637 lines
27 KiB
Python
637 lines
27 KiB
Python
import re
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import arrow
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from json import loads, dumps
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from unidecode import unidecode
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from difflib import SequenceMatcher
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from itertools import permutations
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from time import perf_counter
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from sqlalchemy import text as sqlalchemy_text
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from Controllers.Postgres.engine import get_session_factory
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from Schemas.account.account import AccountRecordsPredict, AccountRecords
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def clean_text(text):
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text = str(text)
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text = re.sub(r'\d{8,}', ' ', text)
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# text = re.sub(r'\b[A-Za-z0-9]*?[0-9]+[A-Za-z0-9]*?[A-Za-z]+[A-Za-z0-9]*\b|\b[A-Za-z0-9]*?[A-Za-z]+[A-Za-z0-9]*?[0-9]+[A-Za-z0-9]*\b', ' ', text)
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text = text.replace("/", " ")
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text = text.replace("_", " ")
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text_remove_underscore = text.replace("-", " ").replace("+", " ")
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text_remove_asterisk = text_remove_underscore.replace("*", " ")
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text_remove_comma = text_remove_asterisk.replace(",", " ")
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text_remove_dots = text_remove_comma.replace(".", " ")
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text_remove_dots = re.sub(r'\s+', ' ', text_remove_dots)
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text_remove_dots = text_remove_dots.strip()
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return text_remove_dots
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def normalize_text(text):
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text = text.replace('İ', 'i')
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text = text.replace('I', 'ı')
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text = text.replace('Ş', 'ş')
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text = text.replace('Ğ', 'ğ')
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text = text.replace('Ü', 'ü')
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text = text.replace('Ö', 'ö')
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text = text.replace('Ç', 'ç')
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return unidecode(text).lower()
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def get_person_initials(person):
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parts = [person.get("firstname", ""), person.get("middle_name", ""), person.get("surname", ""), person.get("birthname", "")]
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return [unidecode(p.strip())[0].upper() for p in parts if p]
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def get_text_initials(matched_text):
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return [unidecode(word.strip())[0].upper() for word in matched_text.split() if word.strip()]
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def generate_dictonary_of_patterns(people):
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"""
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completly remove middle_name instead do regex firstName + SomeWord + surname
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"""
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patterns_dict = {}
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for person in people:
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person_id = person.get('id')
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firstname = person.get('firstname', '').strip() if person.get('firstname') else ""
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middle_name = person.get('middle_name', '').strip() if person.get('middle_name') else ""
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surname = person.get('surname', '').strip() if person.get('surname') else ""
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birthname = person.get('birthname', '').strip() if person.get('birthname') else ""
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if not firstname or not surname:
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continue
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name_parts = {
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'firstname': {
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'orig': firstname,
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'norm': normalize_text(firstname) if firstname else "",
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'init': normalize_text(firstname)[0] if firstname else ""
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},
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'surname': {
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'orig': surname,
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'norm': normalize_text(surname) if surname else "",
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'init': normalize_text(surname)[0] if surname else ""
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}
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}
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if middle_name:
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name_parts['middle_name'] = {
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'orig': middle_name,
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'norm': normalize_text(middle_name) if middle_name else "",
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'init': normalize_text(middle_name)[0] if middle_name else ""
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}
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if birthname and normalize_text(birthname) != normalize_text(surname):
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name_parts['birthname'] = {
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'orig': birthname,
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'norm': normalize_text(birthname),
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'init': normalize_text(birthname)[0] if birthname else ""
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}
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person_patterns = set()
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def create_pattern(parts, formats, separators=None):
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if separators is None:
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separators = [""]
