semvec/pypi-packages huggingface dataset:
discord-eprompt 0.3.0
Reaction-based user interfaces for discord.py bots

# discord-eprompt

[![PyPI version](https://badge.fury.io/py/discord-eprompt.svg)](https://badge.fury.io/py/discord-eprompt)
[![Documentation Status](https://readthedocs.org/projects/discord-eprompt/badge/?version=latest)](https://discord-eprompt.readthedocs.io/en/latest/?badge=latest)

Reaction-based user input prompts for Discord bots

## Usage

A *reaction prompt* is simply a message with a set of predefined reactions and a target user. The bot reacts to the 
message with its set of reactions, leaving what are basically buttons which can be clicked by users. Normally, clicking 
on a reaction would just increase the count of that reaction on the message.  With reaction prompts, however, a 
callback with the choice is fired as soon as the target user clicks on one, allowing bots to actively respond to the 
user's button selection.

The library maintains a number of constraints on the message before the target user makes a decision. Reactions by 
those other than the target user are removed immediately, as are reactions *by* the target user that aren't apart of 
the predefined list of choices.

This package provides one method, `react_prompt_response` which takes a Discord message and turns it into a reaction
prompt. See its own documentation for usage details.

## Testing

Due to the difficulty of unit-testing this stuff there is currently no automated testing suite. However, there is the
`example.py` script which will demonstrate the bot's funcitonality. You will need your own bot and your own server to
use this; see the [Discord dev portal](https://discord.com/developers) for more information.

To use it, copy the `example.json.template` file to `example.json`. Then, edit the new file and replace the value for 
`token` with your bot's token, and `guild` with the ID of the guild to which the tester should send messages. The bot
will use the first available text channel to send its messages.
timst 0.2.1
Image style transfer using Torch
Requires Python: >=3.6

<p align="center">
  <img alt="Logo" src="https://github.com/Radonirinaunimi/Style-Transfer/blob/master/logo/logo.png" width=500>
</p>
<p align="center">
  <img alt="License" src="https://img.shields.io/github/license/Radonirinaunimi/Style-Transfer?style=flat-square">
  <img alt="repo size" src="https://img.shields.io/github/repo-size/Radonirinaunimi/Style-Transfer?style=flat-square">
</p>

#### Description
----------------

**Timst** is a python package based on [pyTorch]() that extracts the features of an image and tranfers them into another; such a technique is known as *image style transfer*. The following implementation is a re-implementation of this [code]() that is based on the following [scientific paper](https://arxiv.org/pdf/1508.06576.pdf). The architecture is based on [Convolutional Neural Network](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) (CNN) which is one of the applications of [Deep Learning](https://en.wikipedia.org/wiki/Deep_learning).


#### How to install
-------------------

There are different ways the package can be installed: 
* By clonning this repository and running the following command in the terminal (you might require <kbd>sudo</kbd> privilege)
```bash
git clone https://github.com/Radonirinaunimi/Style-Transfer
cd Style-Transfer/
python setup.py install --user
```
* By installing it through the Python Package Index (PyPI)
```bash
pip install timst --upgrade
```

#### How to use
---------------

To use **timst**, just run the following:
```bash
timst -i [IMAGE_TO_BE_STYLED] -s [STYLE_TO_BE_APPLIED] [-n NUMBER_OF_ITERATIONS]
```

#### For bugs and feature request
---------------------------------

Open an [issue](https://github.com/Radonirinaunimi/Style-Transfer/issues/new/choose) or a [pull request](https://github.com/Radonirinaunimi/Style-Transfer/compare).