semvec/pypi-packages huggingface dataset:
horn-py 0.6.5
A Flask scaffolding tool
Requires Python: >=3.7

# ![flask](chili.png)  Horn: A Flask scaffolding tool

[![PyPI - License](https://img.shields.io/pypi/l/horn-py.svg)](https://pypi.org/project/horn-py)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/horn-py.svg)](https://pypi.org/project/horn-py)
[![PyPI - Implementation](https://img.shields.io/pypi/implementation/horn-py.svg)](https://pypi.org/project/horn-py)
[![PyPI](https://img.shields.io/pypi/v/horn-py.svg)](https://pypi.org/project/horn-py)
[![Build Status](https://travis-ci.org/bigfang/horn-py.svg?branch=master)](https://travis-ci.org/bigfang/horn-py)
[![Codecov](https://img.shields.io/codecov/c/gh/bigfang/horn-py.svg)](https://codecov.io/gh/bigfang/horn-py)


## Installation

```console
$ pip install horn-py
```


## Usage

```text

Usage:
  horn new <target> [--app=<app> --proj=<proj> --pypi=<pypi> --bare]
  horn new <target> <from> [<checkout>] [--json=<json>] [-f=PATH | --file=PATH]
  horn gen api <module> <table> <fields>...
  horn gen model <module> <table> <fields>...
  horn gen schema <module> (<fields>... | --model=<model> | <fields>...  --model=<model>)
  horn -h | --help
  horn --version

Options:
  --app=<app>               App name [default: app].
  --proj=<proj>             Project name.
  --pypi=<pypi>             Pypi domain.
  --bare                    Bare project.

  --json=<json>             Json string [default: {}].
  -f=PATH, --file=PATH      Json file PATH.

  --model=<model>           Schema baseed on model.

  -h, --help                Show this screen.
  --version                 Show version.

Examples:
  horn new tmp/foo_bar --app foobar --proj FooBar
  horn new tmp/foo_bar https://github.com/bigfang/drf-starter.git --json '{"app":"someapp"}'
  horn gen api Post posts title:string:uniq content:text:nonull author:ref:users
  horn gen model Post posts title:string:uniq:index content:string:nonull author:ref:users:nonull
  horn gen schema Post title:string content:string author:nest:user

Notes:
  Model attrs:  uniq => unique=True, nonull => nullable=False,
                index => index=True, default:val => default=val
  Schema attrs: dump => dump_only, load => load_only, exclude => exclude,
                required => required=True, none => allow_none=True
```
shadowhash 0.0.1
Generate /etc/shadow crypt hashes

# shadowhash

Generate /etc/shadow crypt hashes. You can see the list and format of hashes in
`man 5 crypt`.

## Usage

By default a [yescrypt](https://www.openwall.com/yescrypt/) hash will be generated (used in latest GNU/Linux):
```
$ shadowhash P4ssw0rd
$y$j9T$t7MgdzAA4NZLiAVe.dLJD/$eKtF1WXeiYj5aJe3pD9eAMkHYXHrLIgYrzL1Juz5aQ2
```

There is option to create other hashes types and send passwords from stdin:
```
$ echo P4ssw0rd | shadowhash -t 5
$5$DZ3bXwsPgopGm0CQ$Rhg3l4OYopgQkH..BNm/MytzVd0n75oj8KAMDvGpA95
```

## Installation

```
pip install shadowhash
```
Yogev 0.0.1
Rvc Gui Packege
Requires Python: >=3.7

<div align="center">

<h1>RVC GUI<br><br>
  
For audio file inference only

  <br>

  

</div>

  

 

  
## GUI

![GUI](https://github.com/Tiger14n/RVC-GUI/raw/main/docs/GUI.JPG)
 <br><br>
  
## Direct setup for Windows users
## [Windows-pkg](https://github.com/Tiger14n/RVC-GUI/releases/tag/Windows-pkg)
  
<br><br>
## Preparing the environment


* Install Python version +3.8 if you have not:

* Execute these commands

Windows with Nvidia cards
```bash
python -m pip install -U pip setuptools wheel
pip install -U torch torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
```
Other
```
python -m pip install -U pip setuptools wheel
pip install -U torch torchaudio 
pip install -r requirements.txt
```

Apple silicon Macs fix
```
pip install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu

export PYTORCH_ENABLE_MPS_FALLBACK=1
```
<br>

* Downlaod [hubert_base.pt](https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt/) and place it in the root folder

<br>
 
* Then use this command to start RVC GUI:
```bash
python rvcgui.py
```
Or run this file on windows
```
RVC-GUI.bat
```

# Loading models
use the import button to import a model from a zip file, 
* The .zip must contain the ".pth" weight file. 
* The .zip is recommended to contain the feature retrieval files ".index"

Or place the model manually in root/models
```
models
├───Person1
│   ├───xxxx.pth
│   ├───xxxx.index
│   └───xxxx.npy
└───Person2
    ├───xxxx.pth
    ├───...
    └───...
````
<br>


<br> 

### How to get models?.
* Join the[ AI Hub](https://discord.gg/aihub) Discord 
* [Community Models on HuggingFace](https://huggingface.co/QuickWick/Music-AI-Voices/tree/main) by Wicked aka QuickWick

