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wag-managment-api-service-version-2
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app accounts updated

This commit is contained in:
berkay 2024-11-25 21:34:33 +03:00
parent a371d5d6e3
commit c525ac1117
4 changed files with 226 additions and 134 deletions
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141
api_services/bank_actions/wag_account_record_parser.py
View File

@ -16,6 +16,7 @@ from pydantic import BaseModel
from databases.sql_models.company.company import Companies
from databases.sql_models.identity.identity import People
from service_account_records.regex_func import category_finder
class InsertBudgetRecord(BaseModel):
@ -127,19 +128,22 @@ def remove_spaces_from_string(remove_string: str):
def get_garbage_words(comment: str, search_word: str):
garbage_words = remove_spaces_from_string(comment)
search_word = remove_spaces_from_string(search_word)
for letter in search_word.split(" "):
garbage_words = garbage_words.replace(remove_spaces_from_string(letter), "")
return str(remove_spaces_from_string(garbage_words)).upper()
garbage_words = unidecode(remove_spaces_from_string(comment))
search_word = unidecode(remove_spaces_from_string(search_word))
for word in search_word.split(" "):
garbage_words = garbage_words.replace(remove_spaces_from_string(unidecode(word)), "")
if cleaned_from_spaces := remove_spaces_from_string(garbage_words):
return str(unidecode(cleaned_from_spaces)).upper()
return None
def remove_garbage_words(comment: str, garbage_word: str):
cleaned_comment = remove_spaces_from_string(comment.replace("*", " "))
garbage_word = remove_spaces_from_string(garbage_word.replace("*", " "))
for letter in garbage_word.split(" "):
cleaned_comment = unidecode(remove_spaces_from_string(cleaned_comment))
cleaned_comment = cleaned_comment.replace(remove_spaces_from_string(letter), "")
if garbage_word:
garbage_word = remove_spaces_from_string(garbage_word.replace("*", " "))
for letter in garbage_word.split(" "):
cleaned_comment = unidecode(remove_spaces_from_string(cleaned_comment))
cleaned_comment = cleaned_comment.replace(remove_spaces_from_string(letter), "")
return str(remove_spaces_from_string(cleaned_comment)).upper()
@ -198,47 +202,67 @@ def parse_comment_for_living_space(
):
comment = unidecode(comment)
best_similarity = dict(
company=None, living_space=None, found_from=None, similarity=0.0, garbage=""
company=None, living_space=None, found_from=None, similarity=0.0, garbage="", cleaned=""
)
for person in living_space_dict[iban]["people"]:
person: People = person
first_name = unidecode(person.firstname).upper()
last_name = unidecode(person.surname).upper()
middle_name = unidecode(person.middle_name).upper()
search_word = f"{first_name} {last_name}"
if middle_name:
search_word = f"{first_name} {middle_name} {last_name}"
garbage_words = get_garbage_words(comment, search_word)
cleaned_comment = remove_garbage_words(comment, garbage_words)
similarity_ratio = textdistance.jaro_winkler(cleaned_comment, search_word)
if similarity_ratio > float(best_similarity["similarity"]):
for living_space in living_space_dict[iban]["living_space"]:
if living_space.person_id == person.id:
best_similarity = {
"company": None,
"living_space": living_space,
"found_from": "Person Name",
"similarity": similarity_ratio,
"garbage": garbage_words,
}
# print(
# 'cleaned_comment', cleaned_comment, '\n'
# 'search_word', search_word, '\n'
# 'best_similarity', best_similarity, '\n'
# 'person name', f"{first_name} {last_name}", '\n'
# 'similarity_ratio', similarity_ratio, '\n'
# 'garbage_words', garbage
# )
search_word_list = [
remove_spaces_from_string("".join([f"{first_name} {last_name}"])),
remove_spaces_from_string("".join([f"{last_name} {first_name}"]))
]
if middle_name := unidecode(person.middle_name).upper():
search_word_list.append(remove_spaces_from_string(f"{first_name} {middle_name} {last_name}"))
search_word_list.append(remove_spaces_from_string(f"{last_name} {middle_name} {first_name}"))
cleaned_comment = unidecode(comment).upper()
for search_word in search_word_list:
garbage_words = get_garbage_words(comment, unidecode(search_word))
if garbage_words:
garbage_words = unidecode(garbage_words).upper()
cleaned_comment = unidecode(remove_garbage_words(comment, garbage_words)).upper()
similarity_ratio = textdistance.jaro_winkler(cleaned_comment, str(search_word).upper())
if len(cleaned_comment) < len(f"{first_name}{last_name}"):
continue
if cleaned_comment and 0.9 < similarity_ratio <= 1:
print(
'cleaned comment dict', dict(
garbage=garbage_words, cleaned=cleaned_comment, similarity=similarity_ratio,
search_word=search_word, comment=comment, last_similarity=float(best_similarity["similarity"])
))
if similarity_ratio > float(best_similarity["similarity"]):
for living_space in living_space_dict[iban]["living_space"]:
if living_space.person_id == person.id:
best_similarity = {
"company": None,
"living_space": living_space,
"found_from": "Person Name",
