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· 12 min read

Building a Vue 3 Chat App with vue-advanced-chat

In this tutorial, we will learn how to build a Vue.js chat app using the ChatKitty API and vue-advanced-chat.

vue-advanced-chat is a feature-rich and highly customizable Vue chat component library designed to simplify the process of building modern, real-time chat applications. It provides a wide range of out-of-the-box features, including:

  • Direct messaging and group chats
  • Images, videos, files, voice messages, emojis and link previews
  • Tag users & emojis shortcut suggestions
  • Typing indicators
  • Reactions with emojis and GIFs
  • Markdown text formatting - bold, italic, strikethrough, underline, code, multiline, etc.
  • Online presence indicators for online/offline users' status
  • Delivery and read receipts
  • Theming and customization options including light and dark theme modes
  • Responsive design perfect for mobile

In addition, vue-advanced-chat is compatible with all Javascript frameworks (Vue, Angular, React, etc.) or no framework at all as a web component.

By using vue-advanced-chat alongside a chat API service like ChatKitty, developers can quickly create chat applications with minimal boilerplate code, while also benefiting from a user-friendly and customizable UI.

This library helps reduce the time and effort required to implement chat functionalities, allowing developers to focus on other aspects of their application. Moreover, its responsive design ensures the chat interface adapts to different screen sizes, making it suitable for web and mobile applications alike.

Prerequisites

Before you start, make sure you have the following installed:

tip

You can check out our working demo any time on Stackblitz.

We'll begin by setting up the project and installing the necessary dependencies.

Setting up the project

Create a new Vue.js project using the Vue CLI by running the following command:

npm init vue@latest

This command will install and execute the official Vue project scaffolding tool.

Next, navigate to the newly created project folder:

cd chatkitty-vue-example

Install dependencies

Install the required packages using npm:

npm install @chatkitty/core vue-advanced-chat

Integrating the ChatKitty service

Implementing date utility functions

First, in the src create a new utils directory with a dates.ts file, add helper functions to parse and process ISO date-times returned by ChatKitty. The zeroPad function pads a number with leading zeros, while the isSameDay function checks if two dates are on the same day. The parseTimestamp function formats the timestamp based on the specified format, and the formatTimestamp function formats the timestamp based on whether it is on the same day as the current date.

src/utils/dates.ts
const zeroPad = (num: number, pad: number) => {
return String(num).padStart(pad, '0')
}

const isSameDay = (d1: Date, d2: Date) => {
return (
d1.getFullYear() === d2.getFullYear() &&
d1.getMonth() === d2.getMonth() &&
d1.getDate() === d2.getDate()
)
}

export const parseTimestamp = (timestamp: string, format = '') => {
if (!timestamp) return

const date = new Date(timestamp)

if (format === 'HH:mm') {
return `${zeroPad(date.getHours(), 2)}:${zeroPad(date.getMinutes(), 2)}`
} else if (format === 'DD MMMM YYYY') {
const options: Intl.DateTimeFormatOptions = { month: 'long', year: 'numeric', day: 'numeric' }

return `${new Intl.DateTimeFormat('en-GB', options).format(date)}`
} else if (format === 'DD/MM/YY') {
const options: Intl.DateTimeFormatOptions = {
month: 'numeric',
year: 'numeric',
day: 'numeric'
}

return `${new Intl.DateTimeFormat('en-GB', options).format(date)}`
} else if (format === 'DD MMMM, HH:mm') {
const options: Intl.DateTimeFormatOptions = { month: 'long', day: 'numeric' }

return `${new Intl.DateTimeFormat('en-GB', options).format(date)}, ${zeroPad(
date.getHours(),
2
)}:${zeroPad(date.getMinutes(), 2)}`
}

return date
}

export const formatTimestamp = (date: Date, timestamp: string) => {
const timestampFormat = isSameDay(date, new Date()) ? 'HH:mm' : 'DD/MM/YY'
const result = parseTimestamp(timestamp, timestampFormat)

return timestampFormat === 'HH:mm' ? `Today, ${result}` : result
}

Implementing service functions

Next, create a folder named chatkitty inside the src folder and create an index.ts file within it. Import the ChatKitty package and initialize the ChatKitty instance with your API key:

src/chatkitty/index.ts
import ChatKitty from '@chatkitty/core';

export const chatkitty = ChatKitty.getInstance('YOUR_CHATKITTY_API_KEY');
note

Replace 'YOUR_CHATKITTY_API_KEY' with your actual API key.

In the src/chatkitty/index.ts file, we'll implement various functions for interacting with the ChatKitty API. These functions will handle entering and exiting chat rooms, fetching messages and rooms, user authentication, and sending messages.

Let's start by implementing functions to map ChatKitty users, channels, and messages into vue-advanced-chat objects.

  • Implement the mapUser function to map a ChatKitty user to a vue-advanced-chat user
const mapUser = (user: User) => ({
_id: user.name,
username: user.displayName,
avatar: user.displayPictureUrl,
status: {
state: user.presence.online ? 'online' : 'offline'
},
_user: user
})
  • Implement the mapMessage function to map a ChatKitty message to a vue-advanced-chat message
const mapMessage = (message: Message, timeFormat?: string) => ({
_id: message.id,
content: message.type === 'TEXT' || message.type === 'SYSTEM_TEXT' ? message.body : undefined,
senderId: message.type === 'TEXT' || message.type === 'FILE' ? message.user.name : 'system',
username:
message.type === 'TEXT' || message.type === 'FILE' ? message.user.displayName : 'System',
timestamp: parseTimestamp(message.createdTime, timeFormat || 'HH:mm'),
date: parseTimestamp(message.createdTime, 'DD MMMM YYYY'),
_message: message
})
  • Implement the mapChannel function to map a ChatKitty channel to a vue-advanced-chat room
const mapChannel = async (user: CurrentUser, channel: Channel) => ({
roomId: channel.id,
roomName:
channel.type === 'DIRECT'
? channel.members
.filter((member) => member.id !== user.id)
.map((member) => member.displayName)
.join(', ')
: channel.name,
users:
channel.type === 'DIRECT'
? channel.members.map((member) => mapUser(member))
: await (async () => {
const result = await chatkitty.listChannelMembers({ channel })

if (result.succeeded) {
return result.paginator.items.map((member) => mapUser(member))
}

return []
})(),
lastMessage:
channel.lastReceivedMessage && mapMessage(channel.lastReceivedMessage, 'DD MMMM, HH:mm'),
_channel: channel,
_messages_paginator: null,
_chat_session: null
})
  • Implement the login function to authenticate a user and start a ChatKitty session.
export const login = async (username: string) => {
await chatkitty.startSession({ username })
};
  • Implement the enterRoom function to enter a chat room and start a chat session.
export const enterRoom = async ({
room,
onMessageReceived,
onRoomUpdated,
}: {
room: any;
onMessageReceived: (message: any) => void;
onRoomUpdated: (room: any) => void;
}) => {
const result = await chatkitty.startChatSession({
channel: room._channel,
onMessageReceived: (message) => {
const mapped = mapMessage(message)

room.lastMessage = mapped

onMessageReceived(mapped)
onRoomUpdated(room)
}
})

if (result.succeeded) {
room._chat_session = result.session
}
};
  • Implement the exitRoom function to exit a chat room and end the chat session.
export const exitRoom = (room: any) => {
room._chat_session?.end?.()

room._messages_paginator = null
room._chat_session = null
};
  • Implement the fetchMessages function to fetch messages for a chat room.
export const fetchMessages = async (room: any) => {
if (room._messages_paginator) {
const items = room._messages_paginator.items
.map((message: Message) => mapMessage(message))
.reverse()

const hasMore = room._messages_paginator.hasNextPage

if (hasMore) {
room._messages_paginator = await room._messages_paginator.nextPage()
}

return { items, hasMore }
}

const result = await chatkitty.listMessages({ channel: room._channel })

if (result.succeeded) {
const items = result.paginator.items.map((message) => mapMessage(message)).reverse()

const hasMore = result.paginator.hasNextPage

if (hasMore) {
room._messages_paginator = await result.paginator.nextPage()
}

return { items, hasMore }
}

return { items: [], hasMore: false }
};
  • Implement the fetchRooms function to fetch a list of chat rooms.
export const fetchRooms = async () => {
const user = chatkitty.currentUser

if (!user) {
return []
}

const result = await chatkitty.listChannels({ filter: { joined: true } })

if (result.succeeded) {
return await Promise.all(
result.paginator.items.map(async (channel) => await mapChannel(user, channel))
)
}

return []
};
  • Implement the sendMessage function to send a message to a chat room.
export const sendMessage = async ({ room, content }: any) => {
if (content) {
await chatkitty.sendMessage({ channel: room._channel, body: content })
}
};
  • Implement the logout function to end the ChatKitty session.
export const logout = async () => {
await chatkitty.endSession()
};
note

Refer to the provided code example for the implementation details.

Creating the Chat component

Create a new component directory in src and create a Vue component called ChatComponent.vue inside. In the <script> section of the component, import the ChatKitty service functions and the vue-advanced-chat package. Register the vue-advanced-chat web component before using it in the <template> section.

