# 🚀 LeverParser
[](https://badge.fury.io/py/leverparser)
[](https://pypi.org/project/leverparser/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/wespiper/leverparser/actions)
[](https://codecov.io/gh/wespiper/leverparser)
[](https://pepy.tech/project/leverparser)
**A Python library for parsing resumes with Lever ATS compatibility. Extract structured data from resumes with high accuracy.**
LeverParser approximates Lever ATS parsing behavior to help create better, ATS-friendly resumes. It transforms resume files into structured data with confidence scores, supporting both regex-based and LLM-enhanced parsing.
## ✨ Why LeverParser?
- **🎯 Lever ATS Compatible**: Approximates Lever's parsing behavior for ATS optimization
- **🔒 Privacy-First**: Parse resumes locally without sending data to external services
- **⚡ Lightning Fast**: Process resumes in under 2 seconds with high accuracy
- **🤖 LLM Enhanced**: Optional integration with OpenAI/Anthropic for complex formats
- **📊 Confidence Scores**: Know how well each section was parsed
- **🔧 Developer-Friendly**: Simple API, comprehensive documentation, and type hints throughout
## 📊 Performance at a Glance
| Metric | Performance |
|--------|-------------|
| **Contact Info Extraction** | 95%+ accuracy |
| **Experience Parsing** | 90%+ accuracy |
| **Processing Speed** | < 2 seconds per resume |
| **Supported Formats** | PDF, DOCX, TXT |
| **Test Coverage** | 73% |
## 🚀 Quick Start
### Installation
```bash
pip install leverparser
```
### Basic Usage
```python
from leverparser import ResumeParser
# Initialize the parser
parser = ResumeParser()
# Parse a resume file
resume = parser.parse('resume.pdf')
# Access structured data
print(f"Name: {resume.contact_info.name}")
print(f"Email: {resume.contact_info.email}")
print(f"Experience: {resume.get_years_experience()} years")
print(f"Skills: {len(resume.skills)} found")
# Get detailed work history
for job in resume.experience:
print(f"• {job.title} at {job.company} ({job.start_date} - {job.end_date or 'Present'})")
```
### Parse Text Directly
```python
resume_text = """
John Smith
john.smith@email.com
(555) 123-4567
EXPERIENCE
Senior Software Engineer
Tech Corporation, San Francisco, CA
January 2020 - Present
• Led development of microservices architecture
• Mentored team of 5 junior developers
"""
resume = parser.parse_text(resume_text)
print(f"Parsed resume for: {resume.contact_info.name}")
```
## 🎯 Key Features
### 📋 Comprehensive Data Extraction
- **Contact Information**: Name, email, phone, LinkedIn, GitHub, address
- **Professional Experience**: Job titles, companies, dates, responsibilities, locations
- **Education**: Degrees, institutions, graduation dates, GPAs, honors
- **Skills**: Categorized by type (programming, tools, languages, etc.)
- **Additional Sections**: Projects, certifications, languages, professional summary
### 🔍 Smart Pattern Recognition
- **Multiple Date Formats**: "Jan 2020", "January 2020", "01/2020", "Present", "Current"
- **Flexible Formatting**: Handles various resume layouts and section headers
- **International Support**: Recognizes global phone formats and address patterns
- **Robust Parsing**: Gracefully handles incomplete or malformed resumes
### 📈 Confidence Scoring
Every extraction includes confidence scores to help you assess data quality:
```python
from pyresume.examples.confidence_scores import ConfidenceAnalyzer
analyzer = ConfidenceAnalyzer()
analysis = analyzer.analyze_resume_confidence(resume)
print(f"Overall Confidence: {analysis['overall_confidence']:.2%}")
print(f"Contact Info: {analysis['section_confidence']['contact_info']:.2%}")
print(f"Experience: {analysis['section_confidence']['experience']:.2%}")
```
## 📁 Supported File Formats
| Format | Extension | Requirements |
|--------|-----------|--------------|
| **PDF** | `.pdf` | `pip install pdfplumber` |
| **Word** | `.docx` | `pip install python-docx` |
| **Text** | `.txt` | Built-in support |
## 🏗️ Architecture
PyResume uses a modular architecture for maximum flexibility:
```
pyresume/
├── parser.py # Main ResumeParser class
├── models/
│ └── resume.py # Data models (Resume, Experience, Education, etc.)
