Epubify

Convert PDF files to nicely structured Markdown and EPUB format with intelligent layout detection.

Features

• Smart layout detection for books and academic papers
• Advanced text extraction and OCR capabilities
• Table detection and formatting
• Image extraction and optimization
• Clean markdown output with preserved structure
• EPUB generation with customizable styling
• Multi-language support
• GPU acceleration support (NVIDIA, AMD, Apple Silicon)

Installation

From PyPI (recommended)

pip install epubify

Using uv

uv tool install epubify

Using pipx

pipx install epubify

From source

git clone https://github.com/mustafa-zidan/epubify.git
cd epubify
uv sync

Homebrew (planned)

A Homebrew tap is planned for future releases:

# Coming soon
brew install mustafa-zidan/tap/epubify

For GPU support (NVIDIA/AMD/Apple Silicon), follow the official PyTorch installation guide.

Dependencies

• Python 3.10+
• uv (recommended for dependency management)
• PyTorch (with CUDA/ROCm/MPS support)
• marker-pdf, transformers, markdown

Usage

Command Line

epubify input.pdf

Or via uv:

uv run epubify input.pdf

Options:

As a Library

from pathlib import Path
from epubify.pdf2md import convert_pdf
from epubify.mark2epub import convert_to_epub

# Convert PDF to Markdown
convert_pdf("input.pdf", Path("./output/input"))

# Convert Markdown to EPUB
convert_to_epub(Path("./output/input"), Path("./output"))

Output Structure

output_directory/
├── document_name/
│   ├── document_name.md
│   ├── document_name.epub
│   ├── document_name_metadata.json
│   └── images/
│       ├── image1.png
│       ├── image2.jpg
│       └── ...

Development

Setup

git clone https://github.com/mustafa-zidan/epubify.git
cd epubify
uv sync --group dev

Running tests

uv run pytest

CI/CD

This project uses GitHub Actions for:

• CI (ci.yml) - Runs tests across Python 3.10-3.13 on every push/PR
• Qodana (qodana_code_quality.yml) - Static code analysis via JetBrains Qodana
• Publish (publish.yml) - Automatically publishes to PyPI on GitHub releases using trusted publishing

Publishing a new release

1. Update the version in pyproject.toml
2. Create a GitHub release with a tag matching the version (e.g., v0.1.0)
3. The publish workflow will automatically build and upload to PyPI

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a new branch for your feature
3. Commit your changes
4. Push to your branch
5. Create a Pull Request

Known Issues

• Some image embedding might need manual adjustment
• Some complex mathematical equations might not be perfectly converted
• Certain PDF layouts with multiple columns may require manual adjustment
• Font detection might be imperfect in some cases

License

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

Acknowledgments

• marker-pdf for PDF processing
• PyTorch for GPU acceleration
• Transformers for advanced text processing