"similarity": similarity_ratio,
"garbage": garbage_words,
"cleaned": cleaned_comment,
}
return best_similarity
def parse_comment_for_build_parts(comment: str, max_build_part: int = 200, parse: str = 'DAIRE'):
results, results_list = category_finder(comment), []
print('results[parse]', results[parse])
for result in results[parse] or []:
if digits := "".join([letter for letter in str(result) if letter.isdigit()]):
print('digits', digits)
if int(digits) <= int(max_build_part):
results_list.append(int(digits))
return results_list or None
def parse_comment_for_company_or_individual(comment: str):
companies_list = Companies.filter_all(
Companies.commercial_type != "Commercial", system=True
).data
comment = unidecode(comment)
best_similarity = dict(
company=None, living_space=None, found_from=None, similarity=0.0, garbage=""
company=None, living_space=None, found_from=None, similarity=0.0, garbage="", cleaned=""
)
for company in companies_list:
search_word = unidecode(company.public_name)
@ -252,6 +276,7 @@ def parse_comment_for_company_or_individual(comment: str):
"found_from": "Customer Public Name",
"similarity": similarity_ratio,
"garbage": garbage_words,
"cleaned": cleaned_comment,
}
# print(
# 'cleaned_comment', cleaned_comment, '\n'
@ -272,22 +297,57 @@ def parse_comment_to_split_with_star(account_record: AccountRecords):
return 1, account_record.process_comment
def check_build_living_space_matches_with_build_parts(
living_space_dict: dict, best_similarity: dict, iban: str, whole_comment: str
):
if 0.6 < float(best_similarity['similarity']) < 0.8:
build_parts = living_space_dict[iban]['build_parts']
if best_similarity['living_space']:
build_parts_id = best_similarity['living_space'].build_parts_id
parser_dict = dict(comment=str(whole_comment), max_build_part=len(build_parts))
print('build parts similarity', best_similarity, 'parser_dict', parser_dict)
results_list = parse_comment_for_build_parts(**parser_dict)
print('results_list', results_list)
if not results_list:
return best_similarity
for build_part in build_parts:
print('part_no', int(build_part.part_no), " | ", results_list)
print('build_part', int(build_part.id), int(build_parts_id))
print('cond', int(build_part.id) == int(build_parts_id))
print('cond2', int(build_part.part_no) in results_list)
if int(build_part.id) == int(build_parts_id) and int(build_part.part_no) in results_list:
similarity = float(best_similarity['similarity'])
best_similarity['similarity'] = (1 - similarity) / 2 + similarity
print('similarity', best_similarity['similarity'])
break
return best_similarity
def parse_comment_with_name(
account_record: AccountRecords, living_space_dict: dict = None
):
comments = parse_comment_to_split_with_star(account_record=account_record)
best_similarity = {"similarity": 0.0}
comments_list, comments_length = comments[1:], int(comments[0])
print('comments_list', comments_list, 'comments_length', comments_length)
if (
int(account_record.currency_value) > 0
): # Build receive money from living space people
if not comments_length > 1:
living_space_matches = dict(
living_space_dict=living_space_dict,
iban=account_record.iban,
whole_comment=account_record.process_comment
)
if comments_length == 1:
best_similarity = parse_comment_for_living_space(
iban=account_record.iban,
comment=comments_list[0],
living_space_dict=living_space_dict,
)
best_similarity["send_person_id"] = best_similarity.get("customer_id", None)
living_space_matches['best_similarity'] = best_similarity
# if 0.5 < float(best_similarity['similarity']) < 0.8
best_similarity = check_build_living_space_matches_with_build_parts(**living_space_matches)
return best_similarity
for comment in comments_list:
similarity_result = parse_comment_for_living_space(
@ -299,6 +359,10 @@ def parse_comment_with_name(
best_similarity["similarity"]
):
best_similarity = similarity_result
living_space_matches['best_similarity'] = best_similarity
# if 0.5 < float(best_similarity['similarity']) < 0.8:
best_similarity = check_build_living_space_matches_with_build_parts(**living_space_matches)
print('last best_similarity', best_similarity)
return best_similarity
else: # Build pays money for service taken from company or individual
if not comments_length > 1:
@ -323,7 +387,7 @@ def parse_comment_with_name_iban_description(account_record: AccountRecords):
BuildIbanDescription.iban == account_record.iban, system=True
).data
best_similarity = dict(
company=None, living_space=None, found_from=None, similarity=0.0, garbage=""
company=None, living_space=None, found_from=None, similarity=0.0, garbage="", cleaned=""
)
for comment in comments_list:
for iban_result in iban_results:
@ -341,6 +405,7 @@ def parse_comment_with_name_iban_description(account_record: AccountRecords):
"found_from": "Customer Public Name Description",
"similarity": similarity_ratio,
"garbage": garbage_words,
"cleaned": cleaned_comment,
}
return best_similarity
# print('account_record.process_comment', account_record.process_comment)