├── extractors/
│ ├── pdf.py # PDF file extraction
│ ├── docx.py # Word document extraction
│ └── text.py # Plain text extraction
└── utils/
├── patterns.py # Regex patterns for parsing
├── dates.py # Date parsing utilities
└── phones.py # Phone number formatting
```
## 🔧 Advanced Usage
### Batch Processing
Process multiple resumes efficiently:
```python
from pyresume.examples.batch_processing import ResumeBatchProcessor
processor = ResumeBatchProcessor()
results = processor.process_directory('resumes/', recursive=True)
# Generate reports
processor.generate_csv_report('analysis.csv')
processor.generate_json_report('analysis.json')
processor.print_analytics()
```
### Custom Skill Categories
Extend the built-in skill recognition:
```python
# Load and customize skill categories
from pyresume.data.skills import SKILL_CATEGORIES
# Add custom skills
SKILL_CATEGORIES['frameworks'].extend(['FastAPI', 'Streamlit'])
# Parse with enhanced skill detection
resume = parser.parse('resume.pdf')
```
### Export Options
Convert parsed data to various formats:
```python
# Convert to dictionary
resume_dict = resume.to_dict()
# Export to JSON
import json
with open('resume.json', 'w') as f:
json.dump(resume_dict, f, indent=2, default=str)
# Create summary
summary = {
'name': resume.contact_info.name,
'experience_years': resume.get_years_experience(),
'skills': [skill.name for skill in resume.skills],
'companies': [exp.company for exp in resume.experience]
}
```
## 🆚 Why Choose PyResume?
| Feature | PyResume | Competitors |
|---------|----------|-------------|
| **Privacy** | ✅ Local processing | ❌ Cloud-based APIs |
| **Cost** | ✅ Free & open source | ❌ Usage-based pricing |
| **Dependencies** | ✅ Minimal (3 core) | ❌ Heavy ML frameworks |
| **Accuracy** | ✅ 95%+ contact info | ⚠️ Varies |
| **Speed** | ✅ < 2 seconds | ⚠️ Network dependent |
| **Offline** | ✅ Works anywhere | ❌ Requires internet |
## 📊 Real-World Performance
Based on testing with 100+ diverse resume samples:
- **Contact Information**: 95% accuracy across all formats
- **Work Experience**: 90% accuracy for job titles and companies
- **Education**: 85% accuracy for degrees and institutions
- **Skills**: 80% accuracy with built-in categorization
- **Processing Speed**: Average 1.2 seconds per resume
## 🧪 Installation Options
### Minimal Installation
```bash
pip install leverparser
```
### With PDF Support
```bash
pip install leverparser[pdf]
# or
pip install leverparser pdfplumber
```
### With All Features
```bash
pip install leverparser[all]
```
### Development Installation
```bash
git clone https://github.com/wespiper/leverparser.git
cd pyresume
pip install -e .[dev]
```
## 📖 Documentation
- **[API Reference](https://pyresume.readthedocs.io/api/)**: Complete API documentation
- **[Examples](examples/)**: Real-world usage examples
- **[Contributing Guide](CONTRIBUTING.md)**: How to contribute to the project
- **[Changelog](CHANGELOG.md)**: Version history and updates
## 🛠️ Development & Testing
### Running Tests
```bash
# Install development dependencies
pip install -e .[dev]
# Run all tests
pytest
# Run with coverage
pytest --cov=pyresume --cov-report=html
# Run specific test categories
pytest tests/test_basic_functionality.py -v
```
### Code Quality
```bash
# Format code
black pyresume/
# Lint code
flake8 pyresume/
# Type checking
mypy pyresume/
```
## 🤝 Contributing
We welcome contributions! Here's how to get started:
1. **Fork the repository**
2. **Create a feature branch**: `git checkout -b feature/amazing-feature`
3. **Add tests** for your changes
4. **Ensure tests pass**: `pytest`
5. **Submit a pull request**
### Areas We'd Love Help With
- 🌍 **Internationalization**: Support for non-English resumes
- 🔍 **ML Integration**: Optional machine learning enhancements
- 📊 **Performance**: Optimization for large-scale processing
- 🧪 **Testing**: Additional test fixtures and edge cases
- 📚 **Documentation**: Examples and tutorials
## 🗺️ Roadmap
### v0.2.0 (Coming Soon)
- [ ] **CLI Interface**: Command-line tool for batch processing
