Placement Portal

A comprehensive, full-stack Placement Portal designed to manage and streamline the campus placement process. The project is divided into three main microservices: a modern React frontend, a robust Node.js backend, and a Python-based Machine Learning service for advanced features such as resume parsing and candidate evaluation.

🌐 Live Deployment

You can access the fully deployed live application here: 🔗 https://placement-portal-frontend-x18d.onrender.com/

🚀 Project Structure

The repository is organized into a monorepo-like structure with three main directories:

• /Frontend: The user interface built with React, Vite, and Tailwind CSS.
• /Backend: The core REST API built with Node.js, Express, and PostgreSQL.
• /mlService: A Python Flask service for machine learning tasks.

🛠️ Tech Stack

Frontend

• React 19 with Vite
• Tailwind CSS v4 for styling
• React Router DOM for navigation
• Firebase for authentication/client-side integrations
• Lucide React for icons
• Axios for API calls

Backend

• Node.js & Express.js
• PostgreSQL (pg) for the relational database
• Firebase Admin for secure backend authentication
• Cloudinary for cloud media/resume storage
• Multer for file handling
• Nodemailer for email notifications

ML Service

• Python with Flask
• Scikit-Learn & Numpy for machine learning algorithms
• PyPDF2 for PDF parsing (e.g., resume processing)
• Gunicorn for WSGI HTTP Server

⚙️ Prerequisites

Before you begin, ensure you have the following installed:

• Node.js (v16 or higher recommended)
• Python 3.x
• PostgreSQL

📦 Installation

1. Clone the repository:

   git clone <repository-url>
   cd Placement-Portal

2. Install Node dependencies for both Frontend and Backend: From the root directory, run:

   npm run install:all

   (This uses the root package.json script to run npm install in both the frontend and backend directories)

3. Install Python dependencies for the ML Service:

   cd mlService
   python -m venv .venv
   source .venv/bin/activate  # On Windows use: .venv\Scripts\activate
   pip install -r requirements.txt
   cd ..

🔐 Environment Variables

You will need to configure environment variables for both the Backend and Frontend to run correctly.

Backend (/Backend/.env): Typical variables required:

• PostgreSQL Database credentials
• Firebase Admin SDK credentials
• Cloudinary API keys
• Nodemailer SMTP credentials

Frontend (/Frontend/.env): Typical variables required:

• Backend API base URL
• Firebase client configuration

ML Service (/mlService/.env - if applicable):

• Flask and model configuration variables

🚀 Running the Application

This project uses concurrently to run all three services simultaneously from the root directory.

Note: Ensure your Python virtual environment inside mlService is created and named .venv as the root dev script expects this exact path.

To start the Frontend, Backend, and ML Service together, run the following from the root directory:

npm run dev

This single command will:

1. Start the Node.js backend development server (nodemon index.js).
2. Start the Vite frontend development server.
3. Activate the Python virtual environment and start the Flask ML server (python3 mlService/mlServer.py).

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

📄 License

This project is open-source and available under the MIT License.