GECDahod Library System - Production Server

This is the Production branch. This branch is configured to run on your college's local network server for real-world use.

System Architecture

Here is the high-level architecture of the GECDahod Library System. It leverages Waitress as a production WSGI server and WhiteNoise for static files, making it a robust and self-contained solution for local network deployment.

graph TD
    %% User Interfaces
    Admin[Admin / Staff]
    Student[Student / Library User]
    Scanner[Kiosk Barcode Scanner]
    
    %% Middlewares and Routing
    Nginx[College Network Firewall / Intranet]
    Waitress[Waitress WSGI Server Port 800]
    
    %% Django Core
    subgraph Django Application
        DjangoRouter[URL Routing]
        WhiteNoise[WhiteNoise Static Files]
        Auth[JWT & Admin Auth]
        Views[Business Logic / Views]
        Reports[Reporting Engine Pandas]
        ORM[Django ORM]
    end

    %% Database & Storage
    DB[(SQLite Database)]
    Disk[File System Logs]

    %% Connections
    Student --> |View Dashboard| Nginx
    Admin --> |Access Admin Panel / Reports| Nginx
    Scanner --> |POST /kiosk Barcode| Nginx

    Nginx -.-> |HTTP Port 800| Waitress
    Waitress --> DjangoRouter
    DjangoRouter --> WhiteNoise
    DjangoRouter --> Auth
    Auth --> Views
    Views --> Reports
    Views --> ORM
    Reports -.-> |Generate .xlsx| ORM
    ORM <--> DB
    DB <--> Disk
    
    classDef ui fill:#4a90e2,stroke:#000,stroke-width:2px,color:#fff;
    classDef server fill:#f5a623,stroke:#000,stroke-width:2px,color:#fff;
    classDef app fill:#7ed321,stroke:#000,stroke-width:2px,color:#fff;
    classDef db fill:#9013fe,stroke:#000,stroke-width:2px,color:#fff;
    
    class Admin,Student,Scanner ui;
    class Nginx,Waitress server;
    class DjangoRouter,WhiteNoise,Auth,Views,Reports,ORM app;
    class DB,Disk db;

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Deployment Steps (College Server)

1. Clone the Production branch:

   git clone -b production https://github.com/PAVAN2005-LAB/GED_Dahod_library.git

2. Setup Environment:

   • Install dependencies: pip install -r requirements.txt
   • Create a .env file from .env.example.
   • Crucial: Set DJANGO_DEBUG=False and DJANGO_ALLOWED_HOSTS=*.

3. Prepare Static Files & Database:

   python manage.py makemigrations
   python manage.py migrate
   python manage.py collectstatic --noinput

4. Start the Server (Running on Port 800):

   python run_server.py

Accessing the System

Once the server is running, anyone on the college network can access it by visiting the server's IP address and port 800: http://[YOUR_SERVER_IP]:800 check your ip address by typing ipconfig in the command prompt.

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🐳 Docker Deployment (No Python Required)

If you don't have Python installed, or prefer deploying via containers, you can use Docker.

1. Build the Docker Image: Open a terminal in the project directory where the Dockerfile is located and run:

   docker build -t library-system .

2. Run the Container: Once built, start the server and map port 800:

   docker run -p 800:800 --name library-container library-system

The application is now containerized and accessible at http://localhost:800 (or your server's IP).

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Bulk Import Data (Students & Books)

You can easily bulk import students and books from standard .csv files using our custom management command import_data. This is built for fast ingestion while automatically checking for and skipping duplicate entries.

Import Students

Expected CSV Headers: enrollment_id, name, email, mobile_no, department

python manage.py import_data students /absolute/path/to/your/students.csv

Import Books

Expected CSV Headers: access_code, title, author, shelf_location
(Note: access_code maps to Book ID in the database)

python manage.py import_data books /absolute/path/to/your/books.csv

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API Endpoints Documentation

We provide a comprehensive REST API and dedicated Web endpoints for scanning, reports, and JWT authentication.

Click here to read the full API Documentation

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Key Production Features

• Waitress Server: Handles multiple users concurrently on Windows.
• WhiteNoise Middleware: Fast and efficient serving of CSS/JS/Images.
• Port 800: Standard access on port 800, freeing up default ports and bypassing standard admin restrictions for port 80.
• Pandas Reporting: Heavy data processing handled gracefully with Pandas generating .xlsx reports on the fly.

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Note: For development and code changes, please use the local branch.