algo.py

# algo.py
import math
import time
import queue
import logging
import threading
import pandas as pd
from tqdm import tqdm
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, is_empty_word
from db.db_utils import store_mapping_to_db, cached_get_mapping_from_db, get_dictionary_data_from_db, store_result_to_db
from ask_gpt import query_gpt
from multi_food_item_detector import extract_items, has_delimiters
from mapping_template import empty_template, heterogeneous_template, multi_item_template, nonfood_template, usda_template
# from tasks import insert_result
from specificity_classifier import classify_text_to_specificity

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
similarity_threshold = 0.78


def insert_result(db_conn, run_key, mappings):
    db_cursor = db_conn.cursor()
    for mapping in mappings:
        store_result_to_db(db_cursor, db_conn, run_key, mapping)


class Algo:
    def __init__(self, db_conn, run_key=None):
        self.db_conn = db_conn
        self.run_key = run_key if run_key else int(time.time())

        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()
        self.mappings_with_dictionary = self.initialize_mappings()

    def initialize_mappings(self):
        self.db_cursor.execute('SELECT cleaned_word, dictionary_word, is_food, similarity_score, food_nonfood_score, wweia_category, sr_legacy_food_category, water_content, dry_matter_content, leakage, ignore, specificity from mappings join dictionary on mappings.dictionary_word = dictionary.description')
        rows = self.db_cursor.fetchall()
        mappings_with_dictionary = {}
        for row in rows:
            mappings_with_dictionary[row[0]] = {
                'cleaned_word': row[0],
                'dictionary_word': row[1],
                'is_food': row[2],
                'similarity_score': row[3],
                'food_nonfood_score': row[4],
                'wweia_category': row[5],
                'sr_legacy_food_category': row[6],
                'water_content': row[7],
                'dry_matter_content': row[8],
                'leakage': row[9],
                'ignore': row[10],
                'specificity': row[11]
            }
        return mappings_with_dictionary

    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 usda_template(input_word, clean_word(input_word)) 

        mapping = self.similarity_fast.find_most_similar_word(input_word)

        logging.info(f" - Simlarity Fast mapping: {mapping}")
        # check if the cleaned_word is a substring of the most_similar_word
        is_substring = mapping['cleaned_word'] in mapping['most_similar_word']

        if mapping['similarity_score'] < similarity_threshold and not is_substring:
            logging.info(" - 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 == 'Heterogeneous Mixture':
                        mapping.update(
                            {
                                'dictionary_word': 'Heterogeneous Mixture', 'similarity_score': 1.0, 
                                'confidence_score': 1.0
                            }
                        )
                        return mapping
                    elif gpt_recommended_word == 'Broad Category':
                        category_mapping = self.similarity_fast.find_most_similar_word(input_word, True)
                        mapping.update(
                            {
                                'dictionary_word': category_mapping['dictionary_word'],
                                'similarity_score': category_mapping['similarity_score'],
                                'confidence_score': category_mapping['confidence_score']
                            }
                        )
                    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:
                logging.info(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
        logging.info(f"Handling multi-item {input_word}")
       
        input_word_parts = extract_items(input_word)
        logging.info(f" - Extracted items: {input_word_parts}")
        mappings = []
        for part in input_word_parts:
            mapping = self.handle_single_item(part)
            if mapping:
                # Some words in the mapping can be ignored because they are 
                # just filler words that don't add any value to the mapping
                if mapping['ignore'] == False:
                    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 nonfood_template(
                    input_word, 
                    mapping['cleaned_word'], 
                    mapping['food_nonfood_score']
                )
                break

        dictionary_words = [mapping['dictionary_word'] for mapping in mappings]

        if len(set(dictionary_words)) == 0:
            return empty_template(input_word)

