semvec/pypi-packages huggingface dataset:
hanpinyin 0.1.0
基于 g2pW 使用 torch 推理的 pypinyin
Requires Python: <4,>=3.6

# hanpinyin

基于 [g2pW](https://github.com/GitYCC/g2pW/) 提升 [pypinyin](https://github.com/mozillazg/python-pinyin) 的准确性。

特点：

* 支持使用 torch 推理
* 可以通过训练模型的方式提升拼音准确性。
* 功能和使用习惯与 pypinyin 基本保持一致，支持多种拼音风格。


## 使用

### 安装依赖

1. 安装 [PyTorch](https://pytorch.org/get-started/locally/)。
2. 下载并解压 G2PWModel:

    ```
    wget https://storage.googleapis.com/esun-ai/g2pW/G2PWModel-v2-onnx.zip
    unzip G2PWModel-v2-onnx.zip
    ```
3. 安装 [git-lfs](https://git-lfs.github.com/)。
4. 下载 [bert-base-chinese](https://huggingface.co/bert-base-chinese):

   ```
   git lfs install
   git clone https://huggingface.co/bert-base-chinese
   ```
5. 安装本项目:

   ```
   pip install hanpinyin
   ```

### 使用示例

   ```python
   >>> from pypinyin import Style
   >>> from hanpinyin import G2PWPinyin

   # 需要将 model_dir 和 model_source 的值指向下载的模型数据目录
   >>> g2pw = G2PWPinyin(model_dir='G2PWModel/',
                         model_source='bert-base-chinese/',
                         v_to_u=False, neutral_tone_with_five=True)
   >>> han = '然而，他红了20年以后，他竟退出了大家的视线。'

   # def lazy_pinyin(self, hans, style=Style.NORMAL, errors='default', strict=True, **kwargs)
   # 通过 lazy_pinyin 方法获取拼音数据，各个参数的含义和作用跟 pypinyin 中是一样的，
   # v_to_u 和 neutral_tone_with_five 参数只能在初始化 G2PWPinyin 时指定。

   >>> g2pw.lazy_pinyin(han)
   ['ran', 'er', '，', 'ta', 'hong', 'le', '20', 'nian', 'yi', 'hou', '，', 'ta', 'jing', 'tui', 'chu', 'le', 'da', 'jia', 'de', 'shi', 'xian', '。']

   >>> g2pw.lazy_pinyin(han, style=Style.TONE)
   ['rán', 'ér', '，', 'tā', 'hóng', 'le', '20', 'nián', 'yǐ', 'hòu', '，', 'tā', 'jìng', 'tuì', 'chū', 'le', 'dà', 'jiā', 'de', 'shì', 'xiàn', '。']

   >>> g2pw.lazy_pinyin(han, style=Style.TONE3)
   ['ran2', 'er2', '，', 'ta1', 'hong2', 'le5', '20', 'nian2', 'yi3', 'hou4', '，', 'ta1', 'jing4', 'tui4', 'chu1', 'le5', 'da4', 'jia1', 'de5', 'shi4', 'xian4', '。']
   ```

## 离线使用

默认情况下，即便使用了离线的模型数据，程序使用的 transformers 模块仍旧会从 huggingface.co 下载部分模型元数据。
可以通过设置环境变量 `TRANSFORMERS_OFFLINE=1` 以及环境变量 `HF_DATASETS_OFFLINE=1` 禁用获取元数据的操作，实现完全离线使用的需求。
详见 [transformers 官方文档](https://huggingface.co/docs/transformers/v4.21.2/en/installation#offline-mode)。


## 模型训练

详见 [g2pW](https://github.com/GitYCC/g2pW/#train-model) 官方文档中的说明。
clibot 0.1.4.1
An innovative command-line tool driven by powerful large language models, designed to accelerate task completion and maximize your productivity.
Requires Python: >=3.6

# Clibot

Clibot is an innovative command-line productivity tool powered by advanced language models. It helps you accomplish tasks faster and more efficiently by interacting with AI models directly from the command line. Clibot supports various command line interfaces including CMD, PowerShell, Zsh, and more.

https://github.com/user-attachments/assets/320cd140-4949-46e5-88ba-ba4f9e2e0f96

## Features

- **Multi-Provider Support**: Compatible with multiple AI providers including Groq, OpenAI, Mistral, and Ollama.
- **Conversation History**: Keeps track of your conversations for easy review.
- **Setup and Configuration**: Streamlined setup process to configure your AI providers.
- **Asynchronous Processing**: Utilizes asynchronous operations for improved performance.
- **Error Handling**: Robust error handling and retry mechanisms for a smooth user experience.
- **Customizable Settings**: Adjust AI model parameters like temperature and max tokens.