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patterns = []
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for fmt in formats:
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for sep in separators:
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pattern_parts = []
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for part_type, part_name in fmt:
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if part_name in parts and part_type in parts[part_name]:
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pattern_parts.append(re.escape(parts[part_name][part_type]))
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if pattern_parts:
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patterns.append(r"\b" + sep.join(pattern_parts) + r"\b")
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return patterns
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name_formats = [
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[('orig', 'firstname'), ('orig', 'surname')],
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[('norm', 'firstname'), ('norm', 'surname')],
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[('orig', 'surname'), ('orig', 'firstname')],
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[('norm', 'surname'), ('norm', 'firstname')],
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]
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if 'middle_name' in name_parts:
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name_formats = [
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[('orig', 'firstname'), ('orig', 'middle_name'), ('orig', 'surname')],
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[('norm', 'firstname'), ('norm', 'middle_name'), ('norm', 'surname')],
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]
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person_patterns.update(create_pattern(name_parts, name_formats, [" ", ""]))
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if 'middle_name' in name_parts:
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middle_name_formats = [
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[('orig', 'firstname'), ('orig', 'middle_name')],
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[('norm', 'firstname'), ('norm', 'middle_name')],
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[('orig', 'middle_name'), ('orig', 'surname')],
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[('norm', 'middle_name'), ('norm', 'surname')],
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]
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person_patterns.update(create_pattern(name_parts, middle_name_formats, [" ", ""]))
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if 'birthname' in name_parts and name_parts['surname']['orig'] != name_parts['birthname']['orig']:
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birthname_formats = [
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[('orig', 'firstname'), ('orig', 'birthname')],
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[('norm', 'firstname'), ('norm', 'birthname')],
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[('orig', 'birthname'), ('orig', 'firstname')],
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[('norm', 'birthname'), ('norm', 'firstname')],
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]
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person_patterns.update(create_pattern(name_parts, birthname_formats, [" ", ""]))
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initial_formats = [
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[('init', 'firstname'), ('init', 'middle_name'), ('init', 'surname')],
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[('init', 'firstname'), ('init', 'surname')],
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]
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person_patterns.update(create_pattern(name_parts, initial_formats, ["", ".", " ", ". "]))
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if 'middle_name' in name_parts:
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triple_initial_formats = [
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[('init', 'firstname'), ('init', 'middle_name'), ('init', 'surname')],
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]
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person_patterns.update(create_pattern(name_parts, triple_initial_formats, ["", ".", " ", ". "]))
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compiled_patterns = [re.compile(pattern, re.IGNORECASE) for pattern in person_patterns]
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patterns_dict[person_id] = compiled_patterns
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return patterns_dict
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def extract_person_name_with_regex(found_dict, process_comment, patterns_dict, people):
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cleaned_text = process_comment
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all_matches = []
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for person_id, patterns in patterns_dict.items():
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person = next((p for p in people if p.get('id') == person_id), None)
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if not person:
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continue
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firstname_norm = normalize_text(person.get("firstname", "").strip()) if person.get("firstname") else ""
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middle_name_norm = normalize_text(person.get("middle_name", "").strip()) if person.get("middle_name") else ""
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surname_norm = normalize_text(person.get("surname", "").strip()) if person.get("surname") else ""
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birthname_norm = normalize_text(person.get("birthname", "").strip()) if person.get("birthname") else ""
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text_norm = normalize_text(process_comment)
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person_matches = []
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for pattern in patterns:
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for match in pattern.finditer(text_norm):
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start, end = match.span()
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matched_text = process_comment[start:end]
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matched_text_norm = normalize_text(matched_text)
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is_valid_match = False
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# Strict validation: require both firstname AND surname/birthname
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# No single-word matches allowed
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if len(matched_text_norm.split()) <= 1:
