Procruit — Recruitment Platform

An integrated technical recruitment platform for Candidates, Recruiters, and Freelance Interviewers. Procruit features advanced AI-powered CV parsing, candidate ranking, and a robust AI Proctoring microservice to ensure interview integrity.

Tech Stack

Layer	Technology
Frontend	React 19, Vite, TailwindCSS
Backend	Node.js, Express 5, MongoDB Atlas
AI Service	Python, FastAPI, spaCy, SentenceTransformers, pdfplumber, scikit-learn
Proctoring Service	Python, FastAPI, WebSockets, OpenCV, MediaPipe, NumPy

Prerequisites

• Node.js: v18+
• Python: 3.10+
• npm: (ships with Node.js)

Local Setup Instructions

1. Install Root & Frontend Dependencies

npm install

2. Install Backend Dependencies

cd server
npm install
cd ..

3. Set Up the AI Service (Python & Ollama)

The AI Evaluation service uses an LLM. You must install Ollama locally to run the model.

1. Download and install Ollama from ollama.com
2. Pull the required model: ollama pull llama3 (or phi3 if your RAM is limited to 8GB)
3. Ensure the Ollama server is running (usually automatic on startup).

Create and activate a virtual environment, then install the Python dependencies:

cd ai-service
python -m venv venv

# Windows
.\venv\Scripts\pip install -r requirements.txt
.\venv\Scripts\python -m spacy download en_core_web_sm

# macOS/Linux
# source venv/bin/activate
# pip install -r requirements.txt
# python -m spacy download en_core_web_sm

cd ..

4. Set Up the Proctoring Service (Python)

Create and activate a virtual environment, then install the dependencies for real-time video/audio proctoring:

cd proctoring-service
python -m venv venv

# Windows
.\venv\Scripts\pip install -r requirements.txt

# macOS/Linux
# source venv/bin/activate
# pip install -r requirements.txt

cd ..

Note: Ensure the required MediaPipe model file (face_landmarker.task) is present in the root of the proctoring-service directory before running the application.

Required Environment Variables

You need to set up environment variables for the application to function correctly.

1. Backend (server/.env) Create a .env file inside the server/ directory:

PORT=5001
MONGO_URI=mongodb://<username>:<password>@<cluster-url>/procruit?...
STRIPE_SECRET_KEY=sk_test_...

2. Frontend (.env.local) Create a .env.local file inside the root directory:

GEMINI_API_KEY=your_gemini_api_key

Running the Application

From the project root, you can start all five services simultaneously with a single command:

npm run dev

This concurrently launches the following services (color-coded in terminal):

Service	Address / Port	Color	Description
Frontend (Vite)	http://localhost:3000	Cyan	Main React application
Backend (Express)	http://localhost:5001	Green	Core REST API and Socket.io server
AI Service (FastAPI)	http://localhost:8000	Magenta	CV parsing, candidate ranking, and AI interview evaluation (Ollama)
Proctoring Service (FastAPI)	http://localhost:8001	Yellow	WebSocket server for real-time video/gaze proctoring
Ollama	http://localhost:11434	Blue	Local LLM server (auto-starts, graceful skip if not installed)

Project Structure

procruit/
├── ai-service/          Python FastAPI microservice (CV Parsing & AI)
│   ├── main.py
│   ├── requirements.txt
│   └── venv/
├── proctoring-service/  Python FastAPI microservice (AI Proctoring & WebSockets)
│   ├── main.py
│   ├── requirements.txt
│   └── venv/
├── components/          React UI components
├── pages/               React page components
├── server/              Node.js/Express backend
│   ├── controllers/
│   ├── models/
│   ├── routes/
│   └── index.js
├── utils/               Shared frontend utilities
├── App.tsx              React app entry
├── vite.config.ts       Vite configuration (contains proxy settings for API/WS)
└── package.json         Root scripts (concurrent dev) & frontend deps

Contact Details

For any help or queries, You can contact us: usmannaeem350@gmail.com ahmedafzal25@gmail.com