## Installation

To install Clibot, run the following command:

```bash
pip install clibot
```

> [!TIP]
> You can use locally hosted open source models which are available for free. To use local models, you will need to run your own LLM backend server such as [Ollama](https://github.com/ollama/ollama).
>
> **❗️Note: Localhosted models require high-end hardware for optimal performance and may not work as expected on lower-end systems.**

### Setup

After installation, run the following command to set up Clibot:

```bash
clibot
```

### Usage

Use Clibot by executing the `clibot` command followed by your query or command. Here are some examples:

- General Queries:

```bash
clibot "How is my system?"
```

- Summarizing multiple files:

```bash
clibot "Summarize" < file1.txt < file2.txt
```

- Creating and saving files:

```bash
clibot 'Write a simple README file' > README.md
```

- Reviewing CPU information:

```bash
lscpu | clibot 'Review my CPU'
```

Feel free to explore the various capabilities of Clibot and make the most out of this powerful command-line productivity tool!

### Donations

If you find this project useful, consider supporting my work by making a donation. Every contribution helps me continue developing and maintaining this project.

[![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/linuztx)
streamlit-antd-components-mod 0.1.7
streamlit custom components of Antd Design and Mantine, modded from jihaoran's streamlit-antd-components
Requires Python: >=3.8

# Streamlit-Antd-Components-mod

[![Open in Streamlit][share_badge]][share_link]
[![GitHub][github_badge]][github_link]
[![PyPI][pypi_badge]][pypi_link]
[![download][download_badge]][download_link]

A modded library forked from the excellent Streamlit-Antd-Components project. https://github.com/nicedouble/StreamlitAntdComponents

This library provides component below:

- `buttons` A group of buttons component.
- `divider` A divider line separates different content
- `menu` A versatile menu for navigation
- `steps` A navigation bar that guides users through the steps of a task (modded to enable coloured icons in steps component)
- `cascader` Chooses cascade items in one float layer for better user experience.
- `rate` Rate component
- `switch` Switching between two states or on-off state
- `transfer` Double column transfer choice box
- `segmented` Segmented Controls.
- `tabs` A tabs component.
- `tree` A hierarchical list structure component.
- `alert` Alert component for feedback
- `result` Used to feed back the results of a series of operational tasks
- `tag` Tag for categorizing or markup
- `pagination` A long list can be divided into several pages

## Install

```shell script
pip install streamlit-antd-components-mod
```

## Quickstart

Create a list of steps,use more style params.

```python
import streamlit as st
import streamlit_antd_components_mod as sacm

items = [
    sacm.StepsItem(title='title A', description='description A', icon='check'),
    sacm.StepsItem(title='title B', description='description B', icon='circle-fill'),
    sacm.StepsItem(title='title C', description='description C', icon='x-circle'),
]
selected_step = sacm.steps(
    items=items,
    size='lg',
    color='grey',
    direction='vertical',
    icon_color_map={'check': 'green', 'circle-fill': 'yellow', 'x-circle': '#AA4A44'}
)
st.write(f'The selected step is: {selected_step}')
```

![steps](./img/steps.png)

[share_badge]: https://static.streamlit.io/badges/streamlit_badge_black_white.svg
[share_link]: https://nicedouble-streamlitantdcomponentsdemo-app-middmy.streamlit.app/
[github_badge]: https://badgen.net/badge/icon/GitHub?icon=github&color=black&label
[github_link]: https://github.com/Cocopuffff/StreamlitAntdComponents
[pypi_badge]: https://badgen.net/pypi/v/streamlit-antd-components-mod
[pypi_link]: https://pypi.org/project/streamlit-antd-components-mod/
[download_badge]: https://badgen.net/pypi/dm/streamlit-antd-components-mod
[download_link]: https://pypi.org/project/streamlit-antd-components-mod/#files
mit-21m387 1.0.7
"Python package for MIT's 21M.387"
Requires Python: ~=3.11.1

# 21M.387 Fundamentals of Music Processing

This python package is for the MIT course 21M.387 Fundamentals of Music Processing. It is used in
conjunction with other class material that is distributed during the course of the semester. See [course overview](https://musictech.mit.edu/fmp) for more information.

Contact: egozy at mit dot edu
gwstreamlit 0.0.15
Guidewire Streamlit Engine
Requires Python: <4.0,>=3.12

# GWStreamlit

## Overview

GWStreamlit is a Python package that provides a standardised streamlit interface for Guidewire applications. The user 
interface definition is defined in a yaml file and the package provides a streamlit interface to interact with 
the Guidewire application.

Use in other non Guidewire application is possible, but the package is designed to work with Guidewire applications and
any maintenance for Guidewire applications will be prioritised. This is not to say that any requests for enhancements 
will be ignored, quite the opposite.

## Installation

Installation is via pip, ensure that the latest version is installed. This requires Python 3.12 or later.

```bash
pip install gwstreamlit
```

## Usage

### GWStreamlit creation

GWStreamlit initalization requires an application name and either the name of the yaml file if the files are stored 
in resources/yaml_ui directory. Otherwise, the full path to the yaml file is required.

```python
def build():
    initalize(my_application, my_yaml_file_location)
```

Initialize creates an instance of the GWStreamlit class transferring the yaml file information to a pydantic model. The 
class instance is stored in the streamlit session state. This permits only a single instance of the GWStreamlit class to
exist at any one time.