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# Single word matches are not allowed
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is_valid_match = False
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else:
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# For multi-word matches, require firstname AND (surname OR birthname)
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has_firstname = firstname_norm and firstname_norm in matched_text_norm
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has_surname = surname_norm and surname_norm in matched_text_norm
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has_birthname = birthname_norm and birthname_norm in matched_text_norm
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# Both firstname and surname/birthname must be present
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if (has_firstname and has_surname) or (has_firstname and has_birthname):
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is_valid_match = True
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if is_valid_match:
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person_matches.append({
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'matched_text': matched_text,
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'start': start,
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'end': end
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})
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if person_matches:
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person_matches.sort(key=lambda x: len(x['matched_text']), reverse=True)
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non_overlapping_matches = []
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for match in person_matches:
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overlaps = False
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for existing_match in non_overlapping_matches:
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if (match['start'] < existing_match['end'] and match['end'] > existing_match['start']):
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overlaps = True
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break
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if not overlaps:
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non_overlapping_matches.append(match)
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if non_overlapping_matches:
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found_dict["name_match"] = person
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all_matches.extend([(match, person) for match in non_overlapping_matches])
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if all_matches:
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all_matches.sort(key=lambda x: x[0]['start'], reverse=True)
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for match, person in all_matches:
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matched_text = match['matched_text']
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matched_words = matched_text.split()
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for word in matched_words:
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word_norm = normalize_text(word).strip()
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if not word_norm:
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continue
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text_norm = normalize_text(cleaned_text)
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for word_match in re.finditer(rf'\b{re.escape(word_norm)}\b', text_norm, re.IGNORECASE):
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start, end = word_match.span()
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cleaned_text = cleaned_text[:start] + ' ' * (end - start) + cleaned_text[end:]
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cleaned_text = re.sub(r'\s+', ' ', cleaned_text).strip()
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return found_dict, cleaned_text
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def extract_build_parts_info(found_dict, process_comment):
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"""
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Regex of parts such as :
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2 nolu daire
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9 NUMARALI DAI
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daire 3
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3 nolu dairenin
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11nolu daire
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Daire No 12
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2NOLU DAIRE
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12 No lu daire
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D:10
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NO:11
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NO :3
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"""
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# Initialize apartment number variable
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apartment_number = None
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cleaned_text = process_comment
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def clean_text_apartment_number(text, match):
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clean_text = text.replace(match.group(0), '').strip()
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clean_text = re.sub(r'\s+', ' ', clean_text).strip()
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return clean_text
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# Pattern 1: X nolu daire (with space)
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pattern1 = re.compile(r'(\d+)\s*nolu\s*daire', re.IGNORECASE)
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match = pattern1.search(cleaned_text)
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if match:
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apartment_number = match.group(1)
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found_dict['apartment_number'] = apartment_number
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return found_dict, clean_text_apartment_number(cleaned_text, match)
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# Pattern 4: X nolu dairenin
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pattern4 = re.compile(r'(\d+)\s*nolu\s*daire\w*', re.IGNORECASE)
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match = pattern4.search(cleaned_text)
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if match:
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apartment_number = match.group(1)
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found_dict['apartment_number'] = apartment_number
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return found_dict, clean_text_apartment_number(cleaned_text, match)
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# Pattern 5: XNolu daire (without space)
