LifeManager 1.0.7

A simple Python package

❓ Why I Made LM (LifeManager)

I often found myself losing track of time—hours would pass without a clear sense of what I had actually done, And I had this time slippage in my life; So I decided to use my programming skills to build a Python package that helps me monitor and manage my day with greater precision.

LifeManager (LM) not only helps me track how I spend my time, but it also includes both a Local UI interface and a Telegram bot, so I can log and manage my activities from anywhere.

What is LifeManager?

LifeManager is a program designed to help you track your daily tasks and monitor your banking expenses. It provides detailed reports on what you have accomplished and how much you have spent in various categories.

Additionally, LifeManager can generate charts and export data to Excel files, helping you visualize and analyze your time and money management more effectively.

How to set it up

First You need to run:

pip install LifeManager

Then Its time to set PostgreSQL Username,Password,Host,port :(If you know how to work with .env file, you can set it manually):

from LifeManager.config import Config

cfg = Config()

cfg.change_PostgreSQL_user("your_username")
cfg.change_PostgreSQL_password("your_password")
cfg.change_PostgreSQL_host("your_host")   # e.g. "localhost"
cfg.change_PostgreSQL_port(5432) # or your custom port

🤖 How to use Telegram BOT

If you don't want to work with the CLI version and just simply work with the a telegram bot,

• first activate its flag with

from LifeManager.config import Config

cfg = Config()

cfg.change_telegram_bot_status() # Remember That every time you run this you turn it on/off

• Then, you have to provide a valid TELEGRAM BOT TOKEN(You can fetch it from BotFather ) using:

from LifeManager.config import Config

cfg = Config()

cfg.change_telegram_TOKEN(token="TELEGRAM_TOKEN")
# Remember if you enter invalid token, you get an error when you want to start the bot not here!!

• Now you simply can start/stop the bot with start/stop methods:

import asyncio
from LifeManager.telegram_launcher import TelegramLauncher

async def main():
    launcher = TelegramLauncher()
    
    # Start the bot (checks flags, validates token, launches subprocess)
    started = await launcher.start()
    if not started:
        return

    # Keep the script running until interrupted
    try:
        print("Bot is running. Press Ctrl+C to stop.")
        await asyncio.Event().wait()
    except KeyboardInterrupt:
        pass
    finally:
        # Stop the bot subprocess gracefully
        await launcher.stop()

if __name__ == "__main__":
    asyncio.run(main())

Your Done! Enjoy ....

🖥️ How to Use the UI

To launch the graphical interface, follow these steps:

1. Import the launcher:

   from LifeManager.UI_launcher import UILauncher
   

2. Create a UI instance with a desired port:

   ui = UILauncher()
   

   ⚠️ This Uses you 8569 port. If you want this port or an error occurred use ui.kill_port_8569() to kill the port and free it up.

3. Start or stop the interface:

   ui.start()  # Launches the UI
   ui.stop()   # Stops the UI
   

How to use it raw

In LifeManager Package we have several modules and one sub-package(telegramBOT) :

Module Name	Description
BM	Controls Banking Section
LM	Controls Main Task Section
TM	Controls Time Object
config	Configs for run the package
Cursor	A postgreSQL cursor
logger_config	A logger file
telegram_launcher	Validate and launches telegram sub-package

Now I will Explain each module in depth.

BM Module

The BM module provides a comprehensive class CBanker designed to manage banking data and transactions efficiently. It handles the creation and management of banking tables, allows you to add banks and expense types, make transactions, and retrieve bank records with ease.

Key Features:

• Table Management: Automatically creates and manages necessary database tables related to banks, bankers, and expense types.
• Bank Management: Add new banks and retrieve bank details seamlessly.
• Expense Types: Organize and add expense types with support for parent-child hierarchies.
• Transactions: Record transactions with detailed information including bank name, amount, expense type, and optional descriptions.
• Data Retrieval: Fetch transaction records between specified dates and export them to Excel for analysis.
• Analytics: Generate simple charts for recent transaction data (e.g., last 30 days).
• Bank Initialization: Retrieve the first recorded transaction date for any bank.

