🚀 ApexHire - AI Resume Screener

An intelligent, end-to-end system that automatically reads, analyzes, scores, and ranks resumes based on job descriptions. Built with Python and Natural Language Processing (NLP), this system mimics what a human recruiter does—but faster and more consistently.

⚡ Quick Start (5 Minutes)

1. Setup

git clone https://github.com/ShashankBejjanki1241/ApexHire.git
cd ApexHire
python setup.py

2. Launch Web Interface

streamlit run app/main.py

3. Use the System

• Upload Resume: Go to "📄 Resume Analysis" and upload your resume
• Add Job: Go to "💼 Job Matching" and enter job details
• Get Results: View match scores and recommendations

🎯 Perfect For

Use Case	Description
Job Seekers	Test your resume against job descriptions
Recruiters	Screen multiple candidates efficiently
HR Teams	Standardize resume evaluation
Career Coaches	Help clients improve resumes

🛠️ Features

• 📄 Resume Parsing: Supports PDF, DOCX, and TXT formats
• 🧹 Text Preprocessing: Cleans and tokenizes text data
• 🧠 Skill Extraction: Extracts technical and soft skills
• 📊 Relevance Scoring: Matches resumes against job descriptions
• 🌐 Web Interface: Streamlit-based user interface
• 📈 Ranking System: Ranks candidates by relevance score
• 🔍 Semantic Analysis: Advanced NLP for better matching
• 📋 Detailed Reports: Comprehensive analysis output

🛠️ Tech Stack

• Python 3.8+
• spaCy - Natural Language Processing
• pdfplumber - PDF text extraction
• python-docx - DOCX file handling
• pandas & numpy - Data processing
• scikit-learn - Machine learning utilities
• Streamlit - Web application framework
• NLTK - Natural Language Toolkit

🚀 Usage Options

🌐 Web Interface (Recommended)

streamlit run app/main.py

Features:

• Easy-to-use dashboard
• Real-time analysis
• Interactive visualizations
• Step-by-step guidance

💻 Command Line

# Single file analysis
python cli.py --resume resume.pdf --job job.txt

# Batch processing
python cli.py --batch --resumes data/resumes/ --jobs data/jobs/

# Launch web interface
python cli.py --web

🔧 Programmatic API

from src.main_pipeline import ResumeScreener

screener = ResumeScreener()
result = screener.analyze_resume("resume.pdf")
match = screener.match_resume_to_job("resume.pdf", job_description)

📁 Project Structure

ApexHire/
├── 📁 src/                     # Core AI modules
│   ├── main_pipeline.py        # Main orchestration
│   ├── parser.py               # Resume parsing
│   ├── preprocess.py           # Text preprocessing
│   ├── scorer.py               # Scoring algorithms
│   ├── skills_extractor.py     # Skill extraction
│   ├── resume_analyzer.py      # Resume analysis
│   ├── performance_monitor.py  # Performance tracking
│   └── utils.py                # Utility functions
├── 📁 api/                     # FastAPI REST API
│   └── main.py                 # API endpoints
├── 📁 app/                     # Web interface
│   ├── main.py                 # Streamlit app
│   └── components/             # UI components
├── 📁 config/                  # Configuration
│   └── settings.py             # Centralized settings
├── 📁 docker/                  # Docker configuration
│   ├── Dockerfile              # Container definition
│   └── docker-compose.yml      # Multi-service setup
├── 📁 docs/                    # Documentation
│   ├── api/                    # API documentation
│   ├── deployment/             # Deployment guides
│   ├── development/            # Development guides
│   ├── USER_GUIDE.md          # User guide
│   └── API.md                 # API reference
├── 📁 tests/                   # Test suite
│   ├── test_parser.py          # Parser tests
│   ├── test_performance.py     # Performance tests
│   └── test_api.py            # API tests
├── 📁 data/                    # Sample data
│   ├── resumes/               # Resume files
│   └── job_descriptions/      # Job descriptions
├── 📁 output/                  # Analysis results
├── 📁 logs/                    # Application logs
├── 📄 cli.py                   # Command-line interface
├── 📄 requirements.txt         # Dependencies
├── 📄 setup.py                 # Installation script
└── 📄 README.md                # This file

📋 See PROJECT_STRUCTURE.md for detailed organization

📊 Example Results

🎯 RESUME ANALYSIS RESULTS
==========================================
📄 Resume: example_resume.pdf
📊 Text Length: 12,176 characters
🔧 Technical Skills: python, aws, sql, git, react
🤝 Soft Skills: agile, scrum, leadership

📊 JOB MATCH SCORES:
----------------------------------------
iOS Developer: 0.453 (45.3%)
Full Stack Developer: 0.447 (44.7%)
Python Developer: 0.470 (47.0%)

📋 File Requirements

Resume Files

• Formats: PDF, DOCX, TXT
• Size: Maximum 10MB
• Content: Readable text (not scanned images)
• Language: English (for best results)

Job Descriptions

• Format: Plain text (.txt)
• Content: Clear requirements and responsibilities
• Length: Detailed descriptions work better

🔧 Configuration

The system can be customized by modifying:

• Scoring weights in src/config/settings.py
• Skill lists in src/skills_extractor.py
• Text preprocessing in src/preprocess.py

📚 Documentation

• Quick Start Guide - Get started in 5 minutes
• Complete User Guide - Detailed instructions
• API Documentation - For developers
• Troubleshooting - Common issues

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

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

👨‍💻 Author

Shashank B

• GitHub: @ShashankBejjanki1241
• Project: ApexHire

🙏 Acknowledgments

• spaCy for excellent NLP capabilities
• pdfplumber for reliable PDF parsing
• python-docx for DOCX file handling
• pandas and numpy for data processing
• Streamlit for web interface framework

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