<br>

K7#4523
irc-api 1.0.7
An API written in Python to make IRC bots.
Requires Python: >=3.7

# IRC API
[![Documentation Status](https://readthedocs.org/projects/irc-api/badge/?version=latest)](https://irc-api.readthedocs.io/en/latest/?badge=latest)
[![Licence](https://img.shields.io/github/license/Shadow15510/irc_api?color=green)](https://github.com/Shadow15510/irc_api/blob/master/LICENSE)
[![Build Status](https://github.com/Shadow15510/irc_api/actions/workflows/python-publish.yml/badge.svg)](https://github.com/Shadow15510/irc_api/actions/workflows/python-publish.yml)

## Description
IRC API is a small API to make Python IRC bots. This API is based on decorators to make commands like the `cog` module in `discord.py`.

If you encounter any issue feel free to [open one](https://github.com/Shadow15510/irc_api/issues).
A full documentation is available on [readthedocs](https://irc-api.readthedocs.io/en/latest/).

## Installation
To install IRC API, you should use ``pip install irc-api``, check out the [Pypi project page](https://pypi.org/project/irc-api/) for more informations.

## Licence
This project is under GNU General Public Licence v3.0 or later (GPLv3+).
Please see [LICENCE](https://github.com/Shadow15510/irc_api/blob/master/LICENSE) for more informations
sanickit 0.0.6
A web framework that applies some of the ideas of SvelteKit to Sanic
Requires Python: >=3.10

# SanicKit

[![PyPI - Version](https://img.shields.io/pypi/v/sanickit.svg)](https://pypi.org/project/sanickit)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/sanickit.svg)](https://pypi.org/project/sanickit)

SanicKit is a framework that applies some of the ideas of Svelte-kit to the Sanic web framework. In particular:

* File-path routing
* Easy deployment

The framework is designed for multi-page apps - pages are rendered on the server and sent to the browser. Libraries such as [htmx](https://htmx.org)
can be used to create a more SPA-like experience.

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install sanickit
```

## License

`SanicKit` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
preProcessingGEP 1.0.5
A package for preprocessing text
Requires Python: >=3.6

## Usage of PreProcessingService

You can access following functions:

The provided package is a collection of functions for text preprocessing and cleaning. Here is a brief explanation of each function:

_en_stopwords: Returns a list of English stopwords.

strip_numeric: Removes numbers from a string.

strip_punctuation: Removes all punctuation marks from a string.

remove_stopwords: Removes stopwords from a string.

strip_multiple_whitespaces: Removes excess whitespace from a string.

strip_short: Removes words with a length less than a specified size from a string.

remove_alpha_num: Removes alphanumeric characters from a string.

lemmatize: Lemmatizes a list of tokens.

nltk_pos: Performs Part-of-Speech (POS) tagging using NLTK.

stanford_pos: Performs POS tagging using Stanford POS Tagger.

filter_pos: Filters words based on specific POS tags.

clean_text: Cleans the text by applying various cleaning functions.

preprocess_string: Preprocesses a string by applying a list of cleaning functions.

preprocess_postags: Applies a list of filtering functions to a list of strings.

guided_buy_preprocess: Preprocesses a list of strings for guided buying purposes.

dump_interim_steps: Creates a DataFrame with the intermediate steps of preprocessing.

lemmatization: Performs lemmatization on a string.

cold_start_preprocess: Preprocesses a list of words for cold start purposes.

unique_value_col: Splits words based on '|'.

convert_synonyms:A collection of synonym mappings

clean_description: Includes all above cleaning steps

## Example usage: 
>> pip install preProcessingGEP==1.0.4

import preProcessingGEP

print(preProcessingGEP.PreProcessingService.clean_description(inputText))

### General Syntax to use any function
#### preProcessingGEP.PreProcessingService.function_name()
iframes 0.0.7
Fast detection of iframes in mpegts streams
Requires Python: >=3.6

# iframes
Fast detection of iframes in mpegts streams 

## Install
* pip install iframes
```smalltalk
python3 -mpip install iframes
```

## Run 
* local file
```js
first video.ts
```

```js
iframes video.ts
```
* https
```rebol
first https://example.com/video.ts
```

```rebol
iframes https://example.com/video.ts
```
* multicast
```rebol
first udp://@227.5.5.5:1234
```
```rebol
iframes udp://@227.5.5.5:1234
```
## Output
* iframe pts
```smalltalk
14648.092267
14648.342511
14648.509344
14648.7596
14649.009844
14649.2601
14649.426933
14649.6605
14649.910744
14650.161
14650.411244
14650.6615
14650.911744
14651.162
14651.412244
14651.6625
14651.912744
14652.163
14652.413244
14652.6635
14652.847011
14653.097267
14653.347511
14653.597767
14653.848011
14654.081578
14654.315144
14654.565389
14654.815644
14655.065889
14655.316144
14655.566389
14655.816644
14656.066889
14656.317144
14656.433933
14656.684178
14656.934433
14657.184678
14657.434933
```
### programmatically 

