semvec/pypi-packages huggingface dataset:
discord-ext-lava 0.5.0
A discord.py extension for lavaplayer-based audio nodes.
License: MIT
Requires Python: >=3.10.0,<4.0.0
Keywords: obsidian,lavalink,api,wrapper,async,discord,discord-ext

# discord-ext-lava
A [discord.py](https://github.com/Rapptz/discord.py) extension for [lavaplayer](https://github.com/Walkyst/lavaplayer-fork)-based audio nodes.

## Support
- [Documentation](https://discord-ext-lava.readthedocs.io/)
- [GitHub](https://github.com/Axelware/discord-ext-lava)
- [Discord](https://discord.com/invite/w9f6NkQbde)
sbermarket-api 0.0.3
Not official python client for product market sbermarket API.
License: MIT
Requires Python: >=3.8,<4.0

# sbermarket-api

<div align="center">

[![Build status](https://github.com/nov1kov/sbermarket-api/workflows/build/badge.svg?branch=master&event=push)](https://github.com/nov1kov/sbermarket-api/actions?query=workflow%3Abuild)
[![Python Version](https://img.shields.io/pypi/pyversions/sbermarket-api.svg)](https://pypi.org/project/sbermarket-api/)
[![Dependencies Status](https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg)](https://github.com/nov1kov/sbermarket-api/pulls?utf8=%E2%9C%93&q=is%3Apr%20author%3Aapp%2Fdependabot)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Security: bandit](https://img.shields.io/badge/security-bandit-green.svg)](https://github.com/PyCQA/bandit)
[![Pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/nov1kov/sbermarket-api/blob/master/.pre-commit-config.yaml)
[![Semantic Versions](https://img.shields.io/badge/%20%20%F0%9F%93%A6%F0%9F%9A%80-semantic--versions-e10079.svg)](https://github.com/nov1kov/sbermarket-api/releases)
[![License](https://img.shields.io/github/license/nov1kov/sbermarket-api)](https://github.com/nov1kov/sbermarket-api/blob/master/LICENSE)

Not official python client for product market Sbermarket.

</div>

## Задачи что преследовал:

1. Поиск самого дешевого товара среди магазинов вокруг.
2. Отлеживание в целом цен на продукты.
3. Сделать алерт на дешевые товары.
4. Вести статистику на цены товаров.

## Что внутри?

Токен получается только из статического js скрипта. Где содержится JSON вида:
```json
{"api-version":"3.0","client-token":"TOKEN","is-storefront-ssr":l.sk}
```
Скорее всего он у всех одинаковый.

## Installation

```bash
pip install -U sbermarket-api
```

or install with `Poetry`

```bash
poetry add sbermarket-api
```

## 📈 Releases

You can see the list of available releases on the [GitHub Releases](https://github.com/nov1kov/sbermarket-api/releases) page.

We follow [Semantic Versions](https://semver.org/) specification.

We use [`Release Drafter`](https://github.com/marketplace/actions/release-drafter). As pull requests are merged, a draft release is kept up-to-date listing the changes, ready to publish when you’re ready. With the categories option, you can categorize pull requests in release notes using labels.

## 🛡 License

[![License](https://img.shields.io/github/license/nov1kov/sbermarket-api)](https://github.com/nov1kov/sbermarket-api/blob/master/LICENSE)

This project is licensed under the terms of the `MIT` license. See [LICENSE](https://github.com/nov1kov/sbermarket-api/blob/master/LICENSE) for more details.

## Alternatives

1. Sbermarket client on TypeScript https://github.com/x0rium/sbermarket-api
nlpclassifier-profilener 0.0.1
Performs Classification using Regex for social media user's profile categorization and NER tasks
License: MIT
Keywords: NER,profile categories,classification,social media

NLP Classification Python Package For Profile Category and NER

Functionality of the Package
Performs Classification using Regex for social media user's profile categorization and NER tasks

If profile, returns the profile's category (i.e health professional, politician, journalist, etc)
If NER, returns a dataframe that has information of the entity id, name and it's occurence frequency on the input text.

The package takes the following parameters as input:

Profile: Text containing informations about the user's biography or channel description in the context of communication reaseach porpuses
NER: text

Usage
Make sure you have Python installed in your system.
Run Following command in the CMD.

pip install nlpclassifier

Example
from nlpclassifier_profilener import NLPClassifier

#instatiate the classifier
tpc = TwitterProfileClassifier('profile')
#preprocess the text. Its's important to lowercase and remove accents
yt['channelDesc'] = yt['channelDesc'].apply(lambda x: pipeline.preprocess(x, lower = True)).apply(lambda x: pipeline.strip_accents(x))
#classify channelCategory
yt['channelCategory'] = yt['channelDesc'].apply(lambda x: tpc.classifier(str(x)))

yt['channelCategory'].value_counts()

Note
The package is currently in a very elementary stage and work is in progress.
The whole project is developed with python version Python 3.7.7 and pip version pip 19.2.3.
In case of error, feel free to contact me.

Change Log
==========

0.0.1 (03/09/2022)
-------------------
- First Release