AI Resume Screening System

Streamlit app that ranks resumes against a job description using sentence embeddings, then displays extracted candidate details.

Features

• Upload multiple resumes in PDF or image formats.
• Extract text using PDF parsing and OCR.
• Rank resumes by semantic similarity to a job description.
• Show extracted fields such as name, email, phone, education, and experience.

Tech Stack

• Python 3.10
• Streamlit
• spaCy (en_core_web_sm)
• sentence-transformers (all-MiniLM-L6-v2)
• PyPDF2
• pytesseract + Tesseract OCR

Prerequisites

1. Python 3.10+
2. Tesseract OCR installed on your machine:
   • Windows: install from the official project and add it to PATH.
   • macOS: brew install tesseract
   • Ubuntu/Debian: sudo apt install tesseract-ocr

If Tesseract is installed but not detected on Windows, set the executable path in code (example):

# Example only, uncomment and adjust the path if needed.
# pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

Installation

git clone https://github.com/rushikesh369/AI-resume-screening-system.git
cd AI-resume-screening-system

python -m venv .venv
# Windows PowerShell
.venv\Scripts\Activate.ps1

pip install --upgrade pip
pip install -r requirements.txt
python -m spacy download en_core_web_sm

Run

streamlit run resume_ranker.py

Notes

• Current bias/fairness output is a placeholder message and not a production fairness metric.
• For best OCR quality, use clear, high-resolution resume images.

Repository

• GitHub: https://github.com/rushikesh369/AI-resume-screening-system