AI-Powered Recruitment System

This is a Final-Year Computer Science project featuring a Semantic Recruitment System for automated CV screening, skill matching, and candidate ranking.

Features

• Semantic Matching: Uses Sentence-BERT (all-MiniLM-L6-v2) to understand the context of CVs and Job Descriptions beyond simple keywords.
• Section-Aware Parsing: Extracts text from PDF and DOCX files, identifying key sections like Work Experience and Skills.
• Explainable Ranking: Provides a final score (0.0 to 1.0) based on Semantic Match (80%) and Experience Weight (20%), along with a list of matched skills.
• 3NF Relational Database: PostgreSQL-compatible schema (tested with SQLite) ensuring data integrity and normalization.
• Modern Dashboard: A React-based recruiter interface for batch uploading CVs and viewing rankings.

Architecture

• Backend: FastAPI (Python)
• NLP Engine: spaCy (NER) & Sentence-Transformers (Embeddings)
• Frontend: React with Tailwind CSS v4
• Database: SQLAlchemy (3NF)

Setup Instructions

Backend

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   python -m spacy download en_core_web_sm

3. Set up the environment variables in a .env file (see .env.example):

   DATABASE_URL=sqlite:///./recruitment.db
   UPLOAD_DIR=./uploads
   

4. Initialize the database:

   python init_db.py

5. Start the API server:

   uvicorn main:app --reload

Frontend

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

Usage

1. Open the dashboard in your browser (usually http://localhost:5173).
2. Create a new Job Description.
3. Select the job and upload CVs in batch (PDF/DOCX/TXT).
4. Wait for the background processing to complete and refresh the rankings to see the results.