  • In the src/components/ChatComponent.vue file, import the ChatKitty service functions and the vue-advanced-chat package.
<script setup lang="ts">
import * as chatService from '@/chatkitty';
import { register } from 'vue-advanced-chat';

register();
// ... implementation details
</script>


  • Define the props for the ChatComponent component. These props include theme and username.
const props = defineProps<{
theme: string;
username: string;
}>();
  • Create reactive references for rooms, roomsLoaded, loadingRooms, messages, messagesLoaded, and currentRoom. These references will be used to store and manage the state of the chat component.
const rooms: Ref<any[]> = ref([]);
const roomsLoaded = ref(false);
const loadingRooms = ref(false);

const messages: Ref<any[]> = ref([]);
const messagesLoaded = ref(false);

const currentRoom = ref(null);
  • Implement the setup function to authenticate the user and fetch chat rooms.
const setup = async (username: string) => {
await chatService.login(username);

loadingRooms.value = true;
rooms.value = await chatService.fetchRooms();
loadingRooms.value = false;

roomsLoaded.value = true;
};
  • Implement the fetchMessages function to fetch messages for the selected chat room.
const fetchMessages = async ({ room, options = {} }: any) => {
// ... implementation details
};
  • Implement the sendMessage function to send a message to the current chat room.
const sendMessage = ({ content }: any) => {
chatService.sendMessage({ room: currentRoom.value, content });
};
  • Implement the tearDown function to log out the user and reset the state of the chat component.
const tearDown = async () => {
await chatService.logout();

rooms.value = [];
roomsLoaded.value = false;
loadingRooms.value = false;

messages.value = [];
messagesLoaded.value = false;
};
  • Use the onMounted lifecycle hook to call the setup function when the component is mounted. Also, use the watch function to watch for changes in the username prop and update the chat component accordingly.
onMounted(async () => {
await setup(props.username);

watch(
() => props.username,
async (username) => {
await tearDown();
await setup(username);
}
);
});
  • Define the template for the ChatComponent component. Use the vue-advanced-chat component and pass the necessary props and event listeners.
<template>
<vue-advanced-chat
height="calc(100vh - 100px)"
:current-user-id="username"
:theme="theme"
:loading-rooms="loadingRooms"
:rooms-loaded="roomsLoaded"
:messages-loaded="messagesLoaded"
:single-room="false"
:show-search="false"
:show-add-room="false"
:show-files="false"
:show-audio="false"
:show-emojis="false"
:show-reaction-emojis="false"
.rooms="rooms"
.messages="messages"
@fetch-messages="fetchMessages($event.detail[0])"
@send-message="sendMessage($event.detail[0])"
/>
</template>
note

Refer to the provided code example for the implementation details.

Integrating the chat component into the main app

Open the src/App.vue file and import the ChatComponent Vue component. Add the ChatComponent to the main app template and pass the necessary props, such as the theme and username.

  • Open the src/App.vue file and import the ChatComponent.
import ChatComponent from './components/ChatComponent.vue';
  • Create reactive references for theme and username. These references will be used to store the selected theme and username.
const theme = ref('light');
const username = ref(users[Math.floor(Math.random() * users.length

)].username);
  • Define an array of users with their usernames and names. This array will be used to populate the user selection dropdown.
const users = [
{
username: 'b2a6da08-88bf-4778-b993-7234e6d8a3ff',
name: 'Joni'
},
{
username: 'abc4264d-f1b1-41c0-b4cc-1e9daadfc893',
name: 'Penelope'
},
{
username: 'c6f75947-af48-4893-a78e-0e0b9bd68580',
name: 'Julie'
},
{
username: '2989c53a-d0c5-4222-af8d-fbf7b0c74ec6',
name: 'Paxton'
},
{
username: '8fadc920-f3e6-49ff-9398-1e58b3dc44dd',
name: 'Zaria'
}
];
note

You can create chat users for your app using the ChatKitty Platform API

  • Define the template for the App.vue component. Add the user selection dropdown, theme selection buttons, and the ChatComponent.
<template>
<div class="app-container" :class="{ 'app-container-dark': theme === 'dark' }">
<span class="user-logged" :class="{ 'user-logged-dark': theme === 'dark' }">
Logged in as
</span>
<select v-model="username">
<option v-for="user in users" :key="user.username" :value="user.username">
{{ user.name }}
</option>
</select>

<div class="button-theme">
<button class="button-light" @click="theme = 'light'">Light</button>
<button class="button-dark" @click="theme = 'dark'">Dark</button>
</div>

<ChatComponent :theme="theme" :username="username" />
</div>
</template>
note

Refer to the provided code example for the implementation details.

Styling the app

Add custom styling to the src/App.vue file to make the app look more polished. You can use the provided styling or create your own.

<style lang="css">
body {
margin: 0;
}

input {
-webkit-appearance: none;
}

.app-container {
font-family: 'Quicksand', sans-serif;
padding: 30px;

&.app-container-dark {
background: #131415;
}
}

.user-logged {
font-size: 12px;
margin-right: 5px;
margin-top: 10px;

&.user-logged-dark {
color: #fff;
}
}

select {
height: 20px;
outline: none;
border: 1px solid #e0e2e4;
border-radius: 4px;
background: #fff;
margin-bottom: 20px;
}

.button-theme {
float: right;
display: flex;
align-items: center;

.button-light {
background: #fff;
border: 1px solid #46484e;
color: #46484e;
}

.button-dark {
background: #1c1d21;
border: 1px solid #1c1d21;
}

button {
color: #fff;
outline: none;
cursor: pointer;
border-radius: 4px;
padding: 6px 12px;
margin-left: 10px;
border: none;
font-size: 14px;
transition: 0.3s;
vertical-align: middle;

&.button-github {
height: 30px;
background: none;
padding: 0;
margin-left: 20px;

img {
height: 30px;
}
}

&:hover {
opacity: 0.8;
}

&:active {
opacity: 0.6;
}

@media only screen and (max-width: 768px) {
padding: 3px 6px;
font-size: 13px;
}
}
}
</style>

Conclusion

In this tutorial, we built a simple Vue.js chat app using the ChatKitty API. We created a ChatKitty service to interact with the API, implemented a Chat component that uses the vue-advanced-chat package, and integrated the Chat component into the main app.

Now you have a fully functional chat app that supports direct messaging, group chat, message threads, and more. You can further customize the app by adding more features from the ChatKitty API or by modifying the UI components.

Happy chatting!

· 11 min read

Geometric pattern

In today's fast-changing world, businesses need to stay on top of their game when it comes to customer engagement. With the rise of AI and large language models, chatbots have become an indispensable tool for businesses looking to improve their customer engagement strategies. However, building and managing chat code can be time-consuming and complicated, which is why we are thrilled to announce our new product that allows businesses to integrate and customize complex chat experiences without any hassle.

ChatKitty, the easiest way to build deploy chat

When we founded ChatKitty in 2020, our goal was to create the easiest way to build chat for all types of businesses. We launched our REST API and JS SDKs to empower developers to build chat for online communication problems facing their users. However, as with all Chat API solutions currently in the market, it takes developers significant work and effort to integrate APIs and SDK function calls into their apps. Developers still need to read and understand deeply technical documentation, write a significant amount of code, and handle complex chat interactions. It also requires a lot of additional effort to integrate third-party functionality like emails, push notifications, SMS, and, yes, conversational AI chat -features that businesses building customer engagement platforms need.

We've begun building a comprehensive plug-and-play product that utilizes new technology to solve this problem in a unique and exciting way. We're building a server-side UI rendering engine that allows us to build UI and push it to user devices as HTML or native device views. Meaning we can build UI once, and our customers can use that UI in their apps without writing a single line of code. The "server-side frontend" product will produce UI for not only web apps but for native iOS and Android apps as well.

Opportunities for customer engagement conversational chat

ChatKitty is a chat company, and we recognize the opportunities AI-powered chat creates for our customers. One area where AI has proven to be particularly useful is in the development of AI assistants. AI assistants are computer programs that use natural language processing and machine learning algorithms to interact with users and provide assistance with various tasks. Here are some of the benefits of integrating AI assistants into applications:

Reading and answering questions

One of the most significant benefits of AI assistants is their ability to read and answer lead and customer inquiries. With advanced natural language processing capabilities, these assistants can understand and respond to user questions in seconds, providing instant solutions to their problems. This functionality is particularly useful in customer service and support, where timely and accurate responses are critical to customer satisfaction.

For example, by integrating a ChatGPT-powered chatbot into our demos, we have improved response times and provided potential customers with faster, more accurate solutions to their queries. Not only does this increase lead conversation, but it also reduces the workload on our lead generation team, allowing them to focus on more complex tasks.

Translation

Another significant benefit of AI assistants is their ability to translate languages. With the global nature of business today, it is essential to communicate with customers and clients in their language. By integrating translation functionality into our applications, we can communicate effectively with customers regardless of their location or language.

Summarization

Businesses can also use AI assistants to summarize large amounts of data quickly and efficiently. This functionality is handy for companies that deal with large volumes of data, such as market research or financial analysis. Automating the summarization process can save valuable time and resources, allowing us to focus on more critical tasks.

Customer support chatbots

Perhaps one of the most significant benefits of AI assistants is their ability to provide customer support through chatbots. Chatbots can handle a wide range of customer inquiries, from basic product information to complex technical support. By automating the customer support process, we can provide 24/7 support, improve response times, and reduce costs.

Because language models ChatGPT can remember and integrate new information without retraining, we can easily customize the models for our specific company and business domain.

Chatbots can provide 24/7 availability, allowing customers to get answers to their questions at any time of day or night, regardless of if human agents are available. By automating responses to common customer inquiries, conversational AI helps reduce the workload of human agents, allowing them to focus on more complex customer issues. Chatbots can provide a cost-effective alternative to hiring and training additional customer service representatives. They can handle multiple customer inquiries simultaneously, reducing the need for additional staff. Ultimately, businesses can provide quick and efficient answers to customer inquiries, increasing customer satisfaction and loyalty.

The current state of AI and large language models

I've been following the development of artificial neural networks for several years now. And the progress made by large language models like ChatGPT, GPT-3, and BERT over the past few years has been quite remarkable.

This breakthrough in the field of natural language processing opens up new opportunities for businesses building interactive user applications. By unlocking new abilities for language translation, chatbots, writing assistance, text summarization, and even code generation, language models like ChatGPT have the potential to transform the way we interact with non-human intelligent systems and reshape our society in ways not seen since the dawn of the internet, and the industrial revolution.

That ChatGPT can generate text so human-like may be unexpected given the way ChatGPT works. Like other language models in the Generative Pre-trained Transformer (GPT) family, ChatGPT uses a Transformer architecture. Transformer neural networks, first described by Google in 2017, are a type of deep learning architecture that has become increasingly popular in natural language processing tasks such as language translation and language modeling. Unlike traditional neural networks that process sequences of input data sequentially, Transformers are designed to process entire sequences of input data in parallel.

Before we can dive into the specifics of Transformers, we'll need to first discuss word embeddings.