        # 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['sr_legacy_food_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.get('dry_matter_content', float('inf')):
                        most_conservative_mapping = mapping

        if heterogeneous_exists:
            return heterogeneous_template(input_word)
        elif most_conservative_mapping is not None:
            return multi_item_template(input_word, None, most_conservative_mapping)
        else:
            logging.warning(f" - No mappings found for {input_word}")
            return None

    def handle_single_item(self, input_word):
        input_word_clean = clean_word(input_word)

        if not input_word_clean:
            return None

        if input_word_clean == "":
            return None

        # try the singular form of the word
        singular = self.pluralizer.pluralize(input_word_clean, 1)
        # mapping = cached_get_mapping_from_db(self.db_cursor, singular)
        mapping_with_dict = self.mappings_with_dictionary.get(singular)
        if mapping_with_dict:
            mapping_with_dict.update({
                'input_word': input_word,
            })
            logging.info(f" - Found mapping in db: {mapping_with_dict}")
            return mapping_with_dict

        # try the plural form of the word
        plural = self.pluralizer.pluralize(input_word_clean, 2)
        mapping_with_dict = self.mappings_with_dictionary.get(plural)
        if mapping_with_dict:
            mapping_with_dict.update({
                'input_word': input_word,
            })
            logging.info(f" - Found mapping in db: {mapping_with_dict}")
            return mapping_with_dict

        food_nonfood = classify_as_food_nonfood(input_word_clean)

        # 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 = nonfood_template(input_word, input_word_clean, food_nonfood[1])
            store_mapping_to_db(self.db_cursor, self.db_conn, mapping)
            self.mappings_with_dictionary[input_word_clean] = mapping
            return self.wrap_mapping_with_dictionary_data(mapping)

        mapping = self.perform_mapping(input_word)

        specificity = classify_text_to_specificity(input_word_clean)
        mapping.update({
            'specificity': specificity
        })

        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]
        })

        store_mapping_to_db(self.db_cursor, self.db_conn, mapping)
        self.mappings_with_dictionary[input_word_clean] = 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'])

        # set default on ignore
        ignore = mapping['ignore'] if 'ignore' in mapping else False

        mapping.update({
            'wweia_category': dictionary_result['wweia_category'] if dictionary_result else None,
            'sr_legacy_food_category': dictionary_result['sr_legacy_food_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,
            'ignore': ignore
        })

        return mapping
    
    def add_carbon_credit_data(self, mapping, donor, date, weight):
        if not mapping:
            return None

        mapping.update({
            'donor': donor
        })
        try:
            weight = float(weight)
        except ValueError:
            weight = 0
        except Exception as e:
            logging.info(f" - Error converting weight to float: {e}")
            weight = 0

        if math.isnan(weight):
            weight = 0

        mapping.update({
            'date': date,
            'weight': weight,
            'weight_metric_tonnes': 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
            }

        logging.info(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):
        # input_data is a list of tuples, where each tuple is (description, donor)
        results = []
        result_batch = []
        for input_item in tqdm(input_data, desc="Processing input words"):
            input_word = input_item[0]
            input_word_alt = input_item[1] if len(input_item) > 1 else None
            input_row_num = input_item[2] if len(input_item) > 2 else None
            input_donor = input_item[3] if len(input_item) > 3 else None
            input_date = input_item[4] if len(input_item) > 4 else None
            input_weight = input_item[5] if len(input_item) > 5 else None

            logging.info("")
            logging.info(f"Processing: {input_word}")

            is_empty = False
            if is_empty_word(input_word):
                if is_empty_word(input_word_alt):
                    mapping = empty_template(input_word)
                    is_empty = True
                else:
                    input_word = input_word_alt
            
            if not is_empty:
                if has_delimiters(input_word):
                    mapping = self.handle_multi_item(input_word)
                else:
                    mapping = self.handle_single_item(input_word)

            if mapping:
                mapping = dict(mapping)
                mapping = self.add_carbon_credit_data(mapping, input_donor, input_date, input_weight)
                mapping.update({
                    'run_row': input_row_num
                })
                result_batch.append(mapping)
                # store_result_to_db(self.db_cursor, self.db_conn, self.run_key, mapping)
                results.append(mapping)

            if len(result_batch) >= 100:
                insert_result(self.db_conn, self.run_key, result_batch)
                result_batch = []

        if len(result_batch) > 0:
            insert_result(self.db_conn, self.run_key, result_batch)
            result_batch = []


        return results