sereena-parser 1.0.0

Production-ready resume parser with multi-threading support for PDF and DOCX files

Sereena Parser

Sereena Parser is a production-ready resume parser with multi-threading support that extracts structured information from PDF and DOCX files using advanced NLP techniques.

🚀 Features

• Multi-format Support: Parse PDF and DOCX files with high accuracy
• Advanced NLP: Uses spaCy for named entity recognition and text processing
• Multi-threading: Concurrent processing for batch operations
• Comprehensive Extraction:
  • Personal information (name, email, phone, location)
  • Skills and technologies
  • Education details
  • Work experience
  • Certifications
• Production Ready: Robust error handling, logging, and performance optimization
• Easy Integration: Simple API for seamless integration into existing systems

📦 Installation

pip install sereena-parser

Dependencies

After installation, you'll need to download the spaCy language model:

python -m spacy download en_core_web_sm

🛠️ Usage

Basic Usage

from sereena_parser import ResumeParser

# Initialize the parser
parser = ResumeParser()

# Parse a single resume
file_path, resume_data = parser.parse_single_resume("resume.pdf")

if resume_data:
    print(f"Name: {resume_data.personal_info.name}")
    print(f"Email: {resume_data.personal_info.email}")
    print(f"Skills: {resume_data.skills}")
    print(f"Experience: {len(resume_data.experience)} positions")

Batch Processing

from sereena_parser import ResumeParser

# Initialize parser with custom thread count
parser = ResumeParser(max_workers=8)

# Parse multiple resumes
resume_files = ["resume1.pdf", "resume2.docx", "resume3.pdf"]
results = parser.parse_multiple_resumes(resume_files)

# Export results to JSON
parser.export_results(results, "parsed_resumes.json")

Command Line Interface

# Parse single file
sereena-parser resume.pdf

# Parse multiple files with custom output
sereena-parser *.pdf *.docx -o results.json -w 8

# Verbose output
sereena-parser resume.pdf --verbose

📊 Data Structure

The parser returns structured data in the following format:

@dataclass
class ResumeData:
    personal_info: PersonalInfo     # Name, email, phone, location
    skills: List[str]               # Technical skills
    education: List[Education]      # Education history
    experience: List[Experience]    # Work experience
    certifications: List[str]       # Certifications
    languages: List[str]            # Languages
    summary: Optional[str]          # Brief summary
    raw_text: Optional[str]         # Raw extracted text

🎯 Key Components

PersonalInfo

• name: Extracted full name
• email: Validated email address
• phone: Phone number
• location: Geographic location

Education

• degree: Degree type and field
• institution: Educational institution
• year: Graduation year
• gpa: Grade point average (if available)

Experience

• job_title: Position title
• company: Company name
• duration: Employment duration
• description: Job description

🔧 Configuration

Custom Thread Count

# For heavy workloads
parser = ResumeParser(max_workers=16)

Logging Configuration

import logging
logging.getLogger('sereena_parser').setLevel(logging.DEBUG)

📈 Performance

• Multi-threading: Process multiple resumes concurrently
• Optimized Extraction: Smart text extraction with fallback methods
• Memory Efficient: Processes files without loading entire datasets into memory
• Speed: ~2-5 seconds per resume depending on complexity

🧪 Testing

# Install development dependencies
pip install sereena-parser[dev]

# Run tests
pytest tests/

📋 Requirements

• Python 3.8+
• spaCy English model (en_core_web_sm)
• PyPDF2, pdfplumber (PDF processing)
• python-docx (DOCX processing)
• pandas (data manipulation)
• email-validator (email validation)

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

📄 License

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

🛠️ Troubleshooting

Common Issues

1. spaCy model not found: Run python -m spacy download en_core_web_sm
2. PDF extraction issues: Install pdfplumber for better PDF support
3. Memory issues: Reduce max_workers for large batch processing

Support

For issues and questions, please create an issue on GitHub.

🔮 Roadmap

• Support for more file formats (TXT, RTF)
• Enhanced skill categorization
• Resume scoring and ranking
• Integration with popular HR systems
• Docker container support
• Web API endpoint

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Sereena Parser - Making resume parsing simple, fast, and reliable! 🎯

Replace YOUR_NEW_TOKEN_HERE with actual token

twine upload --repository-url https://test.pypi.org/legacy/
--username token
--password
pypi-AgEIcHlwaS5vcmcCJDZmMWQ5MWFjLTdkYTgtNDRiNi1iM2FhLWY2NThkMDUyODA0MwACKlszLCI3MzU3MTk3Mi0zOGZmLTRhMDUtYmNjZi1iMzhhMjA3YjVjYTAiXQAABiCouekgf6PHSlywlrWMoF0RZ0JuPZRRWq77SAEdbtkjfw
dist/*
--verbose