semvec/pypi-packages huggingface dataset:
jvclient 2.1.25
JIVAS client library for action development.
Requires Python: >=3.12.0
Keywords: jivas

# JVClient (jvclient)

![GitHub release (latest by date)](https://img.shields.io/github/v/release/TrueSelph/jvclient)
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![GitHub issues](https://img.shields.io/github/issues/TrueSelph/jvclient)
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![GitHub](https://img.shields.io/github/license/TrueSelph/jvclient)

`jvclient` is a UI library for building JIVAS applications.

## Installation

To install `jvclient`, use `pip`:

```sh
pip install jvclient
```

## Usage

To use `jvclient`, import the required utility functions or widgets into your streamlit application


## 🔰 Contributing

- **🐛 [Report Issues](https://github.com/TrueSelph/jvclient/issues)**: Submit bugs found or log feature requests for the `jvclient` project.
- **💡 [Submit Pull Requests](https://github.com/TrueSelph/jvclient/blob/main/CONTRIBUTING.md)**: Review open PRs, and submit your own PRs.

<details closed>
<summary>Contributing Guidelines</summary>

1. **Fork the Repository**: Start by forking the project repository to your GitHub account.
2. **Clone Locally**: Clone the forked repository to your local machine using a git client.
   ```sh
   git clone https://github.com/TrueSelph/jvclient
   ```
3. **Create a New Branch**: Always work on a new branch, giving it a descriptive name.
   ```sh
   git checkout -b new-feature-x
   ```
4. **Make Your Changes**: Develop and test your changes locally.
5. **Commit Your Changes**: Commit with a clear message describing your updates.
   ```sh
   git commit -m 'Implemented new feature x.'
   ```
6. **Push to GitHub**: Push the changes to your forked repository.
   ```sh
   git push origin new-feature-x
   ```
7. **Submit a Pull Request**: Create a PR against the original project repository. Clearly describe the changes and their motivations.
8. **Review**: Once your PR is reviewed and approved, it will be merged into the main branch. Congratulations on your contribution!
</details>

<details open>
<summary>Contributor Graph</summary>
<br>
<p align="left">
    <a href="https://github.com/TrueSelph/jvclient/graphs/contributors">
        <img src="https://contrib.rocks/image?repo=TrueSelph/jvclient" />
   </a>
</p>
</details>

## 🎗 License

This project is protected under the Apache License 2.0. See [LICENSE](./LICENSE) for more information.
smashup-cli 0.0.1
Smash Up faction randomizer with TUI configuration.
Requires Python: >=3.9

# Smash Up CLI

Smash Up CLI is a terminal utility for building Smash Up decks. It lets you
select expansions in a TUI and randomly assign factions to each player.

## Features

- TUI configuration to enable or disable expansions.
- Randomized faction assignment per player.
- Config stored at `~/.smashup/factions.toml`.

## Installation

```bash
pip install smashup-cli
```

## Usage

Configure which expansions are available:

```bash
smashup configure
```

<img width="566" height="340" alt="image" src="https://github.com/user-attachments/assets/b35eb626-b904-430a-86c9-76a074509b5a" />


Randomize factions for a game:

```bash
smashup randomize
```

<img width="400" height="220" alt="image" src="https://github.com/user-attachments/assets/59a56c2e-2e40-4654-ad0c-e1adad11a0fa" />


## Development

```bash
pip install -e .
```

## License

MIT
astrapia 0.2.7
ML Inference Library with CoreML and ONNXRunTime backends.
Requires Python: <3.13,>=3.10
Keywords: CoreML, ML-Inference, ONNXRunTime

# Astrapia
[![Test & Build](https://github.com/Tensor46/astrapia/actions/workflows/python-package.yml/badge.svg?branch=main)](https://github.com/Tensor46/astrapia/actions/workflows/python-package.yml)

ML Inference Library with CoreML and ONNXRunTime backends.

## Examples
### MediaPipe
MediaPipe face detector (short & long) and face mesh landmark models in both CoreML and ONNXRunTime are added.