PTIT Library Management System

📌 Table of Contents

• About the Project
• Features
  • For Librarians
  • For Students
  • Advanced Features
• Technologies Used
• Getting Started
  • Backend Setup
  • Frontend Setup
  • Running the Application
• Contact

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📖 About the Project

This project is a Library Management System developed as a major assignment for my Python programming course. The system is designed to streamline the process of managing books, borrowers, and loans within a library environment, featuring advanced recommendation capabilities to enhance user experience.

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🚀 Features

For Librarians

• Book Management: Add, edit, delete, and view book details.
• User Management: Register and manage user accounts, including personal information and roles (e.g., librarian, student).
• Loan Management: Record book loans, returns, and overdue tracking.
• Statistics Dashboard: Comprehensive statistics and analytics for library operations, including book popularity, borrowing trends, etc.
• Book Search: Search for specific books by title, author, or other criteria.

For Students

• Book Browsing: View available books in the library catalog.
• Book Borrowing: Request and borrow books from the library.
• Borrowing History: Track personal borrowing history and current loans.
• Book Search: Search for specific books by title, author, or other criteria.
• Personalized Recommendations: Get book suggestions based on your borrowing history and preferences.

Advanced Features

Collaborative Filtering Recommendation System: The system implements an intelligent recommendation engine that uses collaborative filtering based on the k-Nearest Neighbors (k-NN) algorithm with cosine similarity as the distance metric. By analyzing users' borrowing history and finding similarities between users or books, the system suggests relevant titles that align with individual reading preferences.

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🛠 Technologies Used

Backend

• Programming Language: Python v3.10.11
• Web Framework: FastAPI
• Database: PostgreSQL
• Machine Learning: scikit-learn, pandas, numpy (for recommendation system)

Frontend

• Framework: React.js
• Runtime: Node.js v22.14
• Styling: CSS (with Bootstrap for basic styling)

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⚙️ Getting Started

Backend Setup

1. Clone the repository:

   git clone https://github.com/hoangpc0112/library-management.git
   cd library-management

2. Navigate to the backend directory:

   cd backend

3. Set up your virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

4. Install backend dependencies:

   pip install -r requirements.txt

Frontend Setup

1. Navigate to the frontend directory:

   cd ../frontend

2. Install frontend dependencies:

   npm install

Running the Application

Backend (FastAPI)

1. Navigate to the backend directory and activate virtual environment:

   cd backend
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

2. Start the FastAPI server:

   uvicorn main:app --reload

   The API will be available at http://localhost:8000
3. Access API Documentation:
   • Swagger UI: http://localhost:8000/docs
   • ReDoc: http://localhost:8000/redoc

Frontend (React)

1. In a new terminal, navigate to the frontend directory:

   cd frontend

2. Start the React development server:

   npm run dev

   The frontend will be available at http://localhost:3000

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📬 Contact

• Phạm Chính Hoàng - phamchinhhoang@gmail.com
• Project Link: https://github.com/hoangpc0112/library-management