Metadata-Version: 2.4
Name: pydgey
Version: 0.1.0
Summary: Interactive pipeline widgets for Google Colab and Jupyter.
License-File: LICENSE
Classifier: Development Status :: 3 - Alpha
Requires-Python: >=3.9
Requires-Dist: anywidget>=0.9.0
Requires-Dist: ipython>=7.0.0
Requires-Dist: rich
Requires-Dist: traitlets>=5.0.0
Description-Content-Type: text/markdown

# Pydgey

[![Support](https://img.shields.io/pypi/status/pydgey?label=support)](https://pypi.org/project/pydgey/) [![PyPI](https://img.shields.io/pypi/v/pydgey?label=pypi)](https://pypi.org/project/pydgey/)

<p align="center">
  <img src="docs/static/pydgey-logo.png" width="50%" alt="poorly drawn pydgey logo">
</p>

## Overview

Pydgey is a Python framework for building simple graphical user interfaces for pipelines that run interactively in Jupyter Notebooks (e.g., [Google Colab](https://colab.research.google.com/), [VSCode](https://code.visualstudio.com/docs/datascience/jupyter-notebooks), [JupyterLab](https://jupyter.org/)).

Built on top of [anywidget](https://anywidget.dev/), [React](https://react.dev/), and [shadcn/ui](https://ui.shadcn.com/), Pydgey aims to abstract away some of the complexities of widget building. It allows for pipelines to be defined using standard Python classes, and handles the generation of a clean, React-based UI.

## Purpose

I needed a simple framework to quickly wrap tools and basic pipelines into user-friendly interfaces that run easily on Google Colab. The goal being to save Bachelors and Masters students in our lab the hassle of installing and managing command-line tools, letting them focus on the limited time they have to actually *do science*.

As the intended use is to run Pydgey widgets within the constraints of free Google Colab environments, the goal is not to support massive scale, high-concurrency execution, or complex dependency graphs like [Snakemake](https://snakemake.github.io/) or [Nextflow](https://www.nextflow.io/). Pydgey prioritizes accessibility and ease-of-use for smaller, ad-hoc analyses over the computational efficiency required for large-scale workflows.

## Getting Started

Pydgey can be installed from PyPI:

```bash
$ pip install pydgey
```

### 💡 Example

Create a launcher for an HMMER search pipeline. This will render an interactive widget in the notebook where users can upload files and configure E-values without writing code.

```python
from examples.hmmsearch.hmmsearch import HmmSearchPipeline
from pydgey import create_launcher

# Define your pipeline (or import a pre-made one)
pipeline = HmmSearchPipeline()

# Launch the interactive widget
launcher = create_launcher(pipeline)
launcher
```

This simple snippet produces a full-featured UI with file selectors, parameter inputs, and log streaming.

## 🏗️ Contributing

Contributions are welcome. See [CONTRIBUTING.md](CONTRIBUTING.md) for more details.

## ⚖️ License

This library is provided under the **MIT License**. See [LICENSE](LICENSE) for more information.
