Internship Recommendation System -

A full-stack web application to collect, manage, and recommend internships.

Contributing Guidelines

Thank you for your interest in contributing! Join our Discord Community 💜
This project welcomes developers of all skill levels, and this guide will help you get started smoothly.

To recognize the amazing people who help grow this project, we award badges : See here!

Prerequisites

Before contributing, please ensure you have:

• Git

• Node.js (version 18+) and npm

• Python 3 (version 3.8+) and pip

• A MongoDB Atlas account (you'll need a connection string).

• A Clerk account (you'll need a publishable key for the frontend).

If you're new to any of these, don’t worry -the steps below walk you through everything.

Setting up the Project Locally

The system has a separate frontend (React/Vite) and backend (Flask). Both require specific environment variables to run.

Fork The Repository

Click the Fork button at the top of the repository.
This creates your copy that you can safely experiment with.

Clone Your Fork

git clone https://github.com/traitor09/todo.git
cd todo

Create a new Branch

Always make changes in a separate branch instead of main.

git checkout -b feature/my-new-feature

Use clear branch names:

feature/... for new features
fix/... for bug fixes
docs/... for documentation
refactor/... for improvements

Backend Setup (Flask & MongoDB)

Navigate to the backend folder:

cd backend

Create the local configuration file:

cp .env.example .env

Open the newly created file and replace the placeholders with your actual credentials:

MONGO_CONNECTION_STRING: Your URI from MongoDB Atlas.
MONGO_DB_NAME: The name of the database you want to use (e.g., dev_db).

[Need your credentials? Detailed instructions on where to find your keys HERE ]

(Optional) It is highly recommended to use a Virtual Environment (venv) to keep project dependencies separate from your global Python installation:

• Linux/macOS

  python -m venv venv
  source venv/bin/activate

• Windows (PowerShell)

  python -m venv venv
  .\venv\Scripts\Activate.ps1

• Windows (CMD)

  python -m venv venv
  venv\Scripts\activate.bat

Install Python dependencies:

pip install -r requirements.txt

Set up the database:

python setup_database.py

Run the server:

python app.py

Verification: The terminal should show a message indicating the Flask server is running, usually at http://127.0.0.1:5000. Keep this terminal running!

Frontend

Navigate to the frontend directory:

cd frontend

Install dependencies:

npm install

Create the local configuration file:

cp .env.example .env

Open the file and replace the placeholders:

VITE_API_URL: This should point to your running backend: http://127.0.0.1:5000
VITE_CLERK_PUBLISHABLE_KEY: Your publishable key from the Clerk dashboard.
[Need your credentials? Detailed instructions on where to find your keys HERE ]

Run the Frontend:

npm run dev

Verification: The terminal will show a local URL, typically http://localhost:5173 (or another port). Open this URL in your browser to see the application!

Ensure

While contributing, please:

• Keep code clean and well-commented
• Follow existing coding style
• Test your changes locally
• Make commits small and meaningful

Example commit message:

Add skill preprocessing step to recommendation logic

Commit and Push

git add .
git commit -m "Clear and descriptive message"
git push origin feature/my-new-feature

Open a Pull Request

Go to your fork on GitHub. Click Compare & pull request. Write a clear description of what you changed and why. Submit the PR.

PR Tips:

• Be polite and collaborative
• Explain the reasoning behind your changes
• Screenshots are helpful if UI-related
• A maintainer will review your PR and give feedback or approve it.

STRUCTURE

Project Structure

• backend/

  • app.py – Main Flask application
  • setup_DB.py – Script to populate MongoDB
  • requirements.txt – Python dependencies
  • utils/
    • utils.py – MongoDB connection helper
    • preprocess.py – Data preprocessing functions
    • rule_recommendation.py – Rule-based recommendation logic
    • ml_recommendation.py – ML-based recommendation logic

• frontend/

  • package.json – NPM dependencies
  • tsconfig.json – TypeScript configuration
  • src/
    • App.tsx – Main React component
    • index.tsx – React entry point
    • components/ – React components folder

• Data/

  • dataset.json – Sample internship listings