semvec/pypi-packages huggingface dataset:
token-craft 0.1.0
streamlit custom token craft and management component
Requires Python: >=3.9
Keywords: Python,Streamlit,React,JavaScript,token craft,token manager

# 🌟 Streamlit Token Craft Component 🚀

[![GitHub][github_badge]][github_link] [![PyPI][pypi_badge]][pypi_link] [![Download][download_badge]][download_link]

Welcome to the Streamlit Token Craft Component, where managing tokens is as fun as a barrel of monkeys! 🐒 This custom Streamlit component is designed to bring joy and efficiency to your data management needs of displaying tokens in your Streamlit app.

## Features 🎉
* Inline Editing: Edit tokens directly in the table like a ninja! 🥷
* Dynamic Column Visibility: Play hide and seek with your columns! 🙈🙉
* Action Handling: Manage token deletion with style. It's like having a mini-command center. 🎮
* Responsive Design: Looks great on screens of all sizes, even on your grandma's old monitor! 👵💻

## Demo 📺

Watch the Token Craft strut its stuff!


Peek at more cool tricks up its sleeve!


## Installation 🛠️
Get this party started with a simple command:
```python
pip install streamlit-token-craft
```

## Usage 📚
Here’s a very simple example on how to unleash the power of the Token Craft in your app:

```python
import streamlit as st
from token_craft import st_token_table

mock_tokens = [
    {
        "id": "token98a1c077",
        "key": "token98-e316-49d9",
        "display_key": "token98a...e5437d75",
        "name": "Token 1",
        "dateCreated": "2023-12-20",
        "lastUsed": "Never",
        "is_active": True,
    },
]

rendered_tokens = st_token_table(
    tokens=mock_tokens,
    key="token_table",
)
```
> [!IMPORTANT]
> Keep in mind that the functionality of this component has to be combined with your token management service.
>
>For more complex functionality have a look at [demo full app](demo_full_app.py) & [demo for column selection](demo_col_selection_app.py).

## Contributing 🤝
Got ideas? Jump in! Contributions are as welcome as puppies at a park! 🐶

For more information, see [CONTRIBUTING](CONTRIBUTING.md) instructions.

## License 📜
This project is licenced under an [MIT Licence](LICENSE).




[github_badge]: https://badgen.net/badge/icon/GitHub?icon=github&color=black&label

[github_link]: https://github.com/stavrostheocharis/streamlit-token-craft

[pypi_badge]: 

[pypi_link]: 

[download_badge]: 

[download_link]:
fastgplearn 0.0.17
Fast fitting formula.
Requires Python: >=3.6
Keywords: genetic programming","symbolic learning

[![Fastgplearn](./img.jpg)](https://gitee.com/boliqq07/fastgplearn)


[![Python Versions](https://img.shields.io/pypi/pyversions/fastgplearn.svg)](https://pypi.org/project/fastgplearn/)
![pypi Versions](https://badge.fury.io/py/fastgplearn.svg)
[![Documentation Status](https://readthedocs.org/projects/fastgplearn-en/badge/?version=latest)](https://fastgplearn-en.readthedocs.io/en/latest/?badge=latest)

FastGPLearn
------------------------
Welcome to FastGPLearn.

FastGPLearn implements Genetic Programming in Python, with a ``scikit-learn`` inspired and compatible API.
And the FastGPLearn applied the ``torch`` and ``numpy`` backend for fast calculated, make it accessible for ``CUDA`` .

FastGPLearn inspired by
[gplearn](https://gplearn.readthedocs.io/en/stable/intro.html),
[BGP](https://bgp.readthedocs.io/en/latest/index.html),


Document
----------------------
[fastgplearn document (English)](https://fastgplearn-en.readthedocs.io/en/latest)

[fastgplearn document (Chinese)](https://fastgplearn.readthedocs.io/en/latest/) 
writing...


Quick Install
----------------------

```bash
pip install fastgplearn
```

License
----------------------
MIT Licence

Contact
----------------------
Developer: boliqq07: 986798607@qq.com

Support
----------------------
[![Jetbrains](./jetbrains.svg)](https://jb.gg/OpenSource)