Cloth Store Management Project
This project is a cloth store management system developed using Python, MySQL, and Tkinter. It provides a user-friendly interface for managing customer data, product information, stock inventory, and billing operations. The project integrates with a MySQL database in MySQL Workbench to store and retrieve data efficiently.
Technologies Used
- Python: Programming language used for developing the project.
- MySQL: Relational database management system used for storing and managing data.
- Tkinter: Python library used for creating the graphical user interface.
Key Features
- Customer Management: Allows adding, searching, viewing, and updating customer details.
- Product Management: Enables adding, searching, viewing, and updating product information.
- Stock Management: Tracks and manages the stock inventory, including stock availability and quantity.
- Billing Operations: Provides functionality for generating bills and maintaining billing records.
Installation and Setup
- Clone the repository: https://github.com/kamathamkiran/Cloth-Store-Management-System.git
- Install the required dependencies:
pip install -r requirements.txt
- Create a MySQL database and import the provided SQL file.
- Update the database connection settings in the configuration file.
- Run the application:
python login.py
Usage
- Initially, Connect you project with your mysql workbench of your desktop.
- On running the login.py file, you will observe a option connect to database. Clicking on it, you will see a window asking for login credentials. On correct
details your database connection to mySQL workbench will be successful.
- So, after successful connection the main window will be displayed with various options for managing customers, products, stock, and billing.
- Use the respective menu options or buttons to perform CRUD operations on the database tables.
- The interface provides user-friendly forms and input fields to enter and update data.
- The data is stored and retrieved from the MySQL database, ensuring data integrity and efficient management.
Contribution
Contributions are welcome! If you find any bugs or have suggestions for improvements, please create an issue or submit a pull request.