Usage Overview:

from LifeManager.BM import CBanker

banker = CBanker()

# Create necessary tables (banks, bankers, expense types)
banker.make_tables()

# Add a new bank
banker.add_bank("MyBank")

# Add an expense type (optionally under a parent category)
banker.add_expense("Utilities")

# Make a transaction
banker.make_transaction(
    bank_name="MyBank",
    amount=150.0,
    expense_type="Utilities",
    description="Electricity bill"
)

# Fetch transactions between dates for a bank and export to Excel
banker.fetch_records("MyBank", "2025-01-01", "2025-01-31")

# Show all banks
banker.show_all_banks()

# Get the date of the first transaction for a bank
first_date = banker.bank_first_init_time("MyBank")
print(f"First transaction date: {first_date}")

LM Module

The LM module provides the LifeManager class, a robust solution for personal productivity and time-tracking built on PostgreSQL. It enables you to create and manage daily and weekly task tables, track time spent on tasks, backup and restore your database, and visualize your productivity with charts.

Key Features:

• Database Initialization:

  • make_psql_db(): Create a PostgreSQL database using your .env PSQ_* settings.
  • _create_daily_tasks_table(): Create the master daily tasks table.
  • make_weekly_tables(): Dynamically create a table for the current year & week (e.g., y2025w21).

• Task Management:

  • add_daily_task(task_name, ref_to=None): Add a parent task or subtask (if ref_to is provided).
  • get_all_parent_tasks(): List all parent (top-level) tasks.
  • fetch_all_non_parent_tasks(): List all subtasks.
  • fetch_task_id(task_name): Retrieve the database ID for any task.
  • fetch_child_tasks_of(parent_task_name): Get all subtasks under a given parent.

• Time Tracking:

  • insert_into_weekly_table(duration, task_id, description=None): Log minutes spent on a task in the current week’s table.
  • timer(): Start a timer session and return its UUID.

• Backup & Restore:

  • backup(): Dump the entire database into a timestamped backup folder.
  • restore_backup(backup_path="latest"): Restore from a specific backup file or the most recent one.

• Data Retrieval & Analysis:

  • fetch_all_rows(week=None) → pd.DataFrame: Load all records from a specific weekly table into a pandas DataFrame.
  • show_all_tables(schema="public", table_type="BASE TABLE"): List all tables in your database schema.

• Visualization:

  • chart_it(week=None, start_day="Saturday"): Produce three charts saved in figures/:
    1. Pie chart of task-duration distribution for the selected week
    2. Horizontal bar chart of daily total durations (with custom start day)
    3. Line chart of total weekly durations over time

• Cursor Pooling (Internal):

  • __cursor(): Context-managed cursor pool for safe, efficient DB access.

• Banker Integration:

  • @property bank: Instantiates a CBanker object for integrated banking features.

Usage Overview

from LifeManager.LM import LifeManager

# Initialize manager with default connection pool settings
lm = LifeManager(minconn=1, maxconn=5)

# 1. Setup database and tables
lm.make_psql_db()
lm._create_daily_tasks_table()
lm.make_weekly_tables()

# 2. Add tasks
lm.add_daily_task("Health")
lm.add_daily_task("Morning Run", ref_to="Health")

# 3. Log time
task_id = lm.fetch_task_id("Morning Run")
lm.insert_into_weekly_table(duration=45, task_id=task_id, description="Park jog")

# 4. View tasks
print("Parents:", lm.get_all_parent_tasks())
print("Subtasks:", lm.fetch_child_tasks_of("Health"))

# 5. Backup & Restore
lm.backup()
lm.restore_backup()

# 6. Fetch & analyze
df = lm.fetch_all_rows("y2025w21")
if isinstance(df, pd.DataFrame):
    print(df.head())

# 7. Generate charts
lm.chart_it(start_day="Monday")

# 8. Use banking features
if lm.bank:
    lm.bank.add_bank("MyFinance")

CTimer Module

The CTimer module provides the CTimer class, a versatile timer utility that lets you start, pause, resume, and stop multiple timers concurrently. Each timer is identified by a unique UUID and stored in a global registry for easy retrieval.

