semvec/pypi-packages huggingface dataset:
st-btn-select-custom 0.1.3
Streamlit Button Selection Component
License: MIT
Requires Python: >=3.6

<div align="center">

<img
  alt="Logo"
  src="https://raw.githubusercontent.com/0phoff/st-btn-select/master/public/logo.svg"
  width="100%"
/>

[![Version][version-badge]][version-url]
[![License][license-badge]][license-url]
<a href="https://ko-fi.com/D1D31LPHE"><img alt="Ko-Fi" src="https://www.ko-fi.com/img/githubbutton_sm.svg" height="20"></a>

_Streamlit Button Select Component_

</div>

---

Sometimes you want a user to make a selection, but you only have a few options.  
Using an `st.selectbox` component works, but wouldn't it be easier to simply have a few buttons in a row ?  
Well, this custom component allows just that !


# Installation
```bash
pip install st_btn_select
```


# Getting Started
Creating a Button Selection is really easy.
```python
from st_btn_select import st_btn_select

selection = st_btn_select(('option 1', 'option 2', 'option 3'))
```

You can also use this component as a small top navigation bar.  
```python
from st_btn_select import st_btn_select

page = st_btn_select(
  # The different pages
  ('home', 'about', 'docs', 'playground'),
  # Enable navbar
  nav=True,
  # You can pass a formatting function. Here we capitalize the options
  format_func=lambda name: name.capitalize(),
)

# Display the right things according to the page
if page == 'home':
  st.write('HOMEPAGE')
```

> **NOTE**  
> There can only be one navbar per page, as they will be displayed on top of each other.
> 
> The navbar buttons do not set any URL hashes, and thus the different pages are not bookmarkable, nor can you use the browser history.


## Documentation
Check out this streamlit app for the documentation, as well as a demo.  
[![Open in Streamlit][share-badge]][share-url] 



[version-badge]: https://img.shields.io/pypi/v/st_btn_select?label=version
[version-url]: https://github.com/0phoff/st-btn-select/releases

[license-badge]: https://img.shields.io/pypi/l/st_btn_select
[license-url]: https://github.com/0phoff/st-btn-select/blob/master/LICENSE.md

[share-badge]: https://static.streamlit.io/badges/streamlit_badge_black_white.svg
[share-url]: https://share.streamlit.io/0phoff/st-btn-select/demo/test.py
privacy-meter 1.0
Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.
License: MIT
Requires Python: >=3.6.0


# Privacy Meter

## Introduction

Privacy Meter is an open-source library to audit data privacy in statistical and machine learning algorithms. The tool can help in data protection impact assessment process by providing a quantitative analysis of fundamental privacy risks of a (machine learning) model. It uses state-of-the-art inference techniques to audit a wide range of machine learning algorithms for classification, regression, computer vision, and natural language processing. Privacy Meter generates extensive reports about the aggregate and individual privacy risks for data records in the training set, at multiple levels of access to the model.

## Installation

Privacy Meter supports Python `>=3.6` and works with `tensorflow>=2.4.0` and `torch>=1.10.0`.

You can install `privacy_meter` using `pip` for the latest stable version of the tool:

```bash
pip install privacy_meter
```

Privacy Meter has been tested with other machine learning libraries like HuggingFace and Intel OpenVINO, and can even be extended to be used with a framework of your choice.