Word embeddings

Word embedding is a very interesting concept; it's worth spending a bit of time understanding what word embeddings are and why they are so important for how current state-of-the-art language models work. Before a sequence of text can go through a neural network and be processed, its tokens must be converted to a representation the network is able to understand. Like all neural networks, transformer language models represent inputs, features, and outputs as vectors of numbers (more precisely as arrays of floats). A word embedding is simply an efficient representation of a word or token as a numerical vector of certain length that captures some "meaning" of that word relative to other words in a language. With word embeddings, words that are used in similar contexts are represented closer to each other, than words which tend to used in different contexts.

Each dimension of the vector represents a different feature or aspect of the word. Word embeddings are typically learned from large amounts of text data using algorithms like Word2Vec or GloVe. Once trained, these embeddings can be used as input to neural networks for a variety of natural language processing tasks.

Word embeddings can be used to compare the meaning of words in different contexts, and to identify relationships between words. For example, if we have a vector for "cat" and a vector for "dog", we can measure the distance between these two vectors to determine how similar they are semantically. We can expect that the vectors for "dog" and "cat" be very similar, near a vector for "pet", while the vector for "car" should be quite different. In addition to representing words, word embeddings can also represent phrases, and the relationships between them. This makes it easier for neural networks to learn patterns and relationships between words, and is particularly useful when it comes to natural language processing tasks such as sentiment analysis, question answering, and machine translation.

Note that strictly, GPT models does not deal with words, but rather with tokens, which approximate to words. A token here refers to common sequence of text characters that can easily be assigned a meaning. A token might be a whole word like "cat", or meaningful fragments like "ing", or "ly", or "ed". Tokens make it easier for neural networks to understand compound or non-standard words, and even other languages. After "tokenizing" the input text, an embedding algorithm like Word2Vec then converts these tokens into embeddings.

Transformers

The Transformer was introduced by Vaswani et al. in "Attention Is All You Need" and has quickly became one of the most popular architectures for NLP tasks. Language models are probability distributions over sequences of words (or tokens) in a language. This means their primary function is predicting the likelihood of a particular word being the next word for any sequence in the language.

For a language model to be good at predicting likely next words for a given sequence, it needs to be able to look back in the sequence to remember the context and the semantics of previously seen tokens, in other it needs memory. There are long-term dependencies of the beginning of meaningful sentences, at the end of a sentence. For example, to complete the sentence "Jack came to the pub to have a drink and I talk to ", we'd expect the next word in the sequence to be noun or a pronoun, "him", "her", etc.

However, the relevant piece of information is the word "Jack", all the way at the beginning of the sentence. A good language model needs to be able to remember "Jack", and return a probable next token associated with "Jack" in the language, "Jack came to the pub to have a drink and I talk to him". Without the ability to remember relevant context in a text sequence, the language model is likely to generate sentences that aren't meaningfully although syntactically valid (for example "Jack came to the pub to have a drink and I talk to John").

Attention

In other to learn from massive dataset efficiently, and generate sequences of text, language models need a way of deciding which parts of input sequences are more important, thereby assigning different weights to different parts of the input (as numeric vectors). By assigning greater weight to certain parts of the input, the model can be thought of as "paying attention" to those parts.

The key innovation of the Transformer architecture is its so-called "self-attention" mechanism. Self-attention works by computing a score between every pair of elements in the input sequence. The score is computed by multiplying the vectors representing the two elements. The model then uses the scores to determine which elements it should "pay attention".

In addition to self-attention, the Transformer architecture also uses an encoder-decoder architecture, which enables the model to learn from input sequences of varying lengths. The encoder reads in the input sequence one element at a time and produces a vector representation of the entire sequence. The decoder then uses this vector representation to generate the output.

Screenshot: Transformer architecture

Original Transformer Architecture.
Image credit: Kindra Cooper/"OpenAI GPT-3: Everything You Need to Know"

By using self-attention and an encoder-decoder architecture, Transformer models like ChatGPT are capable of learning from large datasets and generating coherent sequences of text.

Integrating ChatGPT and beyond into our chat platform

Last week, OpenAI introduced APIs for its ChatGPT and Whisper models, giving developers access to its state-of-the-art language and speech-to-text generative AI capabilities. The same day I was able to integrate ChatGPT as an AI assistant into an application powered by ChatKitty.

Screenshot: ChatGPT introduction

Screenshot: ChatGPT french poem

ChatKitty provides Chat Functions, an event-driven serverless framework for extending our platform to handles use-cases that even we can't imagine. With ChatGPT your AI assistant can help your users draft emails, write code, answer questions, translate messages, summarize chats, and much more.

I've written a guide to help developers get started integrating a ChatGPT powered AI assistant into apps.

Summary

I'm excited about the opportunities created by AI-powered chat, for customer engagement and communication. The progress made by large language models using the Transformer architecture like ChatGPT, GPT-3, and BERT is truly remarkable, unlocking new abilities for language translation, chatbots, writing assistance, text summarization, and code generation. Although current large language models are far from perfect, with the help of AI-powered chat conversation, businesses can communicate with customers in a more natural and personalized way, improving engagement and providing better customer service. Issues regard AI safety, potential biases, hallucinations and non-factual results need to be considered, but I'm confident that with continued advances in AI, machine learning and natural language processing, AI-powered chat will be a powerful tool for businesses to engage with customers and increase customer satisfaction.

· 20 min read

Building a Chat App with React Native and Gifted Chat (Part 4)

In this tutorial series, I'll be showing you how to build a functional and secure chat app using the latest React Native libraries, including Gifted Chat and the Expo framework, powered by the ChatKitty platform.


So far you learned how to use the Gifted Chat React Native library with ChatKitty's JavaScript SDK to build a full-featured chat screen with real-time messaging functionality into your app, adding screens for users to create public channels, discover new channels, and view their channels. In the third article of this series, you enhanced that chat experience by implementing in-app and push notifications to notify your users when chat events occur.

In this tutorial, you'll be building on the group chat experience you created adding direct messaging to allow users to communicate privately. You'll also be adding a few enhancements to the chat experience including typing indicators, and chat room presence notifications.

After reading this article, you will be able to:

  1. Create direct channels for users to chat privately

  2. Integrate Gifted Chat's in-built typing indicator and implement a custom more detailed indicator

  3. Notify chat users when a user enters or leaves the chat from a chat screen

  4. Allow users to leave chat channels they are no longer interested in

If you followed along the previous articles, you should already have the ChatKitty JavaScript SDK NPM package added to your Expo React Native project.


Before we begin, let's go over some terms we'll be using a lot in this article.

Direct channels

Previously, we learned about ChatKitty chat channels, specifically public channels that users can discover and join, or be invited to join. Direct channels, on the other hand, let users have private conversations between up to 9 other users. New users cannot be added to a direct channel and there can only exist one direct channel between a set of users. Direct channels are perfect for one-off conversations that don't require an entire channel to discuss.

Entering a chat

When a user starts a chat session and has no other active chat sessions in the session channel, the user has entered a chat. In other words, users who have entered a chat have at least one active chat session for that chat channel, and are active participants of the conversation. Active chat participants receive real-time messaging events, and are present to reply immediately.

Leaving a chat

After a user ends a chat session and has no other active chat sessions in the session channel, the user has left a chat. Users leave a chat when there is no longer at least one active chat session for that chat channel, and are no longer active participants of the conversation. After leaving a chat, users begin to get notifications of events that happened in the chat while they are away.

Leaving a channel

After a user joins a channel, the user becomes a channel member. Channel members can send messages in a channel, and receives messages and notifications related to the channel. If a user is no longer interested in a channel, the user can leave the channel and is no longer a channel member.

Okay, let's get started! 🏎️

Next, you'll be adding direct messaging functionality to your chat app.

Creating a direct messaging channel

Edit the chatScreen.js screen file you previous created to destructure its navigation prop:

export default function ChatScreen({ route, navigation /* Add this */ }) {
const { user } = useContext(AuthContext);
const { channel } = route.params;

// Unchanged
}

Now, you can customize your Gifted Chat message avatar to create or get a direct channel, and navigate the user to a new chat screen with the direct channel when it's pressed.

Define a new method renderAvatar to pass into your GiftedChat component:

import { Avatar, Bubble, GiftedChat } from 'react-native-gifted-chat';

// Unchanged

function renderAvatar(props) {
return (
<Avatar
{...props}
onPressAvatar={(avatarUser) => {
chatkitty
.createChannel({
type: 'DIRECT',
members: [{ id: avatarUser._id }]
})
.then((result) => {
navigation.navigate('Chat', { channel: result.channel });
});
}}
/>
);
}

Set the GiftedChat renderAvatar prop to the method you defined:

return (
<GiftedChat
messages={messages}
onSend={handleSend}
user={mapUser(user)}
loadEarlier={loadEarlier}
isLoadingEarlier={isLoadingEarlier}
onLoadEarlier={handleLoadEarlier}
renderBubble={renderBubble}
renderAvatar={renderAvatar} /* Add this */
/>
);