160
docker-compose.yml
View File

@ -2,8 +2,8 @@ services:
commercial_mongo_service:
container_name: commercial_mongo_service
# image: "bitnami/mongodb:latest"
image: "bitnami/mongodb:4.4.1-debian-10-r3"
image: "bitnami/mongodb:latest"
# image: "bitnami/mongodb:4.4.1-debian-10-r3"
networks:
- network_store_services
environment:
@ -65,85 +65,85 @@ services:
- wag_management_init_service
- grafana
wag_management_service_second:
container_name: wag_management_service_second
restart: on-failure
build:
context: .
dockerfile: service_app/Dockerfile
ports:
- "41576:41575"
networks:
- network_store_services
depends_on:
- wag_management_init_service
- grafana
# wag_management_service_second:
# container_name: wag_management_service_second
# restart: on-failure
# build:
# context: .
# dockerfile: service_app/Dockerfile
# ports:
# - "41576:41575"
# networks:
# - network_store_services
# depends_on:
# - wag_management_init_service
# - grafana
#
# wag_management_init_service:
# container_name: wag_management_init_service
# build:
# context: .
# dockerfile: service_app_init/Dockerfile
# networks:
# - network_store_services
# depends_on:
# - postgres_commercial
#
# wag_bank_services:
# container_name: wag_bank_services
# restart: on-failure
# build:
# context: .
# dockerfile: service_app_banks/mailService.Dockerfile
# networks:
# - network_store_services
# depends_on:
# - postgres_commercial
# environment:
# - DATABASE_URL=postgresql+psycopg2://berkay_wag_user:berkay_wag_user_password@postgres_commercial:5432/wag_database
wag_management_init_service:
container_name: wag_management_init_service
build:
context: .
dockerfile: service_app_init/Dockerfile
networks:
- network_store_services
depends_on:
- postgres_commercial
# wag_account_services:
# container_name: wag_account_services
# restart: on-failure
# build:
# context: .
# dockerfile: service_account_records/account.Dockerfile
# networks:
# - network_store_services
# depends_on:
# - postgres_commercial
# environment:
# - DATABASE_URL=postgresql+psycopg2://berkay_wag_user:berkay_wag_user_password@postgres_commercial:5432/wag_database
# - PYTHONPATH=/
wag_bank_services:
container_name: wag_bank_services
restart: on-failure
build:
context: .
dockerfile: service_app_banks/mailService.Dockerfile
networks:
- network_store_services
depends_on:
- postgres_commercial
environment:
- DATABASE_URL=postgresql+psycopg2://berkay_wag_user:berkay_wag_user_password@postgres_commercial:5432/wag_database
wag_account_services:
container_name: wag_account_services
restart: on-failure
build:
context: .
dockerfile: service_account_records/account.Dockerfile
networks:
- network_store_services
depends_on:
- postgres_commercial
environment:
- DATABASE_URL=postgresql+psycopg2://berkay_wag_user:berkay_wag_user_password@postgres_commercial:5432/wag_database
- PYTHONPATH=/
prometheus:
image: prom/prometheus
container_name: prometheus
ports:
- "9090:9090"
volumes:
- ./prometheus_data/prometheus.yml:/etc/prometheus/prometheus.yml
command:
- '--config.file=/etc/prometheus/prometheus.yml'
networks:
- network_store_services
grafana:
image: grafana/grafana
container_name: grafana
ports:
- "3000:3000"
depends_on:
- prometheus
networks:
- network_store_services
environment:
- GF_SECURITY_ADMIN_USER=admin
- GF_SECURITY_ADMIN_PASSWORD=admin
- GF_USERS_ALLOW_SIGN_UP=false
- GF_USERS_ALLOW_ORG_CREATE=false
volumes:
- grafana_data:/var/lib/grafana
# prometheus:
# image: prom/prometheus
# container_name: prometheus
# ports:
# - "9090:9090"
# volumes:
# - ./prometheus_data/prometheus.yml:/etc/prometheus/prometheus.yml
# command:
# - '--config.file=/etc/prometheus/prometheus.yml'
# networks:
# - network_store_services
#
# grafana:
# image: grafana/grafana
# container_name: grafana
# ports:
# - "3000:3000"
# depends_on:
# - prometheus
# networks:
# - network_store_services
# environment:
# - GF_SECURITY_ADMIN_USER=admin
# - GF_SECURITY_ADMIN_PASSWORD=admin
# - GF_USERS_ALLOW_SIGN_UP=false
# - GF_USERS_ALLOW_ORG_CREATE=false
# volumes:
# - grafana_data:/var/lib/grafana
# wag_management_test_service:
# container_name: wag_management_test_service
@ -175,8 +175,8 @@ networks:
network_store_services:
volumes:
wag_postgres_commercial_data:
grafana_data:
wag_postgres_commercial_data:
wag_commercial_mongodb_data:
# environment:

