Collection of utility functions and classes designed to enhance Python projects. The library is organized into several modules, including logging, cache, translation models, client interactions, data manipulation with pandas, and general-purpose functions.
Python >= 3.10
Python < 3.10
You can install utils-b-infra using pip:
pip install utils-b-infraTo include the translation utilities:
pip install utils-b-infra[translation]The library is organized into the following modules:
- logging.py: Utilities for logging with SlackAPI and writing to a file.
- cache.py: Utilities for caching data in memory, Redis or MongoDB.
- ai.py: Utilities for working with AI models, such as token count, tokenization, and text generation.
- translation.py: Utilities for working with translation APIs (Supported Google Translate and DeepL).
- services.py: Services-related utilities, such as creating google service.
- pandas.py: Utilities for working with pandas dataframes, (df cleaning, insertion into databases...).
- generic.py: Miscellaneous utilities that don't fit into the other specific categories (retry, run in thread, validate, etc.).
Here are few examples, for more details, please refer to the docstrings in the source code.
Logging Utilities
from utils_b_infra.logging import SlackLogger
logger = SlackLogger(project_name="your-project-name", slack_token="your-slack-token", slack_channel_id="channel-id")
logger.info("This is an info message")
logger.error(exc=Exception, header_message="Header message appears above the exception message in the Slack message")Cache Utilities
from time import sleep
from utils_b_infra.cache import Cache, CacheConfig
cache_config = CacheConfig(
cache_type="RedisCache",
redis_host="host",
redis_port=6379,
redis_password="password"
)
@cache.cached(60, namespace="test1", sliding_expiration=False)
def hello(arg1: int, arg2: str) -> dict:
sleep(5)
data = {
"orders": [
"668abd233909666c44033913",
"668ab5167a0b54248b044b14",
"668aad6f1cd076a89e0f4e87",
"668ac1ff28065eadb408a9b5",
"668ac23eb6bb7b781f069567"
],
"stats": {
"1": 10,
"2": 22
}
}
print(data)
return data
if __name__ == "__main__":
hello(arg1=1, arg2="test")Services Utilities
from utils_b_infra.services import get_google_service
google_sheet_service = get_google_service(google_token_path='common/google_token.json',
google_credentials_path='common/google_credentials.json',
service_name='sheets')Pandas Utilities
import pandas as pd
from utils_b_infra.pandas import clean_dataframe, insert_df_into_db_in_chunks
from connections import sqlalchemy_client # Your database connection client
df = pd.read_csv("data.csv")
clean_df = clean_dataframe(df)
with sqlalchemy_client.connect() as db_connection:
insert_df_into_db_in_chunks(
df=clean_df,
table_name="table_name",
conn=db_connection,
if_exists='append',
truncate_table=True,
index=False,
dtype=None,
chunk_size=20_000
)Translation Utilities To use the translation utilities, you need to install the translation extras and set up the necessary environment variables for Google Translate:
pip install utils-b-infra[translation]import os
from utils_b_infra.translation import TextTranslator
# Set up Google Cloud credentials
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'path/to/google_service_account.json'
deepl_api_key = 'your-deepl-api-key'
languages = {
'ru': 'https://ru.example.com',
'ar': 'https://ar.example.com',
'de': 'https://de.example.com',
'es': 'https://es.example.com',
'fr': 'https://fr.example.com',
'uk': 'https://ua.example.com'
}
google_project_id = 'your-google-project-id'
translator = TextTranslator(deepl_api_key=deepl_api_key, languages=languages, google_project_id=google_project_id)
text_to_translate = "Hello, world!"
translations = translator.get_translations(
text=text_to_translate,
source_language="en",
target_langs=["ru", "ar", "de"],
engine="google"
)
for lang, translated_text in translations.items():
print(f"{lang}: {translated_text}")Generic Utilities
from utils_b_infra.generic import retry_with_timeout, validate_numeric_value, run_threaded, Timer
@retry_with_timeout(retries=3, timeout=5)
def fetch_data(arg1, arg2):
# function logic here
pass
with Timer() as t:
fetch_data("arg1", "arg2")
print(t.seconds_taken) # Output: Time taken to run fetch_data function (in seconds)
print(t.minutes_taken) # Output: Time taken to run fetch_data function (in minutes)
run_threaded(fetch_data, arg1="arg1", arg2="arg2")
is_valid = validate_numeric_value(123)
print(is_valid) # Output: TrueThis project is licensed under the MIT License. See the LICENSE file for details.
For all the changes and version history, see the CHANGELOG.