After these changes, chatScreen.js should look like this:

import React, { useContext, useEffect, useState } from 'react';
import { Avatar, Bubble, GiftedChat } from 'react-native-gifted-chat';

import { chatkitty } from '../chatkitty';
import Loading from '../components/loading';
import { AuthContext } from '../context/authProvider';

export default function ChatScreen({ route, navigation }) {
const { user } = useContext(AuthContext);
const { channel } = route.params;

const [messages, setMessages] = useState([]);
const [loading, setLoading] = useState(true);
const [loadEarlier, setLoadEarlier] = useState(false);
const [isLoadingEarlier, setIsLoadingEarlier] = useState(false);
const [messagePaginator, setMessagePaginator] = useState(null);

useEffect(() => {
const startChatSessionResult = chatkitty.startChatSession({
channel: channel,
onMessageReceived: (message) => {
setMessages((currentMessages) =>
GiftedChat.append(currentMessages, [mapMessage(message)])
);
}
});

chatkitty
.listMessages({
channel: channel
})
.then((result) => {
setMessages(result.paginator.items.map(mapMessage));

setMessagePaginator(result.paginator);
setLoadEarlier(result.paginator.hasNextPage);

setLoading(false);
});

return startChatSessionResult.session.end;
}, [user, channel]);

async function handleSend(pendingMessages) {
await chatkitty.sendMessage({
channel: channel,
body: pendingMessages[0].text
});
};

async function handleLoadEarlier() {
if (!messagePaginator.hasNextPage) {
setLoadEarlier(false);

return;
}

setIsLoadingEarlier(true);

const nextPaginator = await messagePaginator.nextPage();

setMessagePaginator(nextPaginator);

setMessages((currentMessages) =>
GiftedChat.prepend(currentMessages, nextPaginator.items.map(mapMessage))
);

setIsLoadingEarlier(false);
}

function renderBubble(props) {
return (
<Bubble
{...props}
wrapperStyle={{
left: {
backgroundColor: '#d3d3d3'
}
}}
/>
);
}

function renderAvatar(props) {
return (
<Avatar
{...props}
onPressAvatar={(avatarUser) => {
chatkitty
.createChannel({
type: 'DIRECT',
members: [{ id: avatarUser._id }]
})
.then((result) => {
navigation.navigate('Chat', { channel: result.channel });
});
}}
/>
);
}

if (loading) {
return <Loading />;
}

return (
<GiftedChat
messages={messages}
onSend={handleSend}
user={mapUser(user)}
loadEarlier={loadEarlier}
isLoadingEarlier={isLoadingEarlier}
onLoadEarlier={handleLoadEarlier}
renderBubble={renderBubble}
renderAvatar={renderAvatar}
/>
);
}

function mapMessage(message) {
return {
_id: message.id,
text: message.body,
createdAt: new Date(message.createdTime),
user: mapUser(message.user)
};
}

function mapUser(user) {
return {
_id: user.id,
name: user.displayName,
avatar: user.displayPictureUrl
};
}

If you run the app now and go to a public chat screen, you should see:

Screenshot: Public chat

Tapping a message avatar should take you to a new chat screen where you can have a direct private conversation.

ChatKitty doesn't expose unique names for direct channels, so we don't see a channel name in the app title bar. Let's create an appropriate name for the chat screen title.

Add a helper method channelDisplayName to the index.js file in the src/chatkitty/ directory:

export function channelDisplayName(channel) {
if (channel.type === 'DIRECT') {
return channel.members.map((member) => member.displayName).join(', ');
} else {
return channel.name;
}
}

After this change, index.js should look like this:

import ChatKitty from '@chatkitty/core';

export const chatkitty = ChatKitty.getInstance('YOUR CHATKITTY API KEY HERE');

export function channelDisplayName(channel) {
if (channel.type === 'DIRECT') {
return channel.members.map((member) => member.displayName).join(', ');
} else {
return channel.name;
}
}

You can now update your app to use a more readable channel display name.

Update homeStack.js in src/context to use the channelDisplayName method:

import { chatkitty, channelDisplayName } from '../chatkitty';

// Unchanged

<ChatStack.Screen
name='Chat'
component={ChatScreen}
options={({ route }) => ({
title: channelDisplayName(route.params.channel) /* Add this */
})}
/>;

Also update browseChannelsScreen.js and homeScreen.js in src/screens to use the helper method:

import { chatkitty, channelDisplayName } from '../chatkitty';

// Unchanged

<List.Item
title={channelDisplayName(item)} /* Add this */
// Unchanged
/>;

Running the app now shows the display names of a direct channel's members in the title bar

Screenshot: Direct chat name

Great! Now you can privately chat with other channel members. Now let's move on to enhancing your chat app's experience with a typing indicator.

Adding a typing indicator with Gifted Chat and ChatKitty

The Gifted Chat React Native library saves you a lot of time when creating a chat UI. By providing a bunch of component props, you can customize the chat UI and implement chat features like typing indicators, using a chat service like ChatKitty.

You'll be using the isTyping Gifted Chat prop to display a typing indicator when another user is typing.

Screenshot: Simple typing indicator partial

ChatKitty tracks the typing state of users sending typing keystrokes in a channel. You'll need to send typing keystrokes to ChatKitty to let it know when a user is typing. When starting a chat session, you can register handler methods with ChatKitty to handle chat events like when a user starts or stops typing, enters or leaves a chat, enters keystrokes, etc. You can track when a user starts and stops typing with ChatKitty handler methods, storing the current user typing in your component state. Using the typing user state, you can set Gifted Chat's isTyping prop to display the typing indicator.

Add a helper method handleInputTextChanged in chatScreen.js to send typing keystrokes to ChatKitty, so it can know when a user is typing:

function handleInputTextChanged(text) {
chatkitty.sendKeystrokes({
channel: channel,
keys: text
});
}

Next, on the GiftedChat component, set the onInputTextChanged prop to handleInputTextChanged.

return (
<GiftedChat
messages={messages}
onSend={handleSend}
user={mapUser(user)}
loadEarlier={loadEarlier}
isLoadingEarlier={isLoadingEarlier}
onLoadEarlier={handleLoadEarlier}
onInputTextChanged={handleInputTextChanged} /* Add this */
renderBubble={renderBubble}
renderAvatar={renderAvatar}
/>
);

ChatKitty is now able to know when your users start and stop typing.

Next, define a new state variable to track the current typing user.

export default function ChatScreen({ route, navigation }) {
const { user } = useContext(AuthContext);
const { channel } = route.params;

const [messages, setMessages] = useState([]);
const [loading, setLoading] = useState(true);
const [loadEarlier, setLoadEarlier] = useState(false);
const [isLoadingEarlier, setIsLoadingEarlier] = useState(false);
const [messagePaginator, setMessagePaginator] = useState(null);
const [typing, setTyping] = useState(null); /* Add this */

// Unchanged
}

You can now register chat event handlers to control the typing state. Register both onTypingStarted and onTypingStopped in ChatKitty.startChatSession to set the typing state.

useEffect(() => {
const startChatSessionResult = chatkitty.startChatSession({
channel: channel,
onMessageReceived: (message) => {
setMessages((currentMessages) =>
GiftedChat.append(currentMessages, [mapMessage(message)])
);
},
onTypingStarted: (typingUser) => { /* Add this */
if (typingUser.id !== user.id) {
setTyping(typingUser);
}
},
onTypingStopped: (typingUser) => { /* Add this */
if (typingUser.id !== user.id) {
setTyping(null);
}
}
});

// Unchanged
}, [user, channel]);

typing now holds the current typing user, so you can set the GiftedChat component isTyping prop to typing != null.

return (
<GiftedChat
messages={messages}
onSend={handleSend}
user={mapUser(user)}
loadEarlier={loadEarlier}
isLoadingEarlier={isLoadingEarlier}
onLoadEarlier={handleLoadEarlier}
onInputTextChanged={handleInputTextChanged}
isTyping={typing != null} /* Add this */
renderBubble={renderBubble}
renderAvatar={renderAvatar}
/>
);

After these changes, chatScreen.js should look like this:

import React, { useContext, useEffect, useState } from 'react';
import { Avatar, Bubble, GiftedChat } from 'react-native-gifted-chat';

import { chatkitty } from '../chatkitty';
import Loading from '../components/loading';
import { AuthContext } from '../context/authProvider';

export default function ChatScreen({ route, navigation }) {
const { user } = useContext(AuthContext);
const { channel } = route.params;

const [messages, setMessages] = useState([]);
const [loading, setLoading] = useState(true);
const [loadEarlier, setLoadEarlier] = useState(false);
const [isLoadingEarlier, setIsLoadingEarlier] = useState(false);
const [messagePaginator, setMessagePaginator] = useState(null);
const [typing, setTyping] = useState(null);

useEffect(() => {
const startChatSessionResult = chatkitty.startChatSession({
channel: channel,
onMessageReceived: (message) => {
setMessages((currentMessages) =>
GiftedChat.append(currentMessages, [mapMessage(message)])
);
},
onTypingStarted: (typingUser) => {
if (typingUser.id !== user.id) {
setTyping(typingUser);
}
},
onTypingStopped: (typingUser) => {
if (typingUser.id !== user.id) {
setTyping(null);
}
}
});

chatkitty
.listMessages({
channel: channel
})
.then((result) => {
setMessages(result.paginator.items.map(mapMessage));

setMessagePaginator(result.paginator);
setLoadEarlier(result.paginator.hasNextPage);

setLoading(false);
});

return startChatSessionResult.session.end;
}, [user, channel]);

async function handleSend(pendingMessages) {
await chatkitty.sendMessage({
channel: channel,
body: pendingMessages[0].text
});
}

async function handleLoadEarlier() {
if (!messagePaginator.hasNextPage) {
setLoadEarlier(false);

return;
}

setIsLoadingEarlier(true);

const nextPaginator = await messagePaginator.nextPage();

setMessagePaginator(nextPaginator);

setMessages((currentMessages) =>
GiftedChat.prepend(currentMessages, nextPaginator.items.map(mapMessage))
);

setIsLoadingEarlier(false);
}

function handleInputTextChanged(text) {
chatkitty.sendKeystrokes({
channel: channel,
keys: text
});
}

function renderBubble(props) {
return (
<Bubble
{...props}
wrapperStyle={{
left: {
backgroundColor: '#d3d3d3'
}
}}
/>
);
}

function renderAvatar(props) {
return (
<Avatar
{...props}
onPressAvatar={(avatarUser) => {
chatkitty
.createChannel({
type: 'DIRECT',
members: [{ id: avatarUser._id }]
})
.then((result) => {
navigation.navigate('Chat', { channel: result.channel });
});
}}
/>
);
}

if (loading) {
return <Loading />;
}

return (
<GiftedChat
messages={messages}
onSend={handleSend}
user={mapUser(user)}
loadEarlier={loadEarlier}
isLoadingEarlier={isLoadingEarlier}
onLoadEarlier={handleLoadEarlier}
onInputTextChanged={handleInputTextChanged}
isTyping={typing != null}
renderBubble={renderBubble}
renderAvatar={renderAvatar}
/>
);
}

function mapMessage(message) {
return {
_id: message.id,
text: message.body,
createdAt: new Date(message.createdTime),
user: mapUser(message.user)
};
}

function mapUser(user) {
return {
_id: user.id,
name: user.displayName,
avatar: user.displayPictureUrl
};
}

If you run your app now on a mobile, you should see a typing indicator when someone else starts typing.

