semvec/pypi-packages huggingface dataset:
workflowy-assistant 0.0.5
Helps you automate your WorkFlowy.
License: MIT
Requires Python: >=3.7

## WorkFlowy Assistant
This module allows you to add multiple bullets under a parent node.

## Installation
```python
pip install workflowy-assistant
```

This module is dependent on Mozilla Firefox. Please install it if you don't have it: https://www.mozilla.org/nl/firefox/new/.

## Usage
```python
from workflowy_assistant import WorkFlowyAssistant

# Insert your WorkFlowy e-mail and password below.
email = 'abc@demo.com'
password = 'abcdef'

# Change the path to your Firefox executable if necessary; use double backslash on Windows.
pathToFirefox = 'C:\\Program Files\\Mozilla Firefox\\firefox.exe'

# Start the WorkFlowyAssistant
WF = WorkFlowyAssistant(email, password, pathToFirefox)

# Replace this with the ID of the parent node. You can find it if you zoom in on the parent node and copy the id after
# 'https://beta.workflowy.com/#/' in the address bar of your browser. 
parent_id = '5e8b93132997'

# Put all items you want to add in a list. Instead of calling the function for every item, it is better to generate the list first and
# call the function once.
listOfBullets = ['A','B','C']

# Add a list of new bullets under a parent node.
WF.add_new_bullet(parent_id, listOfBullets)

# Please close the process after all operations.
WF.close_browser()
```
kiosk-bot 1.0
kogpt2 based kiosk chatbot
License: MIT
Requires Python: >=3.6

# kiosk_bot

KoGPT2를 이용한 간단한 kiosk chatbot 입니다. 

데이터는 AiHub의 [한국어대화 데이터](https://aihub.or.kr/aidata/85)를 사용했습니다. 

데이터는 학습만 진행하였고 공개는 하지 않습니다. 
## Architecture 
Hugging face의 예제들을 보며 구현하였습니다. 

<img width="549" alt="gpt" src="https://user-images.githubusercontent.com/60643542/142431681-85db3d74-172d-45f0-9433-de43a8aeae17.png">

 ```input``` 은 ```User + <BOS> + System + <EOS>```로 구현하였다. 

```output``` 은 ```P(system|user)```를 최대화 시키기위해 모델을 학습하였다. 

## Install 
```
git clone https://github.com/momozzing/kiosk_bot.git
git lfs pull     ## lfs 가 없으면 설치하셔야 합니다. 
pip install requirements.txt
```

## How to train?
기존 학습된 데이터로 대화를 하고 싶으시면 ```install``` 후 ```How to chat``` 으로 넘어가셔도 됩니다. 
```
cd kiosk_bot/data  ## 안에 원하시는 데이터를 넣으시면 됩니다. 

kiosk_bot/GPT-2_fine_tune.py  ## 안에 데이터 loader들의 파일이름을 변경해주시면 됩니다. 

deepspeed --num_gpus=1 GPT-2_fine_tune.py ## 변경후 이것으로 실행하시면 됩니다. 

그후 생성된 skt-kogpt2-base-v2.pt 를 사용하시면 됩니다. 
```

## How to chat?
```
python interactive.py   ##기존 데이터로 채팅하기

python new_data_interactive.py  ##새로운 데이터로 파인튜닝한것 pt파일 불러와서 채팅하기.

--기존 데이터 채팅--

User: 아메리카노 주세요
System: 따뜻한 거 드릴까요?

User: 아이스로 주세요
System: 사이즈는 톨 괜찮으세요?

User: 그란데 사이즈로 주세요
System: 죄송한데 저희 가게에는 레귤러랑 라지 사이즈 밖에 없어요

User:  그럼 라지사이즈로 주세요
System: 네 고객님 테이크아웃 하시나요?

User: 먹고갈게요
System: 네 그럼 머그잔으로 준비해드리겠습니다

User: 감사합니다
System: 좋은 하루 보내세요
```

## Reference
[HuggingFace](https://huggingface.co/transformers/index.html)

[KoGPT2](https://github.com/SKT-AI/KoGPT2)

[AIHUB](https://aihub.or.kr/)
py-appconfig 0.1.2
Persistent configuration storage for python applications
License: MIT
Requires Python: >=3.7,<4.0

# py-AppConfig

Persistent configuration storage for python applications. Based on similar `npm` modules,
because of the lack of a `python` package that does something similar.

> **Note**: This package is still under development. Core functions work just fine,
> and as intended. But a few QOL features need to be worked in.

### Installation

```sh
# still in testing, pip package coming soon
pip install py-appconfig

# for now, package is active and working on TestPyPI
pip install -i https://test.pypi.org/simple/ py-appconfig
```

### Usage

```py
from appconfig import AppConfig

config = AppConfig(project_name="myProject", defaults={'a': 10, 'b': 'this is a b'})
config_values = {
    'number': 1234,
    'string': 'some random string'
}

for item in config_values:
    config.set(item, config_values[item])

print(config.get_all())
print(config.get('number'))
print(config.get('string'))

# reset and delete config values
config.reset('a')
config.reset_all()
config.delete('string')
```

### Option/args during init

1. `project_name` : `str` -> **required**
2. `project_id`: `str` -> *optional*, default = `project_name`
3. `version`: `str` -> *optional*, default = `0.0.1`
4. `conf_name`: `str` -> *optional*, default = `config` (filename of config file)
5. `conf_ext`: `str` -> *optional*, default = `.json` (file extension for config file)
6. `verbose`: `bool` -> *optional*, default = `False` (for verbose logging, needs more work)
7. `defaults`: `dict` -> *optional*, no default value, can be used to set project default settings

>Note: 2 and 3 not necessary, but may be used later for project config identification and things like version migration

### Module functions

```py
from appconfig import AppConfig

config = AppConfig(project_name="myProject")

config.set('key', 'value')
config.get('key')
config.get_all()
```

### To-do

1. Atomically writing configs to prevent corruptions due to runtime errors or system crashes.
2. A better validation system for config values. Right now it only makes sure it does not try to store a function.
3. Type annotations. (partially done)


## Related

Loosely based on the same basic concept as some `npm` packages commonly used with javascript applications.
- [`conf`](https://github.com/sindresorhus/conf) and [`appconfig`](https://raw.githubusercontent.com/anujdatar/appconfig).

## License

[MIT](https://github.com/anujdatar/py-appconfig/blob/master/LICENSE) Copyright (c) 2019-2021 Anuj Datar