Custom PC Build System

A terminal-based Python and MySQL project that helps users build compatible custom PCs by selecting components such as CPU, GPU, Motherboard, RAM, Storage, and PSU.

The project allows users to:

• Create custom PC builds
• View compatible hardware
• Store user builds in a database
• Manage records using admin access
• Calculate approximate total build cost

Note: Component prices and hardware data are based on 2024 market data.

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Features

User Features

• User Login & Signup System
• Continue as Guest
• Create Custom PC Builds
• Compatibility-based component selection
• Automatic PSU recommendation
• Total Price Calculation
• View Other Users' PC Builds

Admin Features

• Insert Records
• Update Records
• Display Records
• Search Records
• Delete Records

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Technologies Used

• Python
• MySQL
• mysql-connector-python
• Tabulate

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Project Structure

Custom-PC-Build-System/
│
├── main.py
├── components.sql
├── README.md
├── Output(s).pdf

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How to Run the Project

Step 1: Clone the Repository

git clone https://github.com/KunalSambyal/Custom-PC-Build.git

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Step 2: Install Required Python Libraries

pip install mysql-connector-python
pip install tabulate

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Step 3: Setup the Database

Import the provided SQL database file:

mysql -u root -p < components.sql

OR

1. Open MySQL Workbench
2. Open components.sql
3. Run the SQL script

This will automatically:

• Create the components database
• Create all required tables
• Insert sample hardware data

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Step 4: Configure MySQL Connection

Open main.py and update your MySQL credentials:

con = sql.connect(
    host="localhost",
    username="root",
    passwd="YOUR_PASSWORD",
    database="components"
)

Replace YOUR_PASSWORD with your MySQL password.

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Step 5: Run the Program

python main.py

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Project Modules

The project includes:

• User Authentication System
• Database CRUD Operations
• Hardware Compatibility Logic
• Dynamic Component Selection
• Admin Management System

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Screenshots & Output

Project execution screenshots and outputs are included in:

Output(s).pdf

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Future Improvements

• GUI version using Tkinter or PyQt
• Secure password hashing
• SQL injection prevention using parameterized queries
• Better exception handling
• Online deployment
• Expanded hardware database

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Author

Kunal Sambyal