AI Powered Resume Score Evaluator

A Streamlit application that evaluates how well a candidate's profile matches a job description by analyzing their resume and GitHub profile. This tool uses natural language processing (NLP) and semantic similarity to provide a comprehensive score based on the candidate's skills, experience, and projects.

Features

• Resume Analysis: Extracts skills, education, experience, and projects from PDF resumes using NLP
• GitHub Profile Analysis: Analyzes repositories, programming languages, stars, and activity level
• Job Description Matching: Compares extracted data to job requirements using semantic similarity
• Scoring System: Provides three scores:
  • Resume Score: Relevance of resume to job description
  • GitHub Score: Relevance and quality of GitHub projects
  • Final Fit Score: Combined evaluation of the candidate's fit
• Skills Gap Analysis: Identifies missing or weak areas in the candidate's profile

Installation

Prerequisites

• Python 3.11 or higher
• pip or uv package manager
• Git (for cloning the repository)
• A PDF resume file
• GitHub profile URL
• Job description text
• Internet connection (for GitHub API access)

Setup

1. Clone the repository:

   git clone https://github.com/muhammad-fiaz/resume-score.git
   cd resume-score
   

2. Create and activate a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt
   

   Or if using uv:

   uv pip install -e .
   

4. Download the spaCy model:

   python -m spacy download en_core_web_md
   

Usage

Run the Streamlit application:

streamlit run main.py

Dependencies

• streamlit: Web interface
• PyPDF2: PDF parsing
• spaCy: Natural language processing
• sentence-transformers: Semantic similarity
• PyGithub: GitHub API integration
• requests: HTTP requests

License

This project is licensed under the MIT License. See the LICENSE file for details.