Neon: an add-on for Lightbulb making it easier to handle component interactions

Overview

Neon

Neon is an add-on for Lightbulb making it easier to handle component interactions.

Installation

pip install git+https://github.com/neonjonn/lightbulb-neon.git

Documentation

ReadTheDocs

Usage

class Menu(neon.ComponentMenu):
    @neon.button("earth", "earth_button", hikari.ButtonStyle.SUCCESS, emoji="\N{DECIDUOUS TREE}")
    async def earth(self, button: neon.Button) -> None:
        await self.edit_msg(f"{button.emoji} - {button.custom_id}")

    @neon.option("Water", "water", emoji="\N{DROPLET}")
    @neon.option("Fire", "fire", emoji="\N{FIRE}")
    @neon.select_menu("sample_select_menu", "Pick fire or water!")
    async def select_menu_test(self, values: list) -> None:
        await self.edit_msg(f"You chose: {values[0]}!")

    @neon.button("Wind", "wind_button", hikari.ButtonStyle.PRIMARY, emoji="\N{WIND BLOWING FACE}\N{VARIATION SELECTOR-16}")
    @neon.button("Rock", "rock_button", hikari.ButtonStyle.SECONDARY, emoji="\N{MOYAI}")
    @neon.button_group()
    async def wind_rock(self, button: neon.Button) -> None:
        await self.edit_msg(f"You pressed: {button.custom_id}")

    @neon.on_timeout(disable_components=True)
    async def on_timeout(self) -> None:
        await self.edit_msg("\N{ALARM CLOCK} Timed out!")

@bot.command
@lightbulb.command("neon", "Check out Neon's component builder!")
@lightbulb.implements(lightbulb.SlashCommand, lightbulb.PrefixCommand)
async def neon_command(ctx: lightbulb.Context) -> None:
    menu = Menu(ctx, timeout=30)
    resp = await ctx.respond("Bar", components=menu.build())
    await menu.run(resp)

Contributing

If you wish to contribute to this project, please open an issue first to describe your issue or feature request.

As soon as you've done that you may make a pull request, and I'll review your changes.

Contributors

Owner
Neon Jonn
Neon Jonn
Repository for XLM-T, a framework for evaluating multilingual language models on Twitter data

This is the XLM-T repository, which includes data, code and pre-trained multilingual language models for Twitter. XLM-T - A Multilingual Language Mode

Cardiff NLP 112 Dec 27, 2022
Simple-System-Convert--C--F - Simple System Convert With Python

Simple-System-Convert--C--F REQUIREMENTS Python version : 3 HOW TO USE Run the c

Jonathan Santos 2 Feb 16, 2022
Extremely simple and fast extreme multi-class and multi-label classifiers.

napkinXC napkinXC is an extremely simple and fast library for extreme multi-class and multi-label classification, that focus of implementing various m

Marek Wydmuch 43 Nov 14, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Master Docs License Apache MXNet (incubating) is a deep learning framework designed for both efficiency an

ROCm Software Platform 29 Nov 16, 2022
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

dm_control: DeepMind Infrastructure for Physics-Based Simulation. DeepMind's software stack for physics-based simulation and Reinforcement Learning en

DeepMind 3k Dec 31, 2022
Moer Grounded Image Captioning by Distilling Image-Text Matching Model

Moer Grounded Image Captioning by Distilling Image-Text Matching Model Requirements Python 3.7 Pytorch 1.2 Prepare data Please use git clone --recurse

YE Zhou 60 Dec 16, 2022
An 16kHz implementation of HiFi-GAN for soft-vc.

HiFi-GAN An 16kHz implementation of HiFi-GAN for soft-vc. Relevant links: Official HiFi-GAN repo HiFi-GAN paper Soft-VC repo Soft-VC paper Example Usa

Benjamin van Niekerk 42 Dec 27, 2022
An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

MetaICL: Learning to Learn In Context This includes an original implementation of "MetaICL: Learning to Learn In Context" by Sewon Min, Mike Lewis, Lu

Meta Research 141 Jan 07, 2023
source code the paper Fast and Robust Iterative Closet Point.

Fast-Robust-ICP This repository includes the source code the paper Fast and Robust Iterative Closet Point. Authors: Juyong Zhang, Yuxin Yao, Bailin De

yaoyuxin 320 Dec 28, 2022
Self Driving RC Car Code

Derp Learning Derp Learning is a Python package that collects data, trains models, and then controls an RC car for track racing. Hardware You will nee

Not Karol 39 Dec 07, 2022
A Python library for unevenly-spaced time series analysis

traces A Python library for unevenly-spaced time series analysis. Why? Taking measurements at irregular intervals is common, but most tools are primar

Datascope Analytics 516 Dec 29, 2022
Pytorch implementation of paper "Efficient Nearest Neighbor Language Models" (EMNLP 2021)

Pytorch implementation of paper "Efficient Nearest Neighbor Language Models" (EMNLP 2021)

Junxian He 57 Jan 01, 2023
Any-to-any voice conversion using synthetic specific-speaker speeches as intermedium features

MediumVC MediumVC is an utterance-level method towards any-to-any VC. Before that, we propose SingleVC to perform A2O tasks(Xi → Ŷi) , Xi means utter

谷下雨 47 Dec 25, 2022
EMNLP 2020 - Summarizing Text on Any Aspects

Summarizing Text on Any Aspects This repo contains preliminary code of the following paper: Summarizing Text on Any Aspects: A Knowledge-Informed Weak

Bowen Tan 35 Nov 14, 2022
Implementation of our NeurIPS 2021 paper "A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs".

PPO-BiHyb This is the official implementation of our NeurIPS 2021 paper "A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Grap

<a href=[email protected]"> 66 Nov 23, 2022
TensorFlow port of PyTorch Image Models (timm) - image models with pretrained weights.

TensorFlow-Image-Models Introduction Usage Models Profiling License Introduction TensorfFlow-Image-Models (tfimm) is a collection of image models with

Martins Bruveris 227 Dec 20, 2022
Open-L2O: A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms

Open-L2O This repository establishes the first comprehensive benchmark efforts of existing learning to optimize (L2O) approaches on a number of proble

VITA 161 Jan 02, 2023
In this work, we will implement some basic but important algorithm of machine learning step by step.

WoRkS continued English 中文 Français Probability Density Estimation-Non-Parametric Methods(概率密度估计-非参数方法) 1. Kernel / k-Nearest Neighborhood Density Est

liziyu0104 1 Dec 30, 2021
Contrastively Disentangled Sequential Variational Audoencoder

Contrastively Disentangled Sequential Variational Audoencoder (C-DSVAE) Overview This is the implementation for our C-DSVAE, a novel self-supervised d

Junwen Bai 35 Dec 24, 2022
Implementations of LSTM: A Search Space Odyssey variants and their training results on the PTB dataset.

An LSTM Odyssey Code for training variants of "LSTM: A Search Space Odyssey" on Fomoro. Check out the blog post. Training Install TensorFlow. Clone th

Fomoro AI 95 Apr 13, 2022