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pattern5 = re.compile(r'(\d+)nolu\s*daire', re.IGNORECASE)
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match = pattern5.search(cleaned_text)
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if match:
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apartment_number = match.group(1)
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found_dict['apartment_number'] = apartment_number
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return found_dict, clean_text_apartment_number(cleaned_text, match)
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# Pattern 7: XNOLU DAIRE (all caps, no space)
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pattern7 = re.compile(r'(\d+)nolu\s*daire', re.IGNORECASE)
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match = pattern7.search(cleaned_text)
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if match:
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apartment_number = match.group(1)
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found_dict['apartment_number'] = apartment_number
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return found_dict, clean_text_apartment_number(cleaned_text, match)
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# Pattern 8: X No lu daire
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pattern8 = re.compile(r'(\d+)\s*no\s*lu\s*daire', re.IGNORECASE)
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match = pattern8.search(cleaned_text)
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if match:
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apartment_number = match.group(1)
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found_dict['apartment_number'] = apartment_number
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return found_dict, clean_text_apartment_number(cleaned_text, match)
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# Pattern 6: Daire No X
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pattern6 = re.compile(r'daire\s*no\s*(\d+)', re.IGNORECASE)
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match = pattern6.search(cleaned_text)
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if match:
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apartment_number = match.group(1)
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found_dict['apartment_number'] = apartment_number
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return found_dict, clean_text_apartment_number(cleaned_text, match)
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# Pattern 2: X NUMARALI DAI
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pattern2 = re.compile(r'(\d+)\s*numarali\s*dai', re.IGNORECASE)
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match = pattern2.search(cleaned_text)
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if match:
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apartment_number = match.group(1)
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found_dict['apartment_number'] = apartment_number
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return found_dict, clean_text_apartment_number(cleaned_text, match)
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# Pattern 3: daire X
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pattern3 = re.compile(r'daire\s*(\d+)', re.IGNORECASE)
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match = pattern3.search(cleaned_text)
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if match:
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apartment_number = match.group(1)
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found_dict['apartment_number'] = apartment_number
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return found_dict, clean_text_apartment_number(cleaned_text, match)
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# Pattern 9: D:X
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pattern9 = re.compile(r'd\s*:\s*(\d+)', re.IGNORECASE)
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match = pattern9.search(cleaned_text)
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if match:
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apartment_number = match.group(1)
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found_dict['apartment_number'] = apartment_number
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return found_dict, clean_text_apartment_number(cleaned_text, match)
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# Pattern 10: NO:X or NO :X
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pattern10 = re.compile(r'no\s*:\s*(\d+)', re.IGNORECASE)
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match = pattern10.search(cleaned_text)
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if match:
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apartment_number = match.group(1)
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found_dict['apartment_number'] = apartment_number
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return found_dict, clean_text_apartment_number(cleaned_text, match)
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return found_dict, cleaned_text
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def extract_months(found_dict, process_comment):
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"""
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Extract Turkish month names and abbreviations from the process comment
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"""
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original_text = process_comment
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# Updated dictionary with normalized keys for better matching
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month_to_number_dict = {
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"ocak": 1, "şubat": 2, "mart": 3, "nisan": 4, "mayıs": 5, "haziran": 6,
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"temmuz": 7, "ağustos": 8, "eylül": 9, "ekim": 10, "kasım": 11, "aralık": 12,
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# Add normalized versions without Turkish characters
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"ocak": 1, "subat": 2, "mart": 3, "nisan": 4, "mayis": 5, "haziran": 6,
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"temmuz": 7, "agustos": 8, "eylul": 9, "ekim": 10, "kasim": 11, "aralik": 12
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}
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def clean_text_month(text, match):
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clean_text = text.replace(match.group(0), '').strip()
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clean_text = re.sub(r'\s+', ' ', clean_text).strip()
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return clean_text
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def normalize_turkish(text):
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"""Properly normalize Turkish text for case-insensitive comparison"""
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text = text.lower()
