semvec/pypi-packages huggingface dataset:
hexo2notionnext 0.0.9
Convert hexo  to notionnext
License: MIT
Keywords: hexo notion notion.so notion-py markdown md converter

# Hexo to NotionNext

## [中文](README.md) [English](README.EN.md)

一个简单的 hexo 导入到 [NotionNext](https://github.com/tangly1024/NotionNext) 或 [nobelium](https://github.com/craigary/nobelium)   database 的库


## 使用

在使用此库之前，您需要完成以下准备工作：

1. Notion Database 
   
   notionnext:  https://tangly1024.com/article/vercel-deploy-notion-next 
   
   nobelium:  https://github.com/craigary/nobelium


2. Hexo 博客的 _post 目录


3. 安装
```
pip install hexo2notionnext 
```

4. 在安装完库之后，创建配置文件 config.yaml 的配置
```
token_v2: <your_token>
database_url: <your_notion_database_url>
hexo_post_path: <your_hexo_post_path>
sulg_format: ':year-:day-:month-:name'
type: notionnext # or nobelium 
```
  
- **token_v2**
notion.so  cookie 中 token_v2 的值

- **database_url**
如果没有, duplicate 下面的到你的 notion
notionNext  Duplicate 这个 [notionNext Notion template](https://tanghh.notion.site/02ab3b8678004aa69e9e415905ef32a5?v=b7eb215720224ca5827bfaa5ef82cf2d) 

nobelium Duplicate 这个 [ nobeliumNotion template](https://craigary.notion.site/866916e3b939468b9b6f1d47dce99f9c)

- **hexo_post_path**
本地 Hexo 目录 的 `source/_posts` 目录

- **sulg_format**
路径的格式参考 https://hexo.io/zh-cn/docs/permalinks ,不支持 :id,  

- **type**
notionnext  or nobelium 


5. 配置完成后,使用以下命令将Hexo文章导入到NotionNext 的 database 中：

```
hexo2notionnext -c config.yaml 
```

## 问题
- For those getting 'HTTPError( requests.exceptions.HTTPError: Invalid input.)' 
  出现这个问题 
1. 在终端上运行 `pip show notion`
2. 获取 notion 包位置并访问其内容（例如`/usr/local/lib/python3.9/site-packages/notion` ）。
3. 将 `store.py`  第 280 行更改为 `limit: 100`
https://github.com/paperboi/kindle2notion/issues/14 

    
## 感谢

https://github.com/Cobertos/md2notion 

https://github.com/jamalex/notion-py

## 许可证

The MIT License.
skduplo 0.1.1
Sci-kit learn tools for machine learning pipelines
License: MIT

# scikit-duplo

Very simple reusable blocks for scikit-learn pipelines  (inspired by scikit-lego)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI](https://img.shields.io/pypi/v/scikit-duplo.svg)](https://pypi.org/project/scikit-duplo)
[![Documentation Status](https://readthedocs.org/projects/scikit-duplo/badge/?version=latest)](https://scikit-duplo.readthedocs.io/en/latest/?badge=latest)

# Installation

Installation from the source tree:

```
python setup.py install
```

Or via pip from PyPI:

```
pip install scikit-duplo
```

# Contents

The sci-kit duplo package contains multiple classes that you can use in a sci-kit
learn compatible pipeline. There are ensemble learning classes within the `meta` subdirectory.
These classes expect you to pass in multiple other Sci-kit learn compatible 
machine learning classes. It will use these to build an ensemble of models to
predict the target variable.

There are feature engineering classes inside the `preprocessing` subdirectory. These are
ColumnTransformer compatible classes that expect to receive a dataframe and set of column
names that it will transform for the downstream pipeline processes.
autotranscribe 0.1.5
An auto transcription service for youtube and normal videos.
License: MIT
Requires Python: >=3.8,<4.0
Keywords: transcription,text,speech to text

# Autotranscribe

Autotranscribe is a simple library to convert video/audio to text. The library currently supports transcription of youtube files as well as implementing multiprocessing for faster transcription. It is built around the OpenAI whisper model.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install autotranscribe.

```bash
pip install autotranscribe
```

## Usage

```python
import autotranscribe

# Transcribe a youtube file with basic functionality.
autotranscribe.transcribe_from_youtube(url,'transcripts/transcript.txt', multiproc = True)
```

## Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

[MIT](https://choosealicense.com/licenses/mit/)
django-ipghrms-perform 1.1
Django IPG HRMS APP PERFORM
License: MIT


============================
Django IPG HRMS perform
============================


Quick start
============


1. Add 'perform' to your INSTALLED_APPS settings like this::

    INSTALLED_APPS = [
        'perform'
    ]

2. Include the perform to project URLS like this::

    path('perform/', include('perform.urls')),

3. Run ``python manage.py migrate`` to create perform model

4. Another Apps Need for this Apps::
    4.1. custom::
    4.2. employee::
    4.3. user