Expense Management System

A full-stack personal expense tracking system built using Streamlit (frontend), FastAPI (backend), and MySQL (database). This app allows you to add, view, delete, and analyze expenses with a user-friendly interface and dynamic charts.

---

Features

• Add multiple expenses per day with category and notes
• View and manage expenses by date
• Delete expenses (individually or in bulk)
• Dashboard with pie charts and bar graphs
• Monthly trend analysis with percent change from previous days
• Clean UI with reset and auto-refresh logic

---

Tech Stack

Layer	Tool
Frontend	Streamlit
Backend	FastAPI
Database	MySQL
Visualization	pandas, matplotlib

---

Folder Structure

project_expense_management/
│
├── Backend/
│   ├── server.py              # FastAPI main backend
│   ├── db_helper.py           # MySQL queries and DB logic
│   └── server.log             # Log output
│
├── Frontend/
│   ├── app.py                 # Streamlit main app
│   ├── insert.py              # Add expense tab
│   ├── view_manage.py         # View/Delete tab
│   ├── dashboard.py           # Dashboard analytics
│   ├── analytics.py           # API calls to analytics endpoint
│
├── requirements.txt           # Python dependencies
├── tests/                     # Test structure (pytest)
└── README.md                  # You're here

---

Setup Instructions

1. Clone the Repository

https://github.com/itsmoksh/Expense-Management-System.git
cd project_expense_management

2. Create Virtual Environment & Install Dependencies

pip install -r requirements.txt

3. Setup MySQL Database

• Create a database named expense_db (or your choice).
• Create the expenses table:

CREATE TABLE expenses (
  id INT AUTO_INCREMENT PRIMARY KEY,
  expense_date DATE,
  amount DECIMAL(10,2),
  category VARCHAR(50),
  notes TEXT
);

• Update your MySQL credentials in db_helper.py

---

Running the Project

1. Start FastAPI Backend

cd Backend
uvicorn server:app --reload

2. Start Streamlit Frontend

cd ../Frontend
streamlit run app.py

---

Dashboard Preview

• Pie chart: category-wise breakdown
• Bar chart: month-wise spending
• Metric: % change from previous day

---

TODO / Future Improvements

• Add user authentication
• Export filtered data as CSV/Excel

---

Author

Moksh Jain
LinkedIn
GitHub

---

License

MIT License – free to use and modify.