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.


Multi-task head pose estimation in-the-wild

Multi-task head pose estimation in-the-wild We provide C++ code in order to replicate the head-pose experiments in our paper https://ieeexplore.ieee.o

Roberto Valle 26 Oct 06, 2022
Pytorch implementation of SimSiam Architecture

SimSiam-pytorch A simple pytorch implementation of Exploring Simple Siamese Representation Learning which is developed by Facebook AI Research (FAIR)

Saeed Shurrab 1 Oct 20, 2021
Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021.

NL-CSNet-Pytorch Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021. Note: this repo only shows the strategy of

WenxueCui 7 Nov 07, 2022
Code for paper: Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks

Group-CAM By Zhang, Qinglong and Rao, Lu and Yang, Yubin [State Key Laboratory for Novel Software Technology at Nanjing University] This repo is the o

zhql 98 Nov 16, 2022
Python Fanduel API (2021) - Lineup Automation

Southpaw is a python package that provides access to the Fanduel API. Optimize your DFS experience by programmatically updating your lineups, analyzin

Brandin Canfield 13 Jan 04, 2023
Pipeline code for Sequential-GAM(Genome Architecture Mapping).

Sequential-GAM Pipeline code for Sequential-GAM(Genome Architecture Mapping). mapping whole_preprocess.sh include the whole processing of mapping. usa

3 Nov 03, 2022
Multiview 3D object detection on MultiviewC dataset through moft3d.

Multiview Orthographic Feature Transformation for 3D Object Detection Multiview 3D object detection on MultiviewC dataset through moft3d. Introduction

Jiahao Ma 20 Dec 21, 2022
A novel framework to automatically learn high-quality scanning of non-planar, complex anisotropic appearance.

appearance-scanner About This repository is an implementation of the neural network proposed in Free-form Scanning of Non-planar Appearance with Neura

Xiaohe Ma 14 Oct 18, 2022
A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.

sne4onnx A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or

Katsuya Hyodo 10 Aug 30, 2022
Transfer style api - An API to use with Tranfer Style App, where you can use two image and transfer the style

Transfer Style API It's an API to use with Tranfer Style App, where you can use

Brian Alejandro 1 Feb 13, 2022
High-Resolution Image Synthesis with Latent Diffusion Models

Latent Diffusion Models arXiv | BibTeX High-Resolution Image Synthesis with Latent Diffusion Models Robin Rombach*, Andreas Blattmann*, Dominik Lorenz

CompVis Heidelberg 5.6k Dec 30, 2022
Wandb-predictions - WANDB Predictions With Python

WANDB API CI/CD Below we capture the CI/CD scenarios that we would expect with o

Anish Shah 6 Oct 07, 2022
JAX bindings to the Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT) library

JAX bindings to FINUFFT This package provides a JAX interface to (a subset of) the Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT) lib

Dan Foreman-Mackey 32 Oct 15, 2022
Implementation of the SUMO (Slim U-Net trained on MODA) model

SUMO - Slim U-Net trained on MODA Implementation of the SUMO (Slim U-Net trained on MODA) model as described in: TODO: add reference to paper once ava

6 Nov 19, 2022
Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

Rbc-traps-generative-design - The generative design for single red clood cell hydrodynamic traps using GEFEST framework

Natural Systems Simulation Lab 4 Jun 16, 2022
Trafffic prediction analysis using hybrid models - Machine Learning

Hybrid Machine learning Model Clone the Repository Create a new Directory as assests and download the model from the below link Model Link To Start th

1 Feb 08, 2022
Code for the paper "Implicit Representations of Meaning in Neural Language Models"

Implicit Representations of Meaning in Neural Language Models Preliminaries Create and set up a conda environment as follows: conda create -n state-pr

Belinda Li 39 Nov 03, 2022
Multi-Scale Geometric Consistency Guided Multi-View Stereo

ACMM [News] The code for ACMH is released!!! [News] The code for ACMP is released!!! About ACMM is a multi-scale geometric consistency guided multi-vi

Qingshan Xu 118 Jan 04, 2023
[CoRL 21'] TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo

TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo Lukas Koestler1*    Nan Yang1,2*,†    Niclas Zeller2,3    Daniel Cremers1

TUM Computer Vision Group 744 Jan 04, 2023
PyTorch implementation for our AAAI 2022 Paper "Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning"

deepGCFX PyTorch implementation for our AAAI 2022 Paper "Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning" Pr

Thilini Cooray 4 Aug 11, 2022