Key Features:

• Unique Timer Instances:

  • Each CTimer() generates a UUID and registers itself in CTimer._instances.
  • Retrieve any timer later using CTimer.get_instance(uid).

• Basic Timing Operations:

  • start(): Begin timing.
  • end(): Stop timing (must have called start()).
  • time_it(): Calculate elapsed time in seconds (automatically resumes if paused).

• Pause & Resume:

  • pause(): Temporarily halt the timer.
  • resume(): Continue timing, automatically accounting for pause durations.

• Logging:

  • Automatically logs creation of new timer instances via the configured logger.

• UUID Management:

  • get_uid(): Retrieve the UUID for later reference.
  • CTimer._instances: Class-level dict of all active timers.

Usage Overview

from LifeManager.TM import CTimer  # adjust import path as needed

# Create a new timer
timer = CTimer()
uid = timer.get_uid()

# Start timing
timer.start()

# ... perform some operations ...
timer.pause()
# ... during pause, code runs without being counted ...
timer.resume()

# Stop timing and get elapsed seconds
timer.end()
elapsed = timer.time_it()
print(f"Elapsed time: {elapsed} seconds")

# Retrieve the same timer later using its UUID
same_timer = CTimer.get_instance(uid)
assert same_timer is timer

Config Module

The Config module provides the Config class to manage application settings for LifeManager. It handles reading and writing a config.ini file, safely updating environment variables in a .env file, and toggling Telegram and PostgreSQL credentials.

Key Features:

• Config File Management

  • _load_config(): Load config.ini or create it with sensible defaults (telegram, backup, postgresql sections).
  • Reads and writes values to config.ini automatically.

• Telegram Bot Configuration

  • change_telegram_bot_status(): Toggle the telegram.enabled flag in config.ini.
  • change_telegram_TOKEN(token): Safely set TELEGRAM_TOKEN in .env and mark telegram.token = true in config.ini.

• PostgreSQL Credentials

  • change_PostgreSQL_user(user_name), change_PostgreSQL_password(password),
    change_PostgreSQL_host(host), change_PostgreSQL_port(port):
    Update PGUSER, PGPASSWORD, PGHOST, PGPORT in .env and mirror user/host/port in config.ini.

• Environment Variable Handling

  • __set_env_variable(full_text): Add or update a single KEY=value line in the project’s .env file without disturbing other entries.

• Flag Retrieval

  • fetch_telegram_flags(): Return True only if both telegram.enabled and telegram.token are true.

Usage Overview

from LifeManager.config import Config

# Initialize (loads or creates config.ini + defaults)
cfg = Config("config.ini")

# Toggle Telegram bot on/off
enabled = cfg.change_telegram_bot_status()
print("Telegram enabled:", enabled)

# Set a new Telegram token
success = cfg.change_telegram_TOKEN("123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11")
print("Token update successful:", success)

# Update PostgreSQL credentials
cfg.change_PostgreSQL_user("dbuser")
cfg.change_PostgreSQL_password("s3cr3t")
cfg.change_PostgreSQL_host("localhost")
cfg.change_PostgreSQL_port(5432)

# Check if Telegram is fully configured
if cfg.fetch_telegram_flags():
    print("Telegram bot is configured and ready.")
else:
    print("Telegram bot is not yet configured.")

Cursor Module

The Cursor module provides the Cursor class, a thin wrapper around a PostgreSQL connection pool with context-managed cursors. It simplifies acquiring connections, executing queries, and handling transactions (commit/rollback) automatically.