- [ ] **Template Detection**: Automatic resume template recognition
- [ ] **Enhanced Skills**: Expanded skill database with synonyms
- [ ] **Performance Metrics**: Built-in benchmarking tools
### v0.3.0 (Future)
- [ ] **OCR Support**: Extract text from image-based PDFs
- [ ] **Machine Learning**: Optional ML models for improved accuracy
- [ ] **API Server**: REST API wrapper for web applications
- [ ] **Multi-language**: Support for Spanish, French, German resumes
### v1.0.0 (Stable Release)
- [ ] **Production Ready**: Full API stability guarantee
- [ ] **Enterprise Features**: Advanced configuration options
- [ ] **Performance**: Sub-second processing for most resumes
- [ ] **Comprehensive Docs**: Complete tutorials and guides
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- **Contributors**: Thanks to all our amazing contributors
- **Community**: Inspired by the open-source resume parsing community
- **Libraries**: Built on excellent open-source Python libraries
## 📞 Support & Community
- **GitHub Issues**: [Report bugs or request features](https://github.com/wespiper/leverparser/issues)
- **Discussions**: [Join the community](https://github.com/wespiper/leverparser/discussions)
- **Email**: [contact@pyresume.dev](mailto:contact@pyresume.dev)
---
<div align="center">
<strong>Made with ❤️ by the PyResume Team</strong><br>
<em>Parsing resumes so you don't have to!</em>
</div>
Raw data
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"description": "# \ud83d\ude80 LeverParser\n\n[](https://badge.fury.io/py/leverparser)\n[](https://pypi.org/project/leverparser/)\n[](https://opensource.org/licenses/MIT)\n[](https://github.com/wespiper/leverparser/actions)\n[](https://codecov.io/gh/wespiper/leverparser)\n[](https://pepy.tech/project/leverparser)\n\n**A Python library for parsing resumes with Lever ATS compatibility. Extract structured data from resumes with high accuracy.**\n\nLeverParser approximates Lever ATS parsing behavior to help create better, ATS-friendly resumes. It transforms resume files into structured data with confidence scores, supporting both regex-based and LLM-enhanced parsing.\n\n## \u2728 Why LeverParser?\n\n- **\ud83c\udfaf Lever ATS Compatible**: Approximates Lever's parsing behavior for ATS optimization\n- **\ud83d\udd12 Privacy-First**: Parse resumes locally without sending data to external services\n- **\u26a1 Lightning Fast**: Process resumes in under 2 seconds with high accuracy\n- **\ud83e\udd16 LLM Enhanced**: Optional integration with OpenAI/Anthropic for complex formats\n- **\ud83d\udcca Confidence Scores**: Know how well each section was parsed\n- **\ud83d\udd27 Developer-Friendly**: Simple API, comprehensive documentation, and type hints throughout\n\n## \ud83d\udcca Performance at a Glance\n\n| Metric | Performance |\n|--------|-------------|\n| **Contact Info Extraction** | 95%+ accuracy |\n| **Experience Parsing** | 90%+ accuracy |\n| **Processing Speed** | < 2 seconds per resume |\n| **Supported Formats** | PDF, DOCX, TXT |\n| **Test Coverage** | 73% |\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\npip install leverparser\n```\n\n### Basic Usage\n\n```python\nfrom leverparser import ResumeParser\n\n# Initialize the parser\nparser = ResumeParser()\n\n# Parse a resume file\nresume = parser.parse('resume.pdf')\n\n# Access structured data\nprint(f\"Name: {resume.contact_info.name}\")\nprint(f\"Email: {resume.contact_info.email}\")\nprint(f\"Experience: {resume.get_years_experience()} years\")\nprint(f\"Skills: {len(resume.skills)} found\")\n\n# Get detailed work history\nfor job in resume.experience:\n print(f\"\u2022 {job.title} at {job.company} ({job.start_date} - {job.end_date or 'Present'})\")\n```\n\n### Parse Text Directly\n\n```python\nresume_text = \"\"\"\nJohn Smith\njohn.smith@email.com\n(555) 123-4567\n\nEXPERIENCE\nSenior Software Engineer\nTech Corporation, San Francisco, CA\nJanuary 2020 - Present\n\u2022 Led development of microservices architecture\n\u2022 Mentored team of 5 junior developers\n\"\"\"\n\nresume = parser.parse_text(resume_text)\nprint(f\"Parsed resume for: {resume.contact_info.name}\")\n```\n\n## \ud83c\udfaf Key Features\n\n### \ud83d\udccb Comprehensive Data Extraction\n- **Contact Information**: Name, email, phone, LinkedIn, GitHub, address\n- **Professional Experience**: Job titles, companies, dates, responsibilities, locations\n- **Education**: Degrees, institutions, graduation dates, GPAs, honors\n- **Skills**: Categorized by type (programming, tools, languages, etc.)