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text = text.replace('i̇', 'i') # Handle dotted i properly
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text = text.replace('ı', 'i') # Convert dotless i to regular i for matching
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text = unidecode(text) # Remove other diacritics
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return text
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if 'months' not in found_dict:
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found_dict['months'] = []
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months_found, working_text = False, original_text
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for month in turkish_months:
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pattern = re.compile(r'\b' + re.escape(month) + r'\b', re.IGNORECASE)
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for match in pattern.finditer(original_text):
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matched_text = match.group(0)
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normalized_month = normalize_turkish(month)
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month_number = None
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if month.lower() in month_to_number_dict:
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month_number = month_to_number_dict[month.lower()]
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elif normalized_month in month_to_number_dict:
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month_number = month_to_number_dict[normalized_month]
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month_info = {'name': month, 'number': month_number}
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found_dict['months'].append(month_info)
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months_found = True
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working_text = working_text.replace(matched_text, '', 1)
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for abbr, full_month in turkish_months_abbr.items():
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pattern = re.compile(r'\b' + re.escape(abbr) + r'\b', re.IGNORECASE)
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for match in pattern.finditer(working_text):
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matched_text = match.group(0)
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normalized_month = normalize_turkish(full_month)
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month_number = None
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if full_month.lower() in month_to_number_dict:
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month_number = month_to_number_dict[full_month.lower()]
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elif normalized_month in month_to_number_dict:
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month_number = month_to_number_dict[normalized_month]
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month_info = {'name': full_month, 'number': month_number}
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found_dict['months'].append(month_info)
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months_found = True
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working_text = working_text.replace(matched_text, '', 1)
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return found_dict, working_text
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def extract_year(found_dict, process_comment):
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"""
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Extract years from the process comment
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"""
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original_text = process_comment
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if 'years' not in found_dict:
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found_dict['years'] = []
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working_text = original_text
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for year in range(start_year, current_year + 1):
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pattern = re.compile(r'\b' + str(year) + r'\b', re.IGNORECASE)
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for match in pattern.finditer(original_text):
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matched_text = match.group(0)
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if str(matched_text).isdigit():
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found_dict['years'].append(int(matched_text))
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working_text = working_text.replace(matched_text, '', 1)
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return found_dict, working_text
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def extract_payment_type(found_dict, process_comment):
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"""
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Extract payment type from the process comment
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aidat
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AİD
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aidatı
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TADİLAT
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YAKIT
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yakıt
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yakit
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"""
|
||
original_text = process_comment
|
||
working_text = original_text
|
||
|
||
if 'payment_types' not in found_dict:
|
||
found_dict['payment_types'] = []
|
||
|
||
payment_keywords = {
|
||
'aidat': ['aidat', 'aİd', 'aid', 'aidatı', 'aidati'],
|
||
'tadilat': ['tadilat', 'tadİlat', 'tadilatı'],
|
||
'yakit': ['yakit', 'yakıt', 'yakıtı', 'yakiti']
|
||
}
|
||
|
||
for payment_type, keywords in payment_keywords.items():
|
||
for keyword in keywords:
|
||
pattern = re.compile(r'\b' + keyword + r'\b', re.IGNORECASE)
|
||
for match in pattern.finditer(original_text):
|
||
matched_text = match.group(0)
|
||
if payment_type not in found_dict['payment_types']:
|
||
found_dict['payment_types'].append(payment_type)
|
||
working_text = working_text.replace(matched_text, '', 1)
|
||
|
||
return found_dict, working_text
|
||
|
||
|
||
def main(session, account_records, people):
|
||
|
||
list_of_regex_patterns = generate_dictonary_of_patterns(people=people)
|
||
dicts_found = dict()
|
||
dicts_not_found = dict()
|
||
count_extracted = 0
|
||
for account_record in account_records:
|
||
account_record_id = str(account_record["id"])
|
||
found_dict = {}
|
||
process_comment_iteration = clean_text(text=account_record["process_comment"])
|
||
found_dict, cleaned_process_comment = extract_person_name_with_regex(
|
||
found_dict=found_dict, process_comment=process_comment_iteration, patterns_dict=list_of_regex_patterns, people=people
|
||
)
|
||
|
||
