# Smart Personal Finance Manager

This is a Python application that helps people manage their personal finances.
It was built to match the assignment requirements for the Smart Personal Finance Manager project.

## Features Implemented

- User account creation
- Login and logout system with a hashed password storage using `hashlib`
- Add income and expense transactions

- Transaction details include:
  - date
  - amount
  - category
  - description

- View transactions with filters:
  - by category
  - by date
  - by both category and date

- Remove transactions by transaction ID
- Set monthly budgets by category
- Budget warnings when spending is close to or above the limit
- Monthly and yearly reports

- Totals for:
  - income
  - expenses
  - savings
  - expenses by category

- Data saved in a JSON file
- Data automatically loaded when the program starts

- Error handling for:
  - invalid dates
  - invalid numbers
  - missing or corrupted data file

- Data visualization with `matplotlib`:
  - pie chart for spending distribution
  - line graph for income vs expenses over time
  - bar chart comparing categories

## Files Included

- `main.py` → main Python program
- `README.md` → instructions and project summary
- `finance_data.json` → created automatically when the program is used
- `reports/` → charts are saved here after reports are generated

## How to Run

1. Make sure Python 3 is installed.
2. Install the libraries below if you haven't already:
  - Matplotlib
  - Pandas
  - Json
  - OS
  - Datetime
  - tKinter
3. Run the program:

## Notes

- The program runs in the terminal.
- Passwords are stored as SHA-256 hashes instead of plain text.
- If the data file is damaged or missing, the program starts with empty data instead of crashing.

## Optional Features Completed

- Hashed passwords for better security

## Suggested Demo Flow

1. Create an account
2. Log in
3. Add a few income and expense transactions
4. Set a budget for one category
5. Add more expenses to trigger a budget alert
6. Generate a monthly or yearly report
7. Open the chart images from the `reports` folder