hair-editor-agent 0.2.0

Specialized agent for hair style and color editing

Hair Editor Agent

Hair Editor Agent is an AI-powered Python library and agent for realistic hairstyle and hair-color editing using image models and vision-capable LLMs.

This package focuses on flexible, descriptive editing: orchestrators may pass free-form textual descriptions for hair_style and hair_color (for example: "Shoulder-length wavy hair with subtle balayage highlights, loose beachy waves"). The agent handles image validation, optional image size/quality tuning, and forwards requests to a configurable LLM-backed client.

Key features

• Free-form hairstyle editing: accept detailed textual descriptions (length, texture, cut, layers, styling).
• Hair color transformations: accept descriptive color instructions (e.g. "rose gold with warm undertones").
• Change appearance task: combine style and color transformations in a single request.
• Optional client-side image resizing/compression to reduce payload size and latency.
• Simple, test-friendly API designed for integration into larger orchestrators and CI-driven publishing.

Installation

Developer / editable install:

python -m pip install -e .[dev]

Install from PyPI (when published):

pip install hair-editor-agent

Install directly from this repository:

pip install git+https://github.com/msalsas/hair-editor-agent.git

Quick start

from hair_editor_agent import HairEditorAgent
from agent_core_framework import AgentTask

# Create the agent. You can tune image payload size via max_image_size and jpeg_quality.
agent = HairEditorAgent(openrouter_api_key="YOUR_API_KEY", max_image_size=1024, jpeg_quality=85)

# Edit hair style and color with descriptive strings
task = AgentTask(
    type="edit_hair_style",
    payload={
        "image_path": "photo.jpg",
        "hair_style": "Shoulder-length wavy hair with subtle balayage highlights, loose beachy waves",
        "hair_color": "warm chestnut with golden highlights"
    }
)
response = agent.process(task)
print(response.success, response.error)
print(response.data)  # contains edited_image_path and echoed style/color when successful

Supported task types (examples): edit_hair_style, edit_hair_color, change_appearance.

Testing

Run the test suite locally:

python -m pytest

(Use PYTHONPATH=src if running tests from the project root to import the local package: PYTHONPATH=src pytest -q)

Publishing to TestPyPI (manual)

1. Bump the version in pyproject.toml (e.g. to 0.2.0).
2. Build distributions:

python -m build

3. Upload to TestPyPI (create a TestPyPI API token first):

python -m twine upload --repository-url https://test.pypi.org/legacy/ dist/*

Notes on CI

A GitHub Actions workflow (.github/workflows/publish.yml) can be included to automate publishing. To enable automated publishing to TestPyPI, add your TestPyPI token as a repository secret named TEST_PYPI_API_TOKEN and run the workflow manually or by pushing a test tag.

The repository also contains tests and basic CI configuration; ensure your runner has Pillow and test dependencies available.

Contributing

Contributions are welcome. Please open an issue or a pull request. Follow the existing code style, add tests for new behavior, and keep changes focused and small. If you add or change public behavior, add/adjust unit tests accordingly.

License

MIT — see the LICENSE file for details.