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.


Code and Datasets from the paper "Self-supervised contrastive learning for volcanic unrest detection from InSAR data"

Code and Datasets from the paper "Self-supervised contrastive learning for volcanic unrest detection from InSAR data" You can download the pretrained

Bountos Nikos 3 May 07, 2022
Projecting interval uncertainty through the discrete Fourier transform

Projecting interval uncertainty through the discrete Fourier transform This repo

1 Mar 02, 2022
Easily benchmark PyTorch model FLOPs, latency, throughput, max allocated memory and energy consumption

⏱ pytorch-benchmark Easily benchmark model inference FLOPs, latency, throughput, max allocated memory and energy consumption Install pip install pytor

Lukas Hedegaard 21 Dec 22, 2022
Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

Kento Nishi 22 Jul 07, 2022
Official implementation of Rethinking Graph Neural Architecture Search from Message-passing (CVPR2021)

Rethinking Graph Neural Architecture Search from Message-passing Intro The GNAS can automatically learn better architecture with the optimal depth of

Shaofei Cai 48 Sep 30, 2022
Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Nikolas Petrou 1 Jan 13, 2022
AITom is an open-source platform for AI driven cellular electron cryo-tomography analysis.

AITom Introduction AITom is an open-source platform for AI driven cellular electron cryo-tomography analysis. AITom is originated from the tomominer l

93 Jan 02, 2023
FNet Implementation with TensorFlow & PyTorch

FNet Implementation with TensorFlow & PyTorch. TensorFlow & PyTorch implementation of the paper "FNet: Mixing Tokens with Fourier Transforms". Overvie

Abdelghani Belgaid 1 Feb 12, 2022
Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets

Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets (including obl

Azavea 1.7k Dec 22, 2022
CNN Based Meta-Learning for Noisy Image Classification and Template Matching

CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to

Kumar Manas 2 Dec 09, 2021
Code To Tune or Not To Tune? Zero-shot Models for Legal Case Entailment.

COLIEE 2021 - task 2: Legal Case Entailment This repository contains the code to reproduce NeuralMind's submissions to COLIEE 2021 presented in the pa

NeuralMind 13 Dec 16, 2022
A parallel framework for population-based multi-agent reinforcement learning.

MALib: A parallel framework for population-based multi-agent reinforcement learning MALib is a parallel framework of population-based learning nested

MARL @ SJTU 348 Jan 08, 2023
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 984 Dec 16, 2022
Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation

Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation By Qiang Zhou*, Zilong Huang*, Lichao Huang, Han Shen, Yon

Forest 117 Apr 01, 2022
Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains

Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains This is an accompanying repository to the ICAIL 2021 pap

4 Dec 16, 2021
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

Realtime Unsupervised Depth Estimation from an Image This is the caffe implementation of our paper "Unsupervised CNN for single view depth estimation:

Ravi Garg 227 Nov 28, 2022
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking We revisit and address issues with Oxford 5k and Paris 6k image retrieval benchm

Filip Radenovic 188 Dec 17, 2022
This is a Python Module For Encryption, Hashing And Other stuff

EnroCrypt This is a Python Module For Encryption, Hashing And Other Basic Stuff You Need, With Secure Encryption And Strong Salted Hashing You Can Do

5 Sep 15, 2022
Artstation-Artistic-face-HQ Dataset (AAHQ)

Artstation-Artistic-face-HQ Dataset (AAHQ) Artstation-Artistic-face-HQ (AAHQ) is a high-quality image dataset of artistic-face images. It is proposed

onion 105 Dec 16, 2022
Pytorch implementation for DFN: Distributed Feedback Network for Single-Image Deraining.

DFN:Distributed Feedback Network for Single-Image Deraining Abstract Recently, deep convolutional neural networks have achieved great success for sing

6 Nov 05, 2022