Screenshot: Simple typing indicator

Pretty cool! However, the out-of-the-box Gifted Chat typing indicator has a few limitations. Firstly, the in-built indicator doesn't work on Web, so if you deploy your Expo app on the Web, your users won't see this amazing feature. Secondly, although the in-built indicator lets your user know someone is typing, it doesn't tell your users who is typing. It would be nice if we could see the name of the user typing, since a group chat might have multiple active members possibly typing at a time.

Adding a detailed typing indicator

Gifted Chat lets you add a custom footer to a chat using its renderFooter prop. Let's use this to render a detailed typing status message if a user is currently typing. This footer shows up on Web and gives your users more information.

Screenshot: Detailed typing indicator partial

  • Start by importing StyleSheet and View from react-native, and Text from react-native-paper.

  • Create a helper method renderFooter inside the chatScreen.js component.

  • Define a styles object with styling for the footer <View/> component.

  • Return a <View/> component nesting a <Text/> displaying the typing user if typing with the new styles or null otherwise.

  • Lastly, on the GiftedChat component, set its renderFooter prop to the renderFooter method.

After these changes chatScreen.js should look like this:

import React, { useContext, useEffect, useState } from 'react';
import { StyleSheet, View } from 'react-native';
import { Avatar, Bubble, GiftedChat } from 'react-native-gifted-chat';
import { Text } from 'react-native-paper';

import { chatkitty } from '../chatkitty';
import Loading from '../components/loading';
import { AuthContext } from '../context/authProvider';

export default function ChatScreen({ route, navigation }) {
const { user } = useContext(AuthContext);
const { channel } = route.params;

const [messages, setMessages] = useState([]);
const [loading, setLoading] = useState(true);
const [loadEarlier, setLoadEarlier] = useState(false);
const [isLoadingEarlier, setIsLoadingEarlier] = useState(false);
const [messagePaginator, setMessagePaginator] = useState(null);
const [typing, setTyping] = useState(null);

useEffect(() => {
const startChatSessionResult = chatkitty.startChatSession({
channel: channel,
onMessageReceived: (message) => {
setMessages((currentMessages) =>
GiftedChat.append(currentMessages, [mapMessage(message)])
);
},
onTypingStarted: (typingUser) => {
if (typingUser.id !== user.id) {
setTyping(typingUser);
}
},
onTypingStopped: (typingUser) => {
if (typingUser.id !== user.id) {
setTyping(null);
}
}
});

chatkitty
.listMessages({
channel: channel
})
.then((result) => {
setMessages(result.paginator.items.map(mapMessage));

setMessagePaginator(result.paginator);
setLoadEarlier(result.paginator.hasNextPage);

setLoading(false);
});

return startChatSessionResult.session.end;
}, [user, channel]);

async function handleSend(pendingMessages) {
await chatkitty.sendMessage({
channel: channel,
body: pendingMessages[0].text
});
}

async function handleLoadEarlier() {
if (!messagePaginator.hasNextPage) {
setLoadEarlier(false);

return;
}

setIsLoadingEarlier(true);

const nextPaginator = await messagePaginator.nextPage();

setMessagePaginator(nextPaginator);

setMessages((currentMessages) =>
GiftedChat.prepend(currentMessages, nextPaginator.items.map(mapMessage))
);

setIsLoadingEarlier(false);
}

function handleInputTextChanged(text) {
chatkitty.sendKeystrokes({
channel: channel,
keys: text
});
}

function renderBubble(props) {
return (
<Bubble
{...props}
wrapperStyle={{
left: {
backgroundColor: '#d3d3d3'
}
}}
/>
);
}

function renderAvatar(props) {
return (
<Avatar
{...props}
onPressAvatar={(avatarUser) => {
chatkitty
.createChannel({
type: 'DIRECT',
members: [{ id: avatarUser._id }]
})
.then((result) => {
navigation.navigate('Chat', { channel: result.channel });
});
}}
/>
);
}

function renderFooter() {
if (typing) {
return (
<View style={styles.footer}>
<Text>{typing.displayName} is typing</Text>
</View>
);
}

return null;
}

if (loading) {
return <Loading />;
}

return (
<GiftedChat
messages={messages}
onSend={handleSend}
user={mapUser(user)}
loadEarlier={loadEarlier}
isLoadingEarlier={isLoadingEarlier}
onLoadEarlier={handleLoadEarlier}
onInputTextChanged={handleInputTextChanged}
renderBubble={renderBubble}
renderAvatar={renderAvatar}
renderFooter={renderFooter}
/>
);
}

function mapMessage(message) {
return {
_id: message.id,
text: message.body,
createdAt: new Date(message.createdTime),
user: mapUser(message.user)
};
}

function mapUser(user) {
return {
_id: user.id,
name: user.displayName,
avatar: user.displayPictureUrl
};
}

const styles = StyleSheet.create({
footer: {
paddingRight: 10,
paddingLeft: 10,
paddingBottom: 5
}
});

With that done, running the app and typing in a chat as another user shows:

Screenshot: Detailed typing indicator

You now have a typing indicator implemented, making your chat app more immersive, great job! Next, let's continue with the immersion by announcing when other users enter or leave a chat.

Adding chat presence notifications

ChatKitty provides chat session handler methods to handle when users enter and leave a chat. In the previous article, you added Expo notifications to provide push and in-app notifications for your chat app. Let's use this in your chat sessions' onParticipantEnteredChat and onParticipantLeftChat handler methods to notify users when users enter or leave a chat.

In chatScreen.js, let's register chat session handler methods using the notification context sendNotification function we created in part 3 to show a notification when a user enters or leaves the chat.

import { NotificationContext } from '../context/notificationProvider'; // Import notification context

export default function ChatScreen({ route, navigation }) {
const { sendNotification } = useContext(NotificationContext); // Add this

//...

useEffect(() => {
const startChatSessionResult = chatkitty.startChatSession({
//...
onParticipantEnteredChat: (participant) => { /* Add this */
sendNotification({
title: `${participant.displayName} entered the chat`
});
},
onParticipantLeftChat: (participant) => { /* Add this */
sendNotification({
title: `${participant.displayName} left the chat`
});
}
});

// Unchanged...
}

After these changes chatScreen.js should look like this:

import React, { useContext, useEffect, useState } from 'react';
import { StyleSheet, View } from 'react-native';
import { Avatar, Bubble, GiftedChat } from 'react-native-gifted-chat';
import { Text } from 'react-native-paper';

import { chatkitty } from '../chatkitty';
import Loading from '../components/loading';
import { AuthContext } from '../context/authProvider';
import { NotificationContext } from '../context/notificationProvider';

export default function ChatScreen({ route, navigation }) {
const { user } = useContext(AuthContext);
const { sendNotification } = useContext(NotificationContext);
const { channel } = route.params;

const [messages, setMessages] = useState([]);
const [loading, setLoading] = useState(true);
const [loadEarlier, setLoadEarlier] = useState(false);
const [isLoadingEarlier, setIsLoadingEarlier] = useState(false);
const [messagePaginator, setMessagePaginator] = useState(null);
const [typing, setTyping] = useState(null);

useEffect(() => {
const startChatSessionResult = chatkitty.startChatSession({
channel: channel,
onMessageReceived: (message) => {
setMessages((currentMessages) =>
GiftedChat.append(currentMessages, [mapMessage(message)])
);
},
onTypingStarted: (typingUser) => {
if (typingUser.id !== user.id) {
setTyping(typingUser);
}
},
onTypingStopped: (typingUser) => {
if (typingUser.id !== user.id) {
setTyping(null);
}
},
onParticipantEnteredChat: (participant) => {
sendNotification({
title: `${participant.displayName} entered the chat`
});
},
onParticipantLeftChat: (participant) => {
sendNotification({
title: `${participant.displayName} left the chat`
});
}
});

chatkitty
.listMessages({
channel: channel
})
.then((result) => {
setMessages(result.paginator.items.map(mapMessage));

setMessagePaginator(result.paginator);
setLoadEarlier(result.paginator.hasNextPage);

setLoading(false);
});

return startChatSessionResult.session.end;
}, [user, channel]);

async function handleSend(pendingMessages) {
await chatkitty.sendMessage({
channel: channel,
body: pendingMessages[0].text
});
}

async function handleLoadEarlier() {
if (!messagePaginator.hasNextPage) {
setLoadEarlier(false);

return;
}

setIsLoadingEarlier(true);

const nextPaginator = await messagePaginator.nextPage();

setMessagePaginator(nextPaginator);

setMessages((currentMessages) =>
GiftedChat.prepend(currentMessages, nextPaginator.items.map(mapMessage))
);

setIsLoadingEarlier(false);
}

function handleInputTextChanged(text) {
chatkitty.sendKeystrokes({
channel: channel,
keys: text
});
}

function renderBubble(props) {
return (
<Bubble
{...props}
wrapperStyle={{
left: {
backgroundColor: '#d3d3d3'
}
}}
/>
);
}

function renderAvatar(props) {
return (
<Avatar
{...props}
onPressAvatar={(avatarUser) => {
chatkitty
.createChannel({
type: 'DIRECT',
members: [{ id: avatarUser._id }]
})
.then((result) => {
navigation.navigate('Chat', { channel: result.channel });
});
}}
/>
);
}

function renderFooter() {
if (typing) {
return (
<View style={styles.footer}>
<Text>{typing.displayName} is typing</Text>
</View>
);
}

return null;
}

if (loading) {
return <Loading />;
}

return (
<GiftedChat
messages={messages}
onSend={handleSend}
user={mapUser(user)}
loadEarlier={loadEarlier}
isLoadingEarlier={isLoadingEarlier}
onLoadEarlier={handleLoadEarlier}
onInputTextChanged={handleInputTextChanged}
renderBubble={renderBubble}
renderAvatar={renderAvatar}
renderFooter={renderFooter}
/>
);
}

function mapMessage(message) {
return {
_id: message.id,
text: message.body,
createdAt: new Date(message.createdTime),
user: mapUser(message.user)
};
}

function mapUser(user) {
return {
_id: user.id,
name: user.displayName,
avatar: user.displayPictureUrl
};
}

const styles = StyleSheet.create({
footer: {
paddingRight: 10,
paddingLeft: 10,
paddingBottom: 5
}
});

If you run your app now, you should see a notification when another user enters a chat. You should also see a notification when the user leaves the chat.

