Make Watson Assistant send messages to your Discord Server

Overview

Make Watson Assistant send messages to your Discord Server

Prerequisites

  1. Sign up for an IBM Cloud account.
  2. Fill in the required information and press the „Create Account“ button.
  3. After you submit your registration, you will receive an e-mail from the IBM Cloud team with details about your account. In this e-mail, you will need to click the link provided to confirm your registration.
  4. Now you should be able to login to your new IBM Cloud account ;-)
  5. Create a Discord account, as well your own Discord server (both are free of charge).

Activate Webhooks in Discord

We want to enable webhooks in our Discord server's settings, which will be used by Watson Assistant to send messages.

  1. Go to your server's settings
  2. Navigate to Integrations
  3. Create a new Webhook, and copy its URL

Note: Discord does not require any additional Authentification, which means that anyone who has the URL can use the Webhook. Ensure that only you, and people you trust have access to it.

Set up your cloud function

Create cloud function

We want to set up a cloud function, which Watson Assistant will be able to access. To do that, you need to go to your IBM Cloud Dashboard, and select Functions.

Alternatively you can click here to access the IBM Cloud functions.

Now you can create a new Action. Give it a sensible name, select python as your runtime, and click create.

Create Cloud Function Action

Paste in the code that can be found here, change the value of discordurl to your URL, and save your changes.

Test cloud function

If you want to test it, you can click on Invoke with parameter, paste in the input below, click apply, and press Invoke.

{
    "content" : "this is a test message sent by your cloud function"
}

If the message was sent successfully, the result should look like this.

Enable as Web Action

Now we need to create an endpoint, which will be used by Watson Assistant to invoce your function.

On the left side, click Endpoints and check the box called Enable as Web Action. Press save, and copy the URL.

Set up your Assistant

Set up Watson Assistant

Go back to your Dashboard, and type Watson Assistant into the search bar. If you already have a Watson Assistant service you can use it, otherwise you can create a free lite version either by clicking Watson Assistant under the Catalog Results Section or following this link.

Create your own Skill

Afterwards launch your Watson Assistant Service, and look for Skills on the left.

If you can't find it, click on the profile icon in the upper right corner, and click Switch to classic experience.

Create a new skill, select Dialog skill and click next. Select Upload skill and provide the skill-Connect-to-Discord.json file.

Enable Webhooks

Before you can test your assistant, you need to provide the cloud funtion's URL.

Click on Options->Webhooks, paste in the URL, and ADD A .json AT THE END.

We could use Discord's webhook link direcly, without adding a .json, and it would send the message as well. However, Discord doesn't return anything (that Watson Assistant can understand), which would prevent us from informing the user of our assistant, that the message was sent correctly.

Test your assistant

Now you can click on the Try it button and test whether the assistant is working correctly.


This codebase proposes modular light python and pytorch implementations of several LiDAR Odometry methods

pyLiDAR-SLAM This codebase proposes modular light python and pytorch implementations of several LiDAR Odometry methods, which can easily be evaluated

Kitware, Inc. 208 Dec 16, 2022
网络协议2天集训

网络协议2天集训 抓包工具安装 Wireshark wireshark下载地址 Tcpdump CentOS yum install tcpdump -y Ubuntu apt-get install tcpdump -y k8s抓包测试环境 查看虚拟网卡veth pair 查看

120 Dec 12, 2022
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing

FairEdit Relevent Publication FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing

5 Feb 04, 2022
Implements pytorch code for the Accelerated SGD algorithm.

AccSGD This is the code associated with Accelerated SGD algorithm used in the paper On the insufficiency of existing momentum schemes for Stochastic O

205 Jan 02, 2023
Official implementation for Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020

Likelihood-Regret Official implementation of Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020. T

Xavier 33 Oct 12, 2022
SIR model parameter estimation using a novel algorithm for differentiated uniformization.

TenSIR Parameter estimation on epidemic data under the SIR model using a novel algorithm for differentiated uniformization of Markov transition rate m

The Spang Lab 4 Nov 30, 2022
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling

large-scale-ITE-UM-benchmark This repository contains code and data to reproduce the results of the paper "A Large Scale Benchmark for Individual Trea

10 Nov 19, 2022
Code for ACL'2021 paper WARP 🌀 Word-level Adversarial ReProgramming

Code for ACL'2021 paper WARP 🌀 Word-level Adversarial ReProgramming. Outperforming `GPT-3` on SuperGLUE Few-Shot text classification.

YerevaNN 75 Nov 06, 2022
Kaggle | 9th place single model solution for TGS Salt Identification Challenge

UNet for segmenting salt deposits from seismic images with PyTorch. General We, tugstugi and xuyuan, have participated in the Kaggle competition TGS S

Erdene-Ochir Tuguldur 276 Dec 20, 2022
Hyperbolic Hierarchical Clustering.

Hyperbolic Hierarchical Clustering (HypHC) This code is the official PyTorch implementation of the NeurIPS 2020 paper: From Trees to Continuous Embedd

HazyResearch 154 Dec 15, 2022
PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021.

GCResNet PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021. The code will

11 May 19, 2022
Dilated Convolution with Learnable Spacings PyTorch

Dilated-Convolution-with-Learnable-Spacings-PyTorch Ismail Khalfaoui Hassani Dilated Convolution with Learnable Spacings (abbreviated to DCLS) is a no

15 Dec 09, 2022
Hunt down social media accounts by username across social networks

Hunt down social media accounts by username across social networks Installation | Usage | Docker Notes | Contributing Installation # clone the repo $

1 Dec 14, 2021
This repository provides the official implementation of 'Learning to ignore: rethinking attention in CNNs' accepted in BMVC 2021.

inverse_attention This repository provides the official implementation of 'Learning to ignore: rethinking attention in CNNs' accepted in BMVC 2021. Le

Firas Laakom 5 Jul 08, 2022
Motion and Shape Capture from Sparse Markers

MoSh++ This repository contains the official chumpy implementation of mocap body solver used for AMASS: AMASS: Archive of Motion Capture as Surface Sh

Nima Ghorbani 135 Dec 23, 2022
This is a repository of our model for weakly-supervised video dense anticipation.

Introduction This is a repository of our model for weakly-supervised video dense anticipation. More results on GTEA, Epic-Kitchens etc. will come soon

2 Apr 09, 2022
SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]

SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021] Pdf: https://openreview.net/forum?id=v5gjXpmR8J Code for our ICLR 2021 pape

Princeton INSPIRE Research Group 113 Nov 27, 2022
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

This is a playground for pytorch beginners, which contains predefined models on popular dataset. Currently we support mnist, svhn cifar10, cifar100 st

Aaron Chen 2.4k Dec 28, 2022
List of content farm sites like g.penzai.com.

内容农场网站清单 Google 中文搜索结果包含了相当一部分的内容农场式条目,比如「小 X 知识网」「小 X 百科网」。此种链接常会 302 重定向其主站,页面内容为自动生成,大量堆叠关键字,揉杂一些爬取到的内容,完全不具可读性和参考价值。 尤为过分的是,该类网站可能有成千上万个分身域名被 Goog

WDMPA 541 Jan 03, 2023
Very deep VAEs in JAX/Flax

Very Deep VAEs in JAX/Flax Implementation of the experiments in the paper Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on I

Jamie Townsend 42 Dec 12, 2022