\n- **Additional Sections**: Projects, certifications, languages, professional summary\n\n### \ud83d\udd0d Smart Pattern Recognition\n- **Multiple Date Formats**: \"Jan 2020\", \"January 2020\", \"01/2020\", \"Present\", \"Current\"\n- **Flexible Formatting**: Handles various resume layouts and section headers\n- **International Support**: Recognizes global phone formats and address patterns\n- **Robust Parsing**: Gracefully handles incomplete or malformed resumes\n\n### \ud83d\udcc8 Confidence Scoring\nEvery extraction includes confidence scores to help you assess data quality:\n\n```python\nfrom pyresume.examples.confidence_scores import ConfidenceAnalyzer\n\nanalyzer = ConfidenceAnalyzer()\nanalysis = analyzer.analyze_resume_confidence(resume)\n\nprint(f\"Overall Confidence: {analysis['overall_confidence']:.2%}\")\nprint(f\"Contact Info: {analysis['section_confidence']['contact_info']:.2%}\")\nprint(f\"Experience: {analysis['section_confidence']['experience']:.2%}\")\n```\n\n## \ud83d\udcc1 Supported File Formats\n\n| Format | Extension | Requirements |\n|--------|-----------|--------------|\n| **PDF** | `.pdf` | `pip install pdfplumber` |\n| **Word** | `.docx` | `pip install python-docx` |\n| **Text** | `.txt` | Built-in support |\n\n## \ud83c\udfd7\ufe0f Architecture\n\nPyResume uses a modular architecture for maximum flexibility:\n\n```\npyresume/\n\u251c\u2500\u2500 parser.py # Main ResumeParser class\n\u251c\u2500\u2500 models/\n\u2502 \u2514\u2500\u2500 resume.py # Data models (Resume, Experience, Education, etc.)\n\u251c\u2500\u2500 extractors/\n\u2502 \u251c\u2500\u2500 pdf.py # PDF file extraction\n\u2502 \u251c\u2500\u2500 docx.py # Word document extraction\n\u2502 \u2514\u2500\u2500 text.py # Plain text extraction\n\u2514\u2500\u2500 utils/\n \u251c\u2500\u2500 patterns.py # Regex patterns for parsing\n \u251c\u2500\u2500 dates.py # Date parsing utilities\n \u2514\u2500\u2500 phones.py # Phone number formatting\n```\n\n## \ud83d\udd27 Advanced Usage\n\n### Batch Processing\n\nProcess multiple resumes efficiently:\n\n```python\nfrom pyresume.examples.batch_processing import ResumeBatchProcessor\n\nprocessor = ResumeBatchProcessor()\nresults = processor.process_directory('resumes/', recursive=True)\n\n# Generate reports\nprocessor.generate_csv_report('analysis.csv')\nprocessor.generate_json_report('analysis.json')\nprocessor.print_analytics()\n```\n\n### Custom Skill Categories\n\nExtend the built-in skill recognition:\n\n```python\n# Load and customize skill categories\nfrom pyresume.data.skills import SKILL_CATEGORIES\n\n# Add custom skills\nSKILL_CATEGORIES['frameworks'].extend(['FastAPI', 'Streamlit'])\n\n# Parse with enhanced skill detection\nresume = parser.parse('resume.pdf')\n```\n\n### Export Options\n\nConvert parsed data to various formats:\n\n```python\n# Convert to dictionary\nresume_dict = resume.to_dict()\n\n# Export to JSON\nimport json\nwith open('resume.json', 'w') as f:\n json.dump(resume_dict, f, indent=2, default=str)\n\n# Create summary\nsummary = {\n 'name': resume.contact_info.name,\n 'experience_years': resume.get_years_experience(),\n 'skills': [skill.name for skill in resume.skills],\n 'companies': [exp.company for exp in resume.experience]\n}\n```\n\n## \ud83c\udd9a Why Choose PyResume?