Pretty cool, right? With a typing indicator and presence notifications your users are now more aware of what other users are doing.

Leaving a channel

If a user is no longer interested in a channel and its discussions, let's give them a way to leave the channel and no longer be a member of that channel. Let's add a long press action to the home screen which when pressed, shows a "leave channel" dialog. React Native Paper provides dialog UI you can use to build the confirmation UI.

Screenshot: Leave channel partial

Edit the homeScreen.js you created earlier in src/screens/ with the following steps:

  • Import Button, Dialog and Portal from react-native-paper
import { useIsFocused } from '@react-navigation/native';
import React, { useEffect, useState } from 'react';
import { FlatList, StyleSheet, View } from 'react-native';
import { Button, Dialog, Divider, List, Portal } from 'react-native-paper'; /* Add this */

  • Add a new state variable to track if the current user wants to leave a channel, storing the selected channel
export default function HomeScreen({ navigation }) {
const [channels, setChannels] = useState([]);
const [loading, setLoading] = useState(true);
const [leaveChannel, setLeaveChannel] = useState(null); /* Add this */

// Unchanged
}

  • Next create helper methods to handle leaving a selected channel or dismissing the selected channel
function handleLeaveChannel() {
chatkitty.leaveChannel({ channel: leaveChannel }).then(() => {
setLeaveChannel(null);

chatkitty.listChannels({ filter: { joined: true } }).then((result) => {
setChannels(result.paginator.items);
});
});
}

function handleDismissLeaveChannel() {
setLeaveChannel(null);
}

  • Finally, create a <Dialog/> component to prompt the current user for confirmation when leaving a channel, and use the onLongPress flat list item prop to select a channel to leave by setting leaveChannel state
return (
<View style={styles.container}>
<FlatList
data={channels}
keyExtractor={(item) => item.id.toString()}
ItemSeparatorComponent={() => <Divider />}
renderItem={({ item }) => (
<List.Item
title={channelDisplayName(item)}
description={item.type}
titleNumberOfLines={1}
titleStyle={styles.listTitle}
descriptionStyle={styles.listDescription}
descriptionNumberOfLines={1}
onPress={() => navigation.navigate('Chat', { channel: item })}
onLongPress={() => { /* Add this */
setLeaveChannel(item);
}}
/>
)}
/>
// Add this
<Portal>
<Dialog visible={leaveChannel} onDismiss={handleDismissLeaveChannel}>
<Dialog.Title>Leave channel?</Dialog.Title>
<Dialog.Actions>
<Button onPress={handleDismissLeaveChannel}>Cancel</Button>
<Button onPress={handleLeaveChannel}>Confirm</Button>
</Dialog.Actions>
</Dialog>
</Portal>
</View>
);

If you try running your app now, and long press a channel on the home screen, you should see a confirmation dialog asking if you want to leave the channel.

Screenshot: Leave channel

Confirming the dialog prompt should remove the channel from your channels list.

Conclusion

Amazing! You've completed this tutorial series, and successfully created a robust and full-featured Expo React Native chat app using Gifted Chat powered by ChatKitty. By using Firebase and ChatKitty Chat Functions, you were able to provide a simple yet secure login and registration flow for your users. Using the ChatKitty real-time SDK, you saved time and effort building out real-time messaging complete with features like push notifications, typing indicators, and user presence. That's what I call easy development. 😉

What's Next?

In the next post, I'll be starting a new series of articles covering how to build chat for Web React projects using the bleeding edge chatscope chat UI kit. Stay tuned for more. 🔥

Like always, if you have any questions, comments or need help with any part of this article, join our Discord Server where you can ask questions, discuss what you're working on, and I'll be more than happy to help.

You can find the complete source code for this project inside this GitHub repository.

👉 Checkout the other blog posts in this series:


This article contains materials adapted from "Chat app with React Native" by Aman Mittal, originally published at Heartbeat.Fritz.Ai.

This article features an image by Volodymyr Hryshchenko.

· 11 min read

Building a Chat App with React Native and Expo (Part 3)

In this tutorial series, I'll be showing you how to build a functional and secure chat app using the latest React Native libraries, including Gifted Chat and the Expo framework powered by the ChatKitty platform.


In the second article of this series, you learned how to use the Gifted Chat React Native library with ChatKitty's JavaScript SDK to build a full featured chat screen with real-time messaging functionality into your app. You also added screens for users to create public channels, discover new channels, and view their channels.

In this tutorial, you'll be using Expo push notifications and ChatKitty Chat Functions to set up local notifications and push notifications to inform users when new messages are received or relevant actions happen inside a channel and across your app.

You can checkout our Expo React Native sample code any time on GitHub.

After reading this article, you will be able to:

  1. Implement local notifications for users to see what's happening from another screen

  2. Use ChatKitty user properties to store arbitrary data related to your users like expo push tokens

  3. Use Expo push notifications and ChatKitty Chat Functions to implement push notifications

If you followed along the last article, you should already have the ChatKitty JavaScript SDK NPM package added to your Expo React Native project.


Before we begin, let's go over some terms we'll be using a lot in this article.

What are local notifications?

Local notifications are messages that pop up while your app is in-use to inform a user of relevant actions related to another screen in your application from their current screen. ChatKitty sends notifications to your app through the ChatKitty JavaScript SDK. You can listen for these notifications and use them to build in-app notification views.

What are push notifications?

Push notifications are short messages sent to mobile devices to alert a user when something of interests happen, and provide information related to that event even when your app isn't currently in-use. Push notifications are a great way to engage your users and improve your customer experience. Push notifications are a critical part of most chat apps and have traditionally been difficult to implement. However, the Expo framework provides seamless support for push notifications, simplifying the process of send push notifications to your users.

Installing notification libraries

For this project, you'll be using Expo push notifications. So, install the Expo notifications, and other dependency modules you'll need to get expo push tokens which are needed to register user devices for push notifications:

npx expo install expo-device expo-notifications

Let's also install the EAS CLI. EAS Build is a hosted service for building app binaries for your Expo and React Native projects. You will be using it to set up and handle Expo push notification credentials.

npm install -g eas-cli

Setting up Expo push notification credentials

info

Check out the official Expo push notifications guide for more information on setting up Expo push notification as things change,

For iOS, the managed Expo workflow handles push notification credentials automatically when you register your device and run the eas build command. However, for Android you'll need to add an Android app to your Firebase project, update your project, and upload your FCM server credentials to Expo.

Adding Firebase credentials to the app

From the Firebase console side menu, go to your "Project settings".

Screenshot: Firebase project settings

Go to the "Your apps" section and click the Android icon:

Screenshot: Screenshot: Firebase add app

Fill out the application details and register your android app

Screenshot: Screenshot: Firebase create android app register

Download the google-services.json file and add it to your Expo React Native project's root directory

Screenshot: Screenshot: Firebase create android app download

In your app.json inside your project's root directory, add an android.googleServicesFile property with the relative path to the google-services.json file, as well as an android.package property with your app's Android package name:

{
"expo": {
...
"android": {
"package": "com.yourpackage.yourcoolapp",
"googleServicesFile": "./google-services.json"
}
...
}
}

Uploading FCM Server Credentials to Expo

To allow Expo to send push notifications to your Android app, you'll need to upload your FCM server key. Before you can upload your server key to Expo, you'll need to create an Expo account.

To get your FCM server key, go to "Project Settings" section of your Firebase project, then go to the "Cloud Messaging" tab. As you will see, Server Key is only available in Cloud Messaging API (Legacy), which is disabled by default, so you will need to enable it.

Screenshot: Enable Firebase Cloud Messaging API

Once you have enabled this, you can copy the server key listed next to the token.

Screenshot: Firebase Cloud Messaging server key

Now, go to the "Credentials" section under the "Account Settings" option from your Expo account's dashboard side menu, and upload the required credentials to Expo.

To run a local Android build, you will need to run this command as well:

eas build --platform android --local

Getting a user's expo push token

To send a push notification to a user using Expo, we'll need their expo push token. Once we get the expo push token, we can then store it as a ChatKitty user property, so we can access it later in a chat function or on a back-end.

We'll interface with Expo notifications using a new context provider. Inside the src/context/ directory, create a new file notificationProvider.js. We'll define a new context and provider component to register the user's device for push notifications and update the current ChatKitty user's properties to store their Expo device token.

src/context/notificationProvider.js
import { createContext, useEffect, useRef, useState } from 'react';
import { Platform } from 'react-native';

import * as Device from 'expo-device';
import * as Notifications from 'expo-notifications';
import { chatkitty } from '../chatkitty';

Notifications.setNotificationHandler({
handleNotification: async () => ({
shouldShowAlert: true,
shouldPlaySound: false,
shouldSetBadge: false
})
});

export const NotificationContext = createContext({});

export const NotificationProvider = ({ children }) => {
const [notification, setNotification] = useState(null);
const notificationListener = useRef();
const responseListener = useRef();

useEffect(() => {
notificationListener.current =
Notifications.addNotificationReceivedListener((notification) => {
setNotification(notification);
});

responseListener.current =
Notifications.addNotificationResponseReceivedListener((response) => {
console.log(response);
});

return () => {
Notifications.removeNotificationSubscription(
notificationListener.current
);
Notifications.removeNotificationSubscription(responseListener.current);
};
}, []);

return (
<NotificationContext.Provider
value={{
notification,
registerForPushNotifications: async () => {
let token;
if (Device.isDevice) {
const { status: existingStatus } = await Notifications.getPermissionsAsync();
let finalStatus = existingStatus;
if (existingStatus !== 'granted') {
const { status } = await Notifications.requestPermissionsAsync();
finalStatus = status;
}
if (finalStatus !== 'granted') {
alert('Failed to get push token for push notification!');
return;
}
token = (await Notifications.getExpoPushTokenAsync()).data;
console.log(token);
} else {
alert('Must use physical device for Push Notifications');
}

if (Platform.OS === 'android') {
await Notifications.setNotificationChannelAsync('default', {
name: 'default',
importance: Notifications.AndroidImportance.MAX,
vibrationPattern: [0, 250, 250, 250],
lightColor: '#FF231F7C'
});
}

await chatkitty.updateCurrentUser((user) => {
user.properties = {
...user.properties,
'expo-push-token': token
};
return user;
});
}
}}
>
{children}
</NotificationContext.Provider>
);
};

Later, we'll be updating the notification provider to send local notifications.