found_dict, cleaned_process_comment = extract_build_parts_info(
|
||
found_dict=found_dict, process_comment=cleaned_process_comment
|
||
)
|
||
found_dict, cleaned_process_comment = extract_months(
|
||
found_dict=found_dict, process_comment=cleaned_process_comment
|
||
)
|
||
found_dict, cleaned_process_comment = extract_year(
|
||
found_dict=found_dict, process_comment=cleaned_process_comment
|
||
)
|
||
found_dict, cleaned_process_comment = extract_payment_type(
|
||
found_dict=found_dict, process_comment=cleaned_process_comment
|
||
)
|
||
if found_dict:
|
||
dicts_found[str(account_record_id)] = found_dict
|
||
else:
|
||
dicts_not_found[str(account_record_id)] = account_record_id
|
||
|
||
|
||
for id_, item in dicts_found.items():
|
||
AccountRecordsPredict.set_session(session)
|
||
AccountRecords.set_session(session)
|
||
|
||
months_are_valid = bool(item.get("months", []))
|
||
years_are_valid = bool(item.get("years", []))
|
||
payment_types_are_valid = bool(item.get("payment_types", []))
|
||
apartment_number_are_valid = bool(item.get("apartment_number", []))
|
||
person_name_are_valid = bool(item.get("name_match", []))
|
||
account_record_to_save = AccountRecords.query.filter_by(id=int(id_)).first()
|
||
save_dict = dict(
|
||
account_records_id=account_record_to_save.id, account_records_uu_id=str(account_record_to_save.uu_id), prediction_model="regex", treshold=1, is_first_prediction=False
|
||
)
|
||
update_dict = dict(prediction_model="regex", treshold=1, is_first_prediction=False)
|
||
if any([months_are_valid, years_are_valid, payment_types_are_valid, apartment_number_are_valid, person_name_are_valid]):
|
||
count_extracted += 1
|
||
if months_are_valid:
|
||
print(f"months: {item['months']}")
|
||
data_to_save = dumps({"data": item['months']})
|
||
prediction_result = AccountRecordsPredict.query.filter_by(account_records_id=account_record_to_save.id, prediction_field="months", prediction_model="regex").first()
|
||
if not prediction_result:
|
||
created_account_prediction = AccountRecordsPredict.create(**save_dict, prediction_field="months", prediction_result=data_to_save)
|
||
created_account_prediction.save()
|
||
else:
|
||
prediction_result.update(**update_dict, prediction_result=data_to_save)
|
||
prediction_result.save()
|
||
if years_are_valid:
|
||
print(f"years: {item['years']}")
|
||
data_to_save = dumps({"data": item['years']})
|
||
prediction_result = AccountRecordsPredict.query.filter_by(account_records_id=account_record_to_save.id, prediction_field="years", prediction_model="regex").first()
|
||
if not prediction_result:
|
||
created_account_prediction = AccountRecordsPredict.create(**save_dict, prediction_field="years", prediction_result=data_to_save)
|
||
created_account_prediction.save()
|
||
else:
|
||
prediction_result.update(**update_dict, prediction_result=data_to_save)
|
||
prediction_result.save()
|
||
if payment_types_are_valid:
|
||
print(f"payment_types: {item['payment_types']}")
|
||
data_to_save = dumps({"data": item['payment_types']})
|
||
prediction_result = AccountRecordsPredict.query.filter_by(account_records_id=account_record_to_save.id, prediction_field="payment_types", prediction_model="regex").first()
|
||
if not prediction_result:
|
||
created_account_prediction = AccountRecordsPredict.create(**save_dict, prediction_field="payment_types", prediction_result=data_to_save)
|
||
created_account_prediction.save()
|
||
else:
|
||
prediction_result.update(**update_dict, prediction_result=data_to_save)
|
||
prediction_result.save()
|
||
if apartment_number_are_valid:
|
||
print(f"apartment_number: {item['apartment_number']}")
|
||
prediction_result = AccountRecordsPredict.query.filter_by(account_records_id=account_record_to_save.id, prediction_field="apartment_number", prediction_model="regex").first()
|
||
if not prediction_result:
|
||
created_account_prediction = AccountRecordsPredict.create(**save_dict, prediction_field="apartment_number", prediction_result=item['apartment_number'])
|
||
created_account_prediction.save()
|
||
else:
|
||
prediction_result.update(**update_dict, prediction_result=item['apartment_number'])
|
||
prediction_result.save()
|
||
if person_name_are_valid:
|
||
print(f"person_name: {item['name_match']}")
|
||
data_to_save = dumps({"data": item['name_match']})
|
||
prediction_result = AccountRecordsPredict.query.filter_by(account_records_id=account_record_to_save.id, prediction_field="person_name", prediction_model="regex").first()
|
||
if not prediction_result:
|
||
created_account_prediction = AccountRecordsPredict.create(**save_dict, prediction_field="person_name", prediction_result=data_to_save)
|
||
created_account_prediction.save()
|
||
else:
|
||
prediction_result.update(**update_dict, prediction_result=data_to_save)
|
||
prediction_result.save()
|
||
|
||
print("\n===== SUMMARY =====")
|
||
print(f"extracted data total : {count_extracted}")
|
||
print(f"not extracted data total : {len(account_records) - count_extracted}")
|
||
print(f"Total account records processed : {len(account_records)}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
|
||
session_factory = get_session_factory()
|
||
session = session_factory()
|
||
|
||
turkish_months = ["OCAK", "ŞUBAT", "MART", "NİSAN", "MAYIS", "HAZİRAN", "TEMMUZ", "AĞUSTOS", "EYLÜL", "EKİM", "KASIM", "ARALIK"]
|
||
turkish_months_abbr = {
|
||
"OCA": "OCAK", "SUB": "ŞUBAT", "ŞUB": "ŞUBAT", "MAR": "MART", "NIS": "NİSAN", "MAY": "MAYIS", "HAZ": "HAZİRAN", "HZR": "HAZİRAN",
|
||
"TEM": "TEMMUZ", "AGU": "AĞUSTOS", "AGT": "AĞUSTOS", "EYL": "EYLÜL", "EKI": "EKİM", "KAS": "KASIM", "ARA": "ARALIK",
|
||
}
|
||
start_year = 1950
|
||
current_year = arrow.now().year
|
||
|
||
people_query = sqlalchemy_text("""
|
||
SELECT DISTINCT ON (p.id) p.firstname, p.middle_name, p.surname, p.birthname, bl.id
|
||
FROM public.people as p
|
||
INNER JOIN public.build_living_space as bl ON bl.person_id = p.id
|
||
INNER JOIN public.build_parts as bp ON bp.id = bl.build_parts_id
|
||
INNER JOIN public.build as b ON b.id = bp.build_id
|
||
WHERE b.id = 1
|
||
ORDER BY p.id
|
||
""")
|
||
|
||
people_raw = session.execute(people_query).all()
|
||
remove_duplicate = list()
|
||
clean_people_list = list()
|
||
for person in people_raw:
|
||
merged_name = f"{person[0]} {person[1]} {person[2]} {person[3]}"
|
||
if merged_name not in remove_duplicate:
|
||
clean_people_list.append(person)
|
||
remove_duplicate.append(merged_name)
|
||
|
||
people = [{"firstname": p[0], "middle_name": p[1], "surname": p[2], "birthname": p[3], 'id': p[4]} for p in clean_people_list]
|
||
query_account_records = sqlalchemy_text("""
|
||
SELECT a.id, a.iban, a.bank_date, a.process_comment FROM public.account_records as a where currency_value > 0
|
||
""") # and bank_date::date >= '2020-01-01'
|
||
account_records = session.execute(query_account_records).all()
|
||
account_records = [{"id": ar[0], "iban": ar[1], "bank_date": ar[2], "process_comment": ar[3]} for ar in account_records]
|
||
|
||
try:
|
||
main(session=session, account_records=account_records, people=people)
|
||
except Exception as e:
|
||
print(f"{e}")
|
||
|
||
session.close()
|
||
session_factory.remove()
|