CV Parser/Anonymizer

A Python service for parsing and anonymizing CVs/resumes using small language models.

Project Overview

This project involves building a Python service that can parse and anonymize CVs/resumes using small language models. The service consists of two main components:

1. A REST API application with endpoints for parsing and anonymizing resumes
2. An implementation using the smolmodels library to create and utilize compact language models

Features

• Parse CVs/resumes in various formats (PDF, DOCX, TXT)
• Extract structured information including contact details, work experience, education, skills, etc.
• Anonymize personal information with configurable anonymization levels
• REST API for integration with other systems
• Small, efficient language models for parsing and anonymization

Installation

# Clone the repository
git clone https://github.com/fpardon-upeo/cv-parser.git
cd cv-parser

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Usage

# Start the API server
cd app
python main.py

The API will be available at http://localhost:8000

API Endpoints

• POST /parse - Extract structured information from a resume
• POST /anonymize - Create an anonymized version of a resume
• GET /health - Health check endpoint
• GET /version - Version information
• GET /docs - API documentation (Swagger/OpenAPI)

Project Structure

• app/ - Main application code
  • api/ - API endpoints and routes
  • core/ - Core application functionality
  • models/ - Data models and schemas
  • services/ - Business logic services
  • utils/ - Utility functions
  • data/ - Sample data for testing
  • output/ - Output files
• build-plan/ - Project planning documents
• tests/ - Test cases

License

MIT