To get the notification context inside your app components, wrap the app routes with notification provider.

Edit the src/context/index.js file to wrap the app routes with the notification provider.

The index.js file should now contain:

src/context/index.js
import React from 'react';
import { DefaultTheme, Provider as PaperProvider } from 'react-native-paper';

import { AuthProvider } from './authProvider';
import { NotificationProvider } from './notificationProvider';
import Routes from './routes';

export default function Providers() {
return (
<PaperProvider theme={theme}>
<AuthProvider>
<NotificationProvider>
<Routes />
</NotificationProvider>
</AuthProvider>
</PaperProvider>
);
}

const theme = {
...DefaultTheme,
roundness: 2,
colors: {
...DefaultTheme.colors,
primary: '#5b3a70',
accent: '#50c878',
background: '#f7f9fb'
}
};

Next, update homeStack.js to call registerForPushNotifications from the notification context.

src/context/homeStack.js
import React, { useContext, useEffect } from 'react';
import { NotificationContext } from './notificationProvider';

export default function HomeStack() {
const { registerForPushNotifications } = useContext(NotificationContext);

useEffect(() => {
registerForPushNotifications();
}, []);

// Unchanged
}

With that, you should have the user's expo push token as the expo-push-token user property. With Expo set up, let's create a ChatKitty chat function to use Expo to send a push notification when a ChatKitty notification event happens.

Adding Expo to your Chat Runtime

ChatKitty makes it easy to integrate your back-end and external services like Expo into a ChatKitty application using Chat Functions. Chat Functions let you write arbitrary code that runs any time a relevant event or action happens inside your app. We'll be using a chat function to send a push notification whenever an event occurs that a user should be notified about, and the user isn't online. With ChatKitty, you can use any NPM package inside your Chat Functions as a Chat Runtime dependency.

From your ChatKitty application dashboard, go to the "Functions" page:

Screenshot: ChatKitty side menu functions

Go to the "Runtime" tab and add a new dependency to the Expo Server SDK NPM package, expo-server-sdk. Version 3.7.0 was the latest version as of the time this article was written.

Screenshot: ChatKitty runtime add expo Remember to click the "Save" icon to confirm your chat runtime dependencies changes.

Now we're ready to define a chat function to send a push notification using Expo, whenever a user should be notified about an event, and the user is offline.

Sending push notifications using a chat function

From your ChatKitty application dashboard, go to the "Functions" page and select the "User Received Notification" event chat function:

Screenshot: ChatKitty chat functions

This chat function runs whenever an event a user can be notified about happens. Edit the chat function to send a push notification if the user isn't currently online.

const { Expo } = require('expo-server-sdk');

const expo = new Expo(); // create Expo client

async function handleEvent(
event: UserReceivedNotificationEvent,
context: Context
) {
if (event.userHasActiveSession) return; // skip if this user is online

const expoPushToken = event.user.properties['expo-push-token']; // get the expo push token registered

if (!expoPushToken || !Expo.isExpoPushToken(expoPushToken)) return; // check expo push token is present and valid

const notification = event.notification;

// send push notification with Expo
await expo.sendPushNotificationsAsync([
{
to: expoPushToken,
sound: 'default',
title: notification.title,
body: notification.body,
data: notification.data
}
]);
}

Screenshot: ChatKitty chat function user received notification Remember to click the "Save" icon to confirm your chat function changes.

If you close the app now, and send a message from another device as another user, you should see a push notification:

Screenshot: Push notification

Handling local notifications with Expo

Now that we have Expo push notifications set up, let's also handle local notifications with Expo.

To send local notifications, let's add a sendNotification function to notificationProvider.js that schedules a local Expo notification and export it in the notification context:

src/context/notificationProvider.js
export const NotificationProvider = ({ children }) => {
// Unchanged...

return (
<NotificationContext.Provider
value={{
notification,
sendNotification: async (content) => {
await Notifications.scheduleNotificationAsync({
content,
trigger: null
});
},
registerForPushNotifications: async () => { /* Unchanged */}
}}
>
{children}
</NotificationContext.Provider>
);
};

Next, in homeStack.js register a ChatKitty onNotificationReceived event listener in the useEffect React hook to show received in-app notifications:

src/context/homeStack.js
import {chatkitty} from '../chatkitty';

export default function HomeStack() {
const {registerForPushNotifications, sendNotification} = useContext(NotificationContext);

useEffect(()=> {
registerForPushNotifications();

chatkitty.onNotificationReceived(async (notification) => {
await sendNotification({
title: notification.title,
body: notification.body
});
});
}, []);

// Unchanged...
}

Conclusion

Pretty cool, you've completed the third part of this tutorial series and successfully implemented push notifications, using the Expo framework and ChatKitty Chat Functions. You've also implemented local notifications that seamlessly inform your users when something they care about happens. Your users are now always in the loop.

What's next?

In the next post of this series, we'll be enhancing your chat app's user experience with direct messaging, typing indicators, and chat presence notifications. Stay tuned for more. 🔥

If you have any questions, comments or need help with any part of this article, join our Discord Server where you can ask questions, discuss what you're working on, and I'll be more than happy to help.

You can find the complete source code for this project inside this GitHub repository.

👉 Checkout the other blog posts in this series:


This article contains materials adapted from "Chat app with React Native" by Aman Mittal, originally published at Heartbeat.Fritz.Ai.

This article features an image by Volodymyr Hryshchenko.

· 18 min read

Building a Chat App with React Native and Gifted Chat (Part 2)

In this tutorial series , I'll be showing you how to build a functional and secure chat app using the latest React Native libraries, including Gifted Chat and the Expo framework powered by the ChatKitty platform.


In the first article of this series, you learned how to use Firebase along with ChatKitty Chat Functions to implement a secure yet simple user login flow by proxying Firebase Authentication through ChatKitty. Along with that, you built a couple of screens with the react-native-paper UI library to allow users to register for your chat app and login into the app.

In this tutorial, you'll be using the Gifted Chat React Native library to create a full-featured chat screen with its out of the box features. You'll also use ChatKitty's JavaScript Chat SDK to add real-time messaging to your chat app.

After reading this article, you will be able to:

  1. Create public channels for users to join

  2. View all channels a user can join and discover channels created by other users

  3. Integrate the react-native-gifted-chat library to implement a group chat screen

You can check out our Expo React Native sample code any time on GitHub.

If you followed along the last article, you should already have the ChatKitty JavaScript SDK NPM package added to your Expo React Native project.


Before we begin, let's go over some terms we'll be using a lot in this article.

What are channels?

Channels are the backbone of the ChatKitty chat experience. Users can join channels and receive or send messages. ChatKitty broadcasts messages created in channels to channel member users with active chat sessions and sends notifications to offline members.

What are chat sessions?

Before a user can begin sending and receiving real-time messages and use in-app chat features like typing indicators, delivery and read receipts, live reactions, etc, their device needs to start a chat session. A user device can start up to 10 chat sessions at a time but usually have only a maximum of one active at a time. You can think of an active chat session as corresponding to being in a "chat room", when the user "leaves" the chat room, its chat session ends.

With that, you have all the information you need build to chat into your app.

Let's go! 🏎️

First, you'll start by creating a screen that shows a list of channels a user can chat in after logging in.

Displaying a user's channels

Start by changing the homeScreen.js you previously created to list the channels a logged-in user is a member of.

The homeScreen.js file should contain:

src/screens/homeScreen.js
import { useIsFocused } from '@react-navigation/native';
import React, { useEffect, useState } from 'react';
import { FlatList, StyleSheet, View } from 'react-native';
import { Divider, List } from 'react-native-paper';

import { chatkitty } from '../chatkitty';
import Loading from '../components/loading';

export default function HomeScreen() {
const [channels, setChannels] = useState([]);
const [loading, setLoading] = useState(true);

const isFocused = useIsFocused();

useEffect(() => {
let isCancelled = false;

chatkitty.listChannels({ filter: { joined: true } }).then((result) => {
if (!isCancelled) {
setChannels(result.paginator.items);

if (loading) {
setLoading(false);
}
}
});

return () => {
isCancelled = true;
};
}, [isFocused, loading]);

if (loading) {
return <Loading />;
}

return (
<View style={styles.container}>
<FlatList
data={channels}
keyExtractor={(item) => item.id.toString()}
ItemSeparatorComponent={() => <Divider />}
renderItem={({ item }) => (
<List.Item
title={item.name}
description={item.type}
titleNumberOfLines={1}
titleStyle={styles.listTitle}
descriptionStyle={styles.listDescription}
descriptionNumberOfLines={1}
onPress={() => {
// TODO navigate to a chat screen.
}}
/>
)}
/>
</View>
);
}

const styles = StyleSheet.create({
container: {
backgroundColor: '#f5f5f5',
flex: 1
},
listTitle: {
fontSize: 22
},
listDescription: {
fontSize: 16
}
});

If you run the app and log in now, it shouldn't look like much.

Screenshot: Home screen empty

Pretty empty, huh? Soon you'll create a screen responsible for creating new channels, so the home screen can be populated.

Creating shared header components and a modal stack navigator

Before we create the CreateChannel screen, we should modify the app header bar to share options across different screens. The CreateChannel screen will be implemented as a modal, so we'll also need a separate stack navigator to wrap the home stack navigator and handle modals. Modals are screens that block interactions with the main view when displaying their content.

Modify your homeStack.js file in the src/context/ directory to apply header bar options across screens and define a stack navigator for app modal screens.