31
service_account_records/app_accounts.py
View File

@ -148,8 +148,10 @@ def account_get_people_and_living_space_info_via_iban() -> dict:
if living_space.person_id
]
people_list = People.filter_all(
People.id.in_(living_spaces_people), system=True
People.id.in_(living_spaces_people),
system=True
).data
print('build_parts', build_parts)
build_living_space_dict[str(account_records_iban[0])] = {
"people": list(people_list),
"living_space": list(living_spaces),
@ -160,11 +162,10 @@ def account_get_people_and_living_space_info_via_iban() -> dict:
def account_records_search():
build_living_space_dict = account_get_people_and_living_space_info_via_iban()
AccountRecords.filter_attr = account_list
AccountRecords.filter_attr, found_list = account_list, []
account_records_list: list[AccountRecords] = AccountRecords.filter_all(
AccountRecords.build_decision_book_id != None, system=True
# AccountRecords.build_decision_book_id != None, system=True
).data
found_list = []
for account_record in account_records_list:
similarity_result = parse_comment_with_name(
account_record=account_record, living_space_dict=build_living_space_dict
@ -172,9 +173,9 @@ def account_records_search():
fs, ac = similarity_result.get("similarity"), account_record.similarity or 0
if float(fs) >= 0.8 and float(fs) > float(ac):
found_list.append(similarity_result)
account_save_search_result(
account_record=account_record, similarity_result=similarity_result
)
# account_save_search_result(
# account_record=account_record, similarity_result=similarity_result
# )
else:
similarity_result = parse_comment_with_name_iban_description(
account_record=account_record
@ -182,9 +183,9 @@ def account_records_search():
fs, ac = similarity_result.get("similarity"), account_record.similarity or 0
if float(fs) >= 0.8 and float(fs) > float(ac):
found_list.append(similarity_result)
account_save_search_result(
account_record=account_record, similarity_result=similarity_result
)
# account_save_search_result(
# account_record=account_record, similarity_result=similarity_result
# )
print("Account Records Search : ", len(found_list), "/", len(account_records_list))
return
@ -309,9 +310,7 @@ def send_accounts_to_decision_payment():
AccountRecords.receive_debit == receive_enum.id,
).data
for account_record in account_records_list:
current_currency_value = pay_the_registration(
account_record, receive_enum, debit_enum
)
current_currency_value = pay_the_registration(account_record, receive_enum, debit_enum)
if current_currency_value > 0:
pay_the_registration(account_record, receive_enum, debit_enum, True)
if abs(float(Decimal(account_record.remainder_balance))) == abs(
@ -319,14 +318,14 @@ def send_accounts_to_decision_payment():
):
account_record.update(status_id=97)
account_record.save()
# todo If the payment is more than the amount, then create a new account record with the remaining amount
# # # todo If the payment is more than the amount, then create a new account record with the remaining amount
return
def account_records_service() -> None:
account_records_find_decision_book()
# account_records_find_decision_book()
account_records_search()
send_accounts_to_decision_payment()
# send_accounts_to_decision_payment()
return

28
service_account_records/regex_func.py Normal file
View File

@ -0,0 +1,28 @@
import re
from difflib import get_close_matches
categories = {
"DAIRE": ["daire", "dagire", "daare", "nolu daire", "no", "nolu dairenin"],
"APARTMAN": ["apartman", "aparman", "aprmn"],
"VILLA": ["villa", "vlla"],
"BINA": ["bina", "binna"]
}
def word_straighten(word, ref_list, threshold=0.8):
matches = get_close_matches(word, ref_list, n=1, cutoff=threshold)
return matches[0] if matches else word
def category_finder(text, output_template="{kategori} {numara}"):
result = {category: [] for category in categories} # Sonuçları depolamak için bir sözlük
for category, patterns in categories.items():
words = re.split(r'\W+', text)
straighten_words = [word_straighten(word, patterns) for word in words]
straighten_text = ' '.join(straighten_words)
pattern = r'(?:\b|\s|^)(?:' + '|'.join(map(re.escape, patterns)) + r')(?:\s*|:|\-|\#)*(\d+)(?:\b|$)'
if founds_list := re.findall(pattern, straighten_text, re.IGNORECASE):
list_of_output = [output_template.format(kategori=category, numara=num) for num in founds_list]
result[category].extend([i for i in list_of_output if str(i).replace(' ', '')])
return result