```py3
from iframes import IFramer
ifrmr = IFramer()
first_frame = ifrmr.first('a_video.ts')
```

```py3
from iframes import IFramer
ifrmr = IFramer(shush=True)  # set IFramer.shush=True to suppress printing pts 
all_iframes = ifrmr.do('https://example.com/coolvideo.ts')
```
aiopypiserver 0.0.7
Like pypiserver, but works behind a proxy with relative href addresses. Uses aiohttp.
Requires Python: >=3.9

# aiopypiserver

A basic PyPi server using aiohttp to serve web pages. Intended to work behind
an Apache proxy with relative href accessing. Available here of from [PyPI](https://pypi.org/project/aiopypiserver/).

# Motivation

This is intended to work behind an Apache proxy. This means providing
href links in the pages as relative links. i.e. ./packages/pkg_name.tar.gz
and not /packages... .

This is addressed as provided by [WSGI](https://github.com/pypiserver/pypiserver/issues/155).
Looking at the code I liked the idea of implementing with ```asyncio``` and ```aiohttp```
in preference to forking the ```pypiserver``` code.

# Usage

```
usage: aiopypiserver [-h] [-p port] [-i address] [-u username] [-P password] [-v] [-q] [package_path]
Private PyPi  server.

positional arguments:
  package_path                       path to packages

options:
  -h, --help                         show this help message and exit
  -p port, --port port               Listen on port
  -i address, --interface address    Listen on address
  -u username, --username username   For uploading packages
  -P password, --password password   ...
  -v, --verbose                      set debug level
  -q, --quiet                        turn off access logging

Browse index at http://localhost:8080/.
```

Can also be run as a module as ```python -m aiopypiserver -h```. Using the internal class is probably a bad idea ATM as the API is likely to change.

By default access logs are generated, as I find it useful to see these.

# Apache

Add the following to your Apache config. This is the item for pypiserver that required wsgi.
```
ProxyPass /pypi/ http://127.0.0.1:8080/
ProxyPassReverse /pypi/ http://127.0.0.1:8080/
```

# Thanks

Please let me know how you get on through the github page.
newpreProcessingGEP 1.0.7
A package for preprocessing text
Requires Python: >=3.6

## Usage of PreProcessingService

You can access following functions:

The provided package is a collection of functions for text preprocessing and cleaning. Here is a brief explanation of each function :

FOR SUPPLIER:


supplier_name_cleaning

supplier_norm_name_cleaning

supplier_name_normalization_bkp


FOR KEYWORD:


removestopwords

lemmatization

removespecialchars

RemoveNonEngWords

SeparateNumStrings

stemming


FOR ITEM:

_en_stopwords: Returns a list of English stopwords.

strip_numeric: Removes numbers from a string.

strip_punctuation: Removes all punctuation marks from a string.

remove_stopwords: Removes stopwords from a string.

strip_multiple_whitespaces: Removes excess whitespace from a string.

strip_short: Removes words with a length less than a specified size from a string.

remove_alpha_num: Removes alphanumeric characters from a string.

lemmatize: Lemmatizes a list of tokens.

nltk_pos: Performs Part-of-Speech (POS) tagging using NLTK.

stanford_pos: Performs POS tagging using Stanford POS Tagger.

filter_pos: Filters words based on specific POS tags.

clean_text: Cleans the text by applying various cleaning functions.

preprocess_string: Preprocesses a string by applying a list of cleaning functions.

preprocess_postags: Applies a list of filtering functions to a list of strings.

guided_buy_preprocess: Preprocesses a list of strings for guided buying purposes.

dump_interim_steps: Creates a DataFrame with the intermediate steps of preprocessing.

lemmatization: Performs lemmatization on a string.

cold_start_preprocess: Preprocesses a list of words for cold start purposes.

unique_value_col: Splits words based on '|'.

convert_synonyms:A collection of synonym mappings

clean_description: Includes all above cleaning steps

## Example usage: 
>> pip install preProcessingGEP==1.0.4

import preProcessingGEP

print(preProcessingGEP.PreProcessingService.clean_description(inputText))

### General Syntax to use any function
#### preProcessingGEP.itemPreprocess.function_name()
#### preProcessingGEP.keywordPreprocess.function_name()
#### preProcessingGEP.function_name()
pytk-net 1.0.0
TkinterHelper布局助手官方拓展和工具库
Requires Python: >=3.8

TkinterHelper布局助手官方拓展和工具库

## 安装方式

> pip install pytk-net

## 工具函数

utils.py 整合了一些常用的工具函数

### 加载图片

> load_image(img_path, width, height):

### 加载图标

> load_font_icon(icon_name, size=100, color="#000")

## 组件

widgets.py 拓展组件类

### 图标组件 Icon

可以在tkinter上展示图标,图标数量众多大概1800多个,基于开源的bootstrap图标.

### 日期输入框 DateEntry

基于ttkbootstrap的DateEntry,重写部分代码,更符合国内使用.

### 拓展选项卡组件 ExtTabs

点击右侧选项菜单 切换视图

## 开源组件

本项目使用的开源项目如下

| 开源名称           |
|----------------|
| bootstrap icon |
| ttkbootstrap   |