarity_score']) | |
| mapping.update({ | |
| 'is_food': food_nonfood_pessimistic[0], | |
| 'food_nonfood_score': food_nonfood_pessimistic[1] | |
| }) | |
| print(f" - Storing new mapping to db: {mapping}") | |
| store_mapping_to_db(self.db_cursor, self.db_conn, mapping) | |
| return self.wrap_mapping_with_dictionary_data(mapping) | |
| def wrap_mapping_with_dictionary_data(self, mapping): | |
| if not mapping: | |
| return None | |
| dictionary_result = get_dictionary_data_from_db(self.db_cursor, mapping['dictionary_word']) | |
| mapping.update({ | |
| 'wweia_category': dictionary_result['wweia_category'] if dictionary_result else None, | |
| 'water_content': dictionary_result['water_content'] if dictionary_result else None, | |
| 'dry_matter_content': dictionary_result['dry_matter_content'] if dictionary_result else None, | |
| 'leakage': dictionary_result['leakage'] if dictionary_result else None | |
| }) | |
| return mapping | |
| def add_carbon_credit_data(self, mapping, donor, date, weight): | |
| if not mapping: | |
| return None | |
| mapping.update({ | |
| 'donor': donor | |
| }) | |
| mapping.update({ | |
| 'date': date, | |
| 'weight': weight, | |
| 'weight_metric_tonnes': float(weight) * 0.000453592, | |
| 'distance': 250, | |
| 'ef': 2.968073544, | |
| 'mt_lb_mile': 0.0000000809, | |
| }) | |
| required_fields_exist = 'leakage' in mapping and mapping['leakage'] is not None and 'dry_matter_content' in mapping and mapping['dry_matter_content'] is not None | |
| if mapping['is_food'] == False or required_fields_exist == False: | |
| return { | |
| 'baseline_emissions': None, | |
| 'leakage_emissions': None, | |
| 'project_emissions': None, | |
| 'total_emissions_reduction': None, | |
| **mapping | |
| } | |
| print(f" - Calculating carbon credits for: {mapping}") | |
| baseline_emissions = mapping['weight_metric_tonnes'] * mapping['dry_matter_content'] * mapping['ef'] | |
| leakage_emissions = mapping['leakage'] * baseline_emissions | |
| project_emissions = mapping['distance'] * mapping['mt_lb_mile'] * baseline_emissions | |
| total_emissions_reduction = baseline_emissions - leakage_emissions - project_emissions | |
| mapping.update({ | |
| 'baseline_emissions': baseline_emissions, | |
| 'leakage_emissions': leakage_emissions, | |
| 'project_emissions': project_emissions, | |
| 'total_emissions_reduction': total_emissions_reduction | |
| }) | |
| return mapping | |
| def match_words(self, input_data, stream_results=False): | |
| # input_data is a list of tuples, where each tuple is (description, donor) | |
| results = [] | |
| for input_item in tqdm(input_data, desc="Processing input words"): | |
| input_word = input_item[0] | |
| input_donor = input_item[1] if len(input_item) > 1 else None | |
| input_date = input_item[2] if len(input_item) > 2 else None | |
| input_weight = input_item[3] if len(input_item) > 3 else None | |
| if not isinstance(input_word, str) or pd.isna(input_word) or input_word == "" or input_word.lower() == "nan": | |
| continue | |
| print() | |
| print(f"Processing: {input_word}") | |
| if ',' in input_word or '/' in input_word: | |
| mapping = self.handle_multi_item(input_word) | |
| else: | |
| mapping = self.handle_single_item(input_word) | |
| if mapping: | |
| mapping = self.add_carbon_credit_data(mapping, input_donor, input_date, input_weight) | |
| results.append(mapping) | |
| if stream_results: | |
| return mapping | |
| self.save_to_csv(results) | |
| return results | |