# ResumeParser Pro 🚀

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Production-ready AI-powered resume parser with parallel processing capabilities. Extract structured data from resumes in PDF, DOCX, and TXT formats using state-of-the-art language models.

## 🌟 Features

- **🤖 AI-Powered**: Uses advanced language models (GPT, Gemini, Claude, etc.)
- **⚡ Parallel Processing**: Process multiple resumes simultaneously
- **📊 Structured Output**: Returns clean, validated JSON data
- **🎯 High Accuracy**: Extracts 20+ fields with intelligent categorization
- **📈 Production Ready**: Robust error handling and logging
- **🔌 Easy Integration**: Simple API with just 3 lines of code

## 🚀 Quick Start

### Installation
pip install resumeparser-pro

For full functionality (recommended)
pip install resumeparser-pro[full]


### Basic Usage
from resumeparser_pro import ResumeParserPro

Initialize parser
parser = ResumeParserPro(
provider="google_genai",
model_name="gemini-2.0-flash",
api_key="your-api-key"
)

Parse single resume
result = parser.parse_resume("resume.pdf")
print(f"Name: {result.resume_data.contact_info.full_name}")
print(f"Experience: {result.resume_data.total_experience_months} months")



### Batch Processing

Process multiple resumes in parallel
file_paths = ["resume1.pdf", "resume2.docx", "resume3.pdf"]
results = parser.parse_batch(file_paths)

Get successful results
successful_resumes = parser.get_successful_resumes(results)
print(f"Parsed {len(successful_resumes)} resumes successfully")


## 📊 Extracted Data

ResumeParser Pro extracts **20+ structured fields**:

### Contact Information
- Full name, email, phone number
- Location, LinkedIn, GitHub, portfolio
- Other social profiles

### Professional Data
- Work experience with **integer month durations**
- Education with GPA standardization
- Skills categorized by type
- Projects with technologies and outcomes
- Certifications with dates and organizations

### Metadata
- Total experience in months
- Industry classification
- Seniority level assessment

## 🎯 Supported Models

| Provider | Models | Setup |
|----------|--------|-------|
| **Google** | Gemini 2.0 Flash, Gemini Pro | `provider="google_genai"` |
| **OpenAI** | GPT-4o, GPT-4o-mini | `provider="openai"` |
| **Anthropic** | Claude 3.5 Sonnet | `provider="anthropic"` |

## 📈 Performance

- **Speed**: ~3-5 seconds per resume
- **Parallel Processing**: 5-10x faster for batch operations
- **Accuracy**: 95%+ field extraction accuracy
- **File Support**: PDF, DOCX, TXT formats

## 🛠️ Advanced Features

### Custom Configuration

parser = ResumeParserPro(
provider="openai",
model_name="gpt-4o-mini",
api_key="your-api-key",
max_workers=10, # Parallel processing workers
temperature=0.1 # Model consistency
)


### Error Handling

results = parser.parse_batch(file_paths, include_failed=True)

Get processing summary
summary = parser.get_summary(results)
print(f"Success rate: {summary['success_rate']:.1f}%")
print(f"Failed files: {len(summary['failed_files'])}")


## 📋 Requirements

- Python 3.8+
- API key from supported provider
- Optional: PyMuPDF, python-docx for enhanced file support

## 🤝 Contributing

Contributions welcome! Please read our contributing guidelines.

## 📄 License

MIT License - see LICENSE file for details.

## 🆘 Support

- 📖 [Documentation](https://github.com/yourusername/resumeparser-pro/docs)
- 🐛 [Issue Tracker](https://github.com/yourusername/resumeparser-pro/issues)
- 💬 [Discussions](https://github.com/yourusername/resumeparser-pro/discussions)

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