Command-Line Expense Tracker 🐍💰

A robust, feature-rich, command-line interface (CLI) application for personal expense tracking, built with Python and MySQL. This project demonstrates a multi-layered application architecture, advanced database concepts, and data analysis capabilities in a non-web environment.

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✨ Key Features

• Multi-User System: Secure user registration and login with hashed passwords (bcrypt).
• Role-Based Access: Distinction between standard user and admin roles, with admins having special privileges.
• Full CRUD Functionality: Complete Create, Read, Update, and Delete operations for:
  • Accounts: Manage multiple financial accounts (Cash, Bank, Credit Card).
  • Categories: Create custom, nested income and expense categories.
  • Transactions: Add, edit, and delete detailed transactions.
  • Budgets: Set and update monthly budgets for different expense categories.
• Advanced Database Integration: Leverages MySQL features to ensure data integrity and performance:
  • Stored Procedures: For atomic transactions like posting a new expense.
  • Triggers: To automatically update account balances after any transaction change.
  • Functions: For reusable SQL logic.
• Data Analysis & Visualization:
  • Generates insightful reports on spending habits using the pandas library.
  • Creates and saves visualizations (bar charts, pie charts) using seaborn and matplotlib.
• CSV Import/Export: Easily export your transaction history to a CSV file or import new transactions from a CSV.
• Robust & Interactive CLI: A user-friendly, menu-driven interface built with helper functions and input validators.

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🏗️ Project Architecture

This application is built using a multi-layered architecture, which separates concerns and makes the codebase modular, scalable, and easy to test.

+---------------------------------+
|   Command-Line Interface (CLI)  |  <-- (main.py) - User Interaction
+---------------------------------+
                |
+---------------------------------+
|         Service Layer           |  <-- (services/) - Business Logic
+---------------------------------+
                |
+---------------------------------+
|        Repository Layer         |  <-- (repos/) - Data Access Logic
+---------------------------------+
                |
+---------------------------------+
|          Model Layer            |  <-- (models/) - Data Structures
+---------------------------------+
                |
+---------------------------------+
|       MySQL Database            |  <-- Stored Procs, Triggers, Functions
+---------------------------------+

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🛠️ Tech Stack

• Language: Python 3.10+
• Database: MySQL
• Core Libraries:
  • mysql-connector-python: For connecting to and interacting with the MySQL database.
  • bcrypt: For modern, secure password hashing.
  • python-dotenv: For managing environment variables (like database credentials).
• Data Science & Visualization:
  • pandas: For data manipulation and analysis.
  • seaborn & matplotlib: For creating and saving plots.
• CLI & Utilities:
  • tabulate: For printing clean, formatted tables in the console.

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🚀 How to Run the Project

Follow these steps to get the application running on your local machine.

Prerequisites

• Python 3.10 or newer
• A running MySQL Server instance
• Git for cloning the repository

Installation & Setup

1. Clone the Repository

   git clone [https://github.com/Shailya777/Expense-Tracker.git](https://github.com/your-username/your-repo-name.git)
   cd your-repo-name

2. Set Up a Virtual Environment

   # Create the virtual environment
   python -m venv venv
   
   # Activate it
   # On macOS/Linux:
   source venv/bin/activate
   # On Windows:
   venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

4. Configure the Database

   • Create a .env file in the root directory by copying the example file:

     cp .env.example .env

   • Open the .env file and fill in your MySQL credentials (DB_USER, DB_PASSWORD, etc.).

   • Log in to your MySQL client and run the provided SQL scripts in the following order to set up the database, tables, and sample data:

     1. sql/schema.sql
     2. sql/procs.sql
     3. sql/functions.sql
     4. sql/triggers.sql
     5. sql/final_seed.sql

5. Run the Application You're all set! Start the application with this command:

   python expense_tracker/main.py

   The CLI menu will appear, and you can start by registering a new user.

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🧠 What I Learned

This project was a fantastic learning experience that went beyond basic scripting. Here are some of the key concepts I mastered:

• Advanced SQL: I learned that a database is more than just a data store. By implementing Stored Procedures, Triggers, and Functions, I was able to enforce complex business rules and data integrity directly at the database level, making the application more robust and secure.

• Multi-Layered Architecture: I gained a deep appreciation for the Service and Repository patterns. Separating the application into distinct layers (UI, business logic, data access) made the code incredibly organized, easy to debug, and highly testable.

• Object-Oriented Programming (OOP) in Python:

  • I used Abstract Base Classes (abc) to define common interfaces for models like Account and Transaction, enforcing a consistent structure through inheritance.
  • The dataclasses module was a game-changer for creating clean, boilerplate-free model classes, making the code that represents data both readable and concise.

• Modern Python Features:

  • I extensively used the typing module for type hinting. This dramatically improved code clarity and allowed me to catch potential bugs early without even running the code.
  • I learned about secure authentication practices, using the bcrypt library to hash and verify passwords, ensuring user credentials are never stored in a readable format.

• Data Analysis Workflow: I implemented a complete data analysis pipeline: fetching raw data from the SQL database, loading it into a pandas DataFrame for powerful aggregation and manipulation, and finally, using seaborn to create meaningful visualizations that tell a story about user spending.

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Created by: Shailya Gandhi (https://www.linkedin.com/in/shailya-gandhi-b395a953/)