Key Features:

• Connection Pooling

  • Uses psycopg2.pool.SimpleConnectionPool to maintain a pool of reusable database connections.
  • Configurable minimum and maximum connections (minconn, maxconn).

• Environment-Driven Configuration

  • Loads .env with python-dotenv to populate PGUSER, PGPASSWORD, PGHOST, and PGPORT.
  • Defaults to database name lifemanager (override by editing .env or code).

• Context-Managed Cursors

  • @contextmanager _cursor():
    • Acquires a connection and cursor.
    • Yields the cursor for query execution.
    • Commits on success or rolls back on exception.
    • Ensures cursor is closed and connection is returned to the pool.

• Logging

  • Exceptions during query execution are logged via the configured logger.

Usage Overview

from LifeManager.cursor import Cursor  # adjust import path if needed

# Initialize with a pool of 1–5 connections
db = Cursor(minconn=1, maxconn=5)

# Use the context-managed cursor for queries
with db._cursor() as cur:
    # Create a table
    cur.execute("""
        CREATE TABLE IF NOT EXISTS example (
            id SERIAL PRIMARY KEY,
            name TEXT NOT NULL
        )
    """)
    # Insert a row
    cur.execute("INSERT INTO example (name) VALUES (%s)", ("Alice",))

# Fetch data
with db._cursor() as cur:
    cur.execute("SELECT id, name FROM example")
    rows = cur.fetchall()
    for row in rows:
        print(row)

Logger Config Module

All logging functionality is centralized in the logger_config folder so every module writes to the same rotating log directory. The logger object provided here can be imported and used across your application.

Key Features:

• Shared Logger Instance

  • A single shared_logger is configured at DEBUG level.
  • Prevents duplicate handlers by checking if handlers already exist.

• Dynamic Log Files

  • Creates a log/ directory (if not already present).
  • Each run generates a new log file named with the current timestamp (DD-MM-YYYY--HH-MM-SS.log).

• Structured Formatting

  • Log entries use the format:

    2025-05-23 14:30:01,234 - INFO - Message text
    

Usage Overview

from LifeManager.logger_config import logger

# Log informational message
logger.info("Application started")

# Log debugging details
logger.debug("Debugging variable x = %s", x)

# Log warnings and errors
logger.warning("This is a warning")
logger.error("An error occurred", exc_info=True)

Telegram Launcher Module

The telegram_launcher module provides TelegramLauncher, an async helper to validate and launch your Telegram bot process using aiogram. It checks configuration flags, verifies the bot token, and manages the bot subprocess lifecycle.

Key Features:

• Flag & Token Validation

  • __check_flags(): Ensures telegram.enabled and telegram.token are set in config.ini.
  • __is_token_valid(token): Asynchronously verifies the token by calling Bot.get_me() via aiogram.

• Bot Launch & Shutdown

  • start():
    1. Reloads environment variables.
    2. Checks flags and presence of TELEGRAM_TOKEN in .env.
    3. Validates token with Telegram API.
    4. Spawns the bot subprocess (python -m LifeManager.telegram.telegram) on success.
  • stop(): Gracefully terminates the bot process, with timeout and forced kill fallback.

• Environment Loading

  • Uses python-dotenv to load/override .env variables each run.

• Logging & Console Feedback

  • Prints status messages to the console (print) and logs via shared logger.
  • Logs critical failures (missing flags/token), invalid tokens, and process lifecycle events.

Usage Overview

import asyncio
from LifeManager.telegram_launcher import TelegramLauncher

async def main():
    launcher = TelegramLauncher()

    # Start the bot (validates config and token)
    success = await launcher.start()
    if not success:
        return

    # ... bot is running as a separate process ...

    # To stop the bot later:
    await launcher.stop()

if __name__ == "__main__":
    asyncio.run(main())