The homeStack.js file should contain:

src/context/homeStack.js
import { createStackNavigator } from '@react-navigation/stack';
import React from 'react';

import HomeScreen from '../screens/homeScreen';

const ChatStack = createStackNavigator();
const ModalStack = createStackNavigator();

export default function HomeStack() {
return (
<ModalStack.Navigator screenOptions={{
headerShown: false,
presentation: 'modal'
}}>
<ModalStack.Screen name='ChatApp' component={ChatComponent} />
</ModalStack.Navigator>
);
}

function ChatComponent() {
return (
<ChatStack.Navigator
screenOptions={{
headerStyle: {
backgroundColor: '#5b3a70'
},
headerTintColor: '#ffffff',
headerTitleStyle: {
fontSize: 22
}
}}
>
<ChatStack.Screen name='Home' component={HomeScreen} />
</ChatStack.Navigator>
);
}

Creating a channel creation screen

Now we can create a new screen file createChannelScreen.js inside the src/screens/ directory. From this screen, users will create new public channels other users can join and chat in.

The createChannelScreen.js file should contain:

src/screens/createChannelScreen.js
import React, { useState } from 'react';
import { StyleSheet, View } from 'react-native';
import { IconButton, Title } from 'react-native-paper';

import { chatkitty } from '../chatkitty';
import FormButton from '../components/formButton';
import FormInput from '../components/formInput';

export default function CreateChannelScreen({ navigation }) {
const [channelName, setChannelName] = useState('');

function handleButtonPress() {
if (channelName.length > 0) {
chatkitty
.createChannel({
type: 'PUBLIC',
name: channelName
})
.then(() => navigation.navigate('Home'));
}
}

return (
<View style={styles.rootContainer}>
<View style={styles.closeButtonContainer}>
<IconButton
icon='close-circle'
size={36}
iconColor='#5b3a70'
onPress={() => navigation.goBack()}
/>
</View>
<View style={styles.innerContainer}>
<Title style={styles.title}>Create a new channel</Title>
<FormInput
labelName='Channel Name'
value={channelName}
onChangeText={(text) => setChannelName(text)}
clearButtonMode='while-editing'
/>
<FormButton
title='Create'
modeValue='contained'
labelStyle={styles.buttonLabel}
onPress={() => handleButtonPress()}
disabled={channelName.length === 0}
/>
</View>
</View>
);
}

const styles = StyleSheet.create({
rootContainer: {
flex: 1
},
closeButtonContainer: {
position: 'absolute',
top: 30,
right: 0,
zIndex: 1
},
innerContainer: {
flex: 1,
justifyContent: 'center',
alignItems: 'center'
},
title: {
fontSize: 24,
marginBottom: 10
},
buttonLabel: {
fontSize: 22
}
});

Okay, let's test the CreateChannel screen by adding a temporary button to open the screen in our home screen header bar, and creating a new channel.

The homeStack.js file should contain:

src/context/homeStack.js
import { createStackNavigator } from '@react-navigation/stack';
import React from 'react';
import { IconButton } from 'react-native-paper';

import CreateChannelScreen from '../screens/createChannelScreen';
import HomeScreen from '../screens/homeScreen';

const ChatStack = createStackNavigator();
const ModalStack = createStackNavigator();

export default function HomeStack() {
return (
<ModalStack.Navigator screenOptions={{
headerShown: false,
presentation: 'modal'
}}>
<ModalStack.Screen name='ChatApp' component={ChatComponent} />
<ModalStack.Screen name='CreateChannel' component={CreateChannelScreen} />
</ModalStack.Navigator>
);
}

function ChatComponent() {
return (
<ChatStack.Navigator
screenOptions={{
headerStyle: {
backgroundColor: '#5b3a70'
},
headerTintColor: '#ffffff',
headerTitleStyle: {
fontSize: 22
}
}}
>
<ChatStack.Screen
name='Home'
component={HomeScreen}
options={({ navigation }) => ({
headerRight: () => (
<IconButton
icon='plus'
size={28}
iconColor='#ffffff'
onPress={() => navigation.navigate('CreateChannel')}
/>
)
})}
/>
</ChatStack.Navigator>
);
}

If you run the app now, you should see a plus icon in the header bar:

Screenshot: Home screen add

Tap the button and create a new channel:

Screenshot: Create channel screen

Tap "Create", and you should be redirected back to the home screen with your new channel:

Screenshot: Home screen added

You now have a channel to send messages, receive messages and chat in. Next, let's get started building a channel chat screen with the react-native-gifted-chat library.

Creating a chat screen

Gifted Chat is an open-source React Native library that saves you tons of time and development effort building chat UIs. The library is extensible and customizable with a large online community making it a great option to build chat.

To use Gifted Chat, add its NPM package to your Expo React Native project:

npx expo install react-native-gifted-chat

Next, create a file chatScreen.js inside the src/screens/ directory. This screen will render a chat screen for users to send new messages and view messages they've sent and received. We'll be updating this screen throughout the rest of this tutorial series to add more advanced and sophisticated chat features.

chatScreen.js will need quite a few things, so let's break down what you'll be doing:

  • Import GiftedChat since we need a GiftedChat component to add the chat UI and functionality.

  • Retrieve the current user from our authentication context, so we can show messages as created by the current user and perform other current user specific functions.

  • Retrieve the channel to start this chat with using the route props.

  • Create a ChatScreen functional React component, and inside it define a messages state variable. This array will hold message data objects representing the chat message history. This variable is initially an empty array.

  • Define a couple of helper functions mapUser and mapMessage to map the current user and message objects we get from ChatKitty into a schema Gifted Chat recognizes.

  • Use an useEffect React hook to start a new chat session with the ChatScreen channel using the ChatKitty startChatSession function. Register an onMessageReceived function that appends new messages received from ChatKitty into the existing Gifted Chat managed messages, and replace the messages state when a new message is received. After starting the chat session, fetch the channel's last messages using listMessages then replace the messages state. As part of cleaning up when the component is about to be destroyed, return the ChatSession's end function to the useEffect function to end the chat session and free up ChatKitty resources.

  • Define a helper function handleSend, to send a new message using the ChatKitty sendMessage function.

  • Return to be rendered a GiftedChat with the messages state, a mapped GiftedChat current user chatUser, and the handleSend helper function.

The chatScreen.js file should contain:

src/screens/chatScreen.js
import React, { useContext, useEffect, useState } from 'react';
import { Bubble, GiftedChat } from 'react-native-gifted-chat';

import { chatkitty } from '../chatkitty';
import Loading from '../components/loading';
import { AuthContext } from '../context/authProvider';

export default function ChatScreen({ route }) {
const { user } = useContext(AuthContext);
const { channel } = route.params;

const [messages, setMessages] = useState([]);
const [loading, setLoading] = useState(true);

useEffect(() => {
const startChatSessionResult = chatkitty.startChatSession({
channel: channel,
onMessageReceived: (message) => {
setMessages((currentMessages) =>
GiftedChat.append(currentMessages, [mapMessage(message)])
);
}
});

chatkitty
.listMessages({
channel: channel
})
.then((result) => {
setMessages(result.paginator.items.map(mapMessage));

setLoading(false);
});

return startChatSessionResult.session.end;
}, [user, channel]);

async function handleSend(pendingMessages) {
await chatkitty.sendMessage({
channel: channel,
body: pendingMessages[0].text
});
}

function renderBubble(props) {
return (
<Bubble
{...props}
wrapperStyle={{
left: {
backgroundColor: '#d3d3d3'
}
}}
/>
);
}

if (loading) {
return <Loading />;
}

return (
<GiftedChat
messages={messages}
onSend={handleSend}
user={mapUser(user)}
renderBubble={renderBubble}
/>
);
}

function mapMessage(message) {
return {
_id: message.id,
text: message.body,
createdAt: new Date(message.createdTime),
user: mapUser(message.user)
};
}

function mapUser(user) {
return {
_id: user.id,
name: user.displayName,
avatar: user.displayPictureUrl
};
}

Now, let's add the Chat screen to the home stack navigator. Edit homeStack.js in src/context/ with a new screen entry.

The homeStack.js file should contain:

src/context/homeStack.js
import { createStackNavigator } from '@react-navigation/stack';
import React from 'react';
import { IconButton } from 'react-native-paper';

import ChatScreen from '../screens/chatScreen';
import CreateChannelScreen from '../screens/createChannelScreen';
import HomeScreen from '../screens/homeScreen';

const ChatStack = createStackNavigator();
const ModalStack = createStackNavigator();

export default function HomeStack() {
return (
<ModalStack.Navigator screenOptions={{
headerShown: false,
presentation: 'modal'
}}>
<ModalStack.Screen name='ChatApp' component={ChatComponent} />
<ModalStack.Screen name='CreateChannel' component={CreateChannelScreen} />
</ModalStack.Navigator>
);
}

function ChatComponent() {
return (
<ChatStack.Navigator
screenOptions={{
headerStyle: {
backgroundColor: '#5b3a70'
},
headerTintColor: '#ffffff',
headerTitleStyle: {
fontSize: 22
}
}}
>
<ChatStack.Screen
name='Home'
component={HomeScreen}
options={({ navigation }) => ({
headerRight: () => (
<IconButton
icon='plus'
size={28}
iconColor='#ffffff'
onPress={() => navigation.navigate('CreateChannel')}
/>
)
})}
/>
<ChatStack.Screen
name='Chat'
component={ChatScreen}
options={({ route }) => ({
title: route.params.channel.name
})}
/>
</ChatStack.Navigator>
);
}

Before we can begin chatting, you'll need to update the homeScreen.js component to redirect to a Channel screen.

The homeScreen.js file should contain:

src/screens/homeScreen.js
import { useIsFocused } from '@react-navigation/native';
import React, { useEffect, useState } from 'react';
import { FlatList, StyleSheet, View } from 'react-native';
import { Divider, List } from 'react-native-paper';

import { chatkitty } from '../chatkitty';
import Loading from '../components/loading';

export default function HomeScreen({ navigation }) {
const [channels, setChannels] = useState([]);
const [loading, setLoading] = useState(true);

const isFocused = useIsFocused();

useEffect(() => {
let isCancelled = false;

chatkitty.listChannels({ filter: { joined: true } }).then((result) => {
if (!isCancelled) {
setChannels(result.paginator.items);

if (loading) {
setLoading(false);
}
}
});

return () => {
isCancelled = true;
};
}, [isFocused, loading]);

if (loading) {
return <Loading />;
}

return (
<View style={styles.container}>
<FlatList
data={channels}
keyExtractor={(item) => item.id.toString()}
ItemSeparatorComponent={() => <Divider />}
renderItem={({ item }) => (
<List.Item
title={item