FastAPI Resume Parser

A modern, high-performance resume parsing API built with FastAPI that extracts structured information from PDF resumes using advanced NLP techniques.

🌟 Features

• Advanced PDF Processing - Supports multiple PDF parsing methods (PyPDF, pdfminer.six)
• NLP-Powered Extraction - Uses spaCy for intelligent text analysis
• Comprehensive Data Extraction:
  • 📧 Personal Information (name, email, phone)
  • 🔗 Social Media Links (LinkedIn, GitHub)
  • 💼 Skills and Technologies
  • 🎓 Education Details
  • 🗺️ Location and Address Information
  • 🌍 Languages
• Modern FastAPI - Built with FastAPI 0.128.0 with automatic OpenAPI documentation
• Type-Safe - Comprehensive type hints throughout
• Production-Ready - Proper error handling, logging, and validation
• Maintainable Architecture - Clear API, service, extractor, schema, and domain layers
• Docker Support - Containerized for easy deployment
• AWS Lambda Ready - Configured for serverless deployment

🚀 Quick Start

Prerequisites

• Python 3.10+
• pip

Installation

1. Clone the repository

   git clone https://github.com/YOUR_USERNAME/fastapi_resume_parser.git
   cd fastapi_resume_parser

2. Create virtual environment

   python3.11 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Download required NLTK data

   python -c "import nltk; nltk.download('punkt_tab'); nltk.download('averaged_perceptron_tagger_eng'); nltk.download('maxent_ne_chunker_tab'); nltk.download('stopwords'); nltk.download('words')"

5. Run the server

   uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

6. Test the API

   Visit http://localhost:8000/docs for interactive API documentation

📖 API Documentation

Endpoints

Method	Endpoint	Description
GET	/	Root endpoint - API status
GET	/health	Health check endpoint
POST	/v1/resumes/parse	Parse resume and extract information
POST	/parse	Backward-compatible parse endpoint

Example Usage

Using cURL:

curl -X POST "http://localhost:8000/v1/resumes/parse" \
  -H "accept: application/json" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@resume.pdf"

Using Python:

import requests

url = "http://localhost:8000/v1/resumes/parse"
files = {"file": open("resume.pdf", "rb")}
response = requests.post(url, files=files)
print(response.json())

Response Format

{
  "status": "success",
  "filename": "resume.pdf",
  "personal_info": {
    "name": "John Doe",
    "email": ["john.doe@example.com"],
    "phone_number": "+1234567890"
  },
  "social_links": {
    "linkedin": "linkedin.com/in/johndoe",
    "github": "johndoe"
  },
  "skills": ["Python", "FastAPI", "Machine Learning"],
  "education_details": {
    "courses": ["B.Tech"],
    "specializations": ["Computer Science"],
    "college": ["University of Technology"]
  },
  "languages": ["English"],
  "processing_info": {
    "text_length": 1250,
    "tokens_processed": 320,
    "entities_found": 15
  }
}

🏗️ Project Structure

fastapi_resume_parser/
├── app/
│   ├── core/
│   │   ├── config.py          # Configuration management
│   │   ├── errors.py          # Application-specific exceptions
│   │   └── logging.py         # Logging setup
│   ├── api/
│   │   ├── dependencies.py    # FastAPI dependency factories
│   │   └── routes/            # HTTP route modules
│   ├── domain/
│   │   └── models.py          # Internal parser result models
│   ├── extractors/            # Focused resume field extractors
│   ├── resources/             # Local parsing vocabularies
│   ├── schemas/
│   │   └── responses.py       # Public API response models
│   ├── services/              # Parser orchestration and infrastructure services
│   ├── main.py                # FastAPI application
├── tests/
│   └── test_api.py            # API tests
├── requirements.txt           # Production dependencies
├── requirements-dev.txt       # Development dependencies
├── pyproject.toml             # Tool configurations
├── Dockerfile                 # Docker configuration
└── README.md                  # This file

🧪 Testing

Run the test suite:

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest

# Run tests with coverage
pytest --cov=app --cov-report=html

🛠️ Development

Code Quality

This project uses several tools to maintain code quality:

# Format code
black app/ tests/

# Sort imports
isort app/ tests/

# Lint code
flake8 app/ tests/

# Type checking
mypy app/

Pre-commit Hooks

Install pre-commit hooks to automatically check code quality:

pre-commit install

🐳 Docker

Build and Run

# Build the image
docker build -t fastapi-resume-parser .

# Run the container
docker run -p 8000:8000 fastapi-resume-parser

🚀 Deployment

Environment Variables

Create a .env file based on .env.example:

DEBUG=false
LOG_LEVEL=info
MAX_FILE_SIZE=10485760
CORS_ORIGINS=*
ALLOW_CREDENTIALS=false

Production Deployment

The app is ready to deploy as a standard ASGI service with Uvicorn, as a Docker container, or behind an API gateway using the included Mangum handler.

🤝 Contributing

Contributions are welcome! Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

📝 License

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

🙏 Acknowledgments

• FastAPI - Modern web framework
• spaCy - Industrial-strength NLP
• pdfminer.six - PDF text extraction

📧 Contact

For questions or support, please open an issue on GitHub.

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Made with ❤️ using FastAPI and Python