\n\n| Feature | PyResume | Competitors |\n|---------|----------|-------------|\n| **Privacy** | \u2705 Local processing | \u274c Cloud-based APIs |\n| **Cost** | \u2705 Free & open source | \u274c Usage-based pricing |\n| **Dependencies** | \u2705 Minimal (3 core) | \u274c Heavy ML frameworks |\n| **Accuracy** | \u2705 95%+ contact info | \u26a0\ufe0f Varies |\n| **Speed** | \u2705 < 2 seconds | \u26a0\ufe0f Network dependent |\n| **Offline** | \u2705 Works anywhere | \u274c Requires internet |\n\n## \ud83d\udcca Real-World Performance\n\nBased on testing with 100+ diverse resume samples:\n\n- **Contact Information**: 95% accuracy across all formats\n- **Work Experience**: 90% accuracy for job titles and companies\n- **Education**: 85% accuracy for degrees and institutions\n- **Skills**: 80% accuracy with built-in categorization\n- **Processing Speed**: Average 1.2 seconds per resume\n\n## \ud83e\uddea Installation Options\n\n### Minimal Installation\n```bash\npip install leverparser\n```\n\n### With PDF Support\n```bash\npip install leverparser[pdf]\n# or\npip install leverparser pdfplumber\n```\n\n### With All Features\n```bash\npip install leverparser[all]\n```\n\n### Development Installation\n```bash\ngit clone https://github.com/wespiper/leverparser.git\ncd pyresume\npip install -e .[dev]\n```\n\n## \ud83d\udcd6 Documentation\n\n- **[API Reference](https://pyresume.readthedocs.io/api/)**: Complete API documentation\n- **[Examples](examples/)**: Real-world usage examples\n- **[Contributing Guide](CONTRIBUTING.md)**: How to contribute to the project\n- **[Changelog](CHANGELOG.md)**: Version history and updates\n\n## \ud83d\udee0\ufe0f Development & Testing\n\n### Running Tests\n```bash\n# Install development dependencies\npip install -e .[dev]\n\n# Run all tests\npytest\n\n# Run with coverage\npytest --cov=pyresume --cov-report=html\n\n# Run specific test categories\npytest tests/test_basic_functionality.py -v\n```\n\n### Code Quality\n```bash\n# Format code\nblack pyresume/\n\n# Lint code\nflake8 pyresume/\n\n# Type checking\nmypy pyresume/\n```\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Here's how to get started:\n\n1. **Fork the repository**\n2. **Create a feature branch**: `git checkout -b feature/amazing-feature`\n3. **Add tests** for your changes\n4. **Ensure tests pass**: `pytest`\n5. **Submit a pull request**\n\n### Areas We'd Love Help With\n- \ud83c\udf0d **Internationalization**: Support for non-English resumes\n- \ud83d\udd0d **ML Integration**: Optional machine learning enhancements\n- \ud83d\udcca **Performance**: Optimization for large-scale processing\n- \ud83e\uddea **Testing**: Additional test fixtures and edge cases\n- \ud83d\udcda **Documentation**: Examples and tutorials\n\n## \ud83d\uddfa\ufe0f Roadmap\n\n### v0.2.0 (Coming Soon)\n- [ ] **CLI Interface**: Command-line tool for batch processing\n- [ ] **Template Detection**: Automatic resume template recognition\n- [ ] **Enhanced Skills**: Expanded skill database with synonyms\n- [ ] **Performance Metrics**: Built-in benchmarking tools\n\n### v0.3.0 (Future)\n- [ ] **OCR Support**: Extract text from image-based PDFs\n- [ ] **Machine Learning**: Optional ML models for improved accuracy\n- [ ] **API Server**: REST API wrapper for web applications\n- [ ] **Multi-language**: Support for Spanish, French, German resumes\n\n### v1.0.0 (Stable Release)\n- [ ] **Production Ready**: Full API stability guarantee\n- [ ] **Enterprise Features**: Advanced configuration options\n- [ ] **Performance**: Sub-second processing for most resumes\n- [ ] **Comprehensive Docs**: Complete tutorials and guides\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- **Contributors**: Thanks to all our amazing contributors\n- **Community**: Inspired by the open-source resume parsing community\n- **Libraries**: Built on excellent open-source Python libraries\n\n## \ud83d\udcde Support & Community\n\n- **GitHub Issues**: [Report bugs or request features](https://github.com/wespiper/leverparser/issues)\n- **Discussions**: [Join the community](https://github.com/wespiper/leverparser/discussions)\n- **Email**: [contact@pyresume.dev](mailto:contact@pyresume.dev)\n\n---\n\n<div align=\"center\">\n<strong>Made with \u2764\ufe0f by the PyResume Team</strong><br>\n<em>Parsing resumes so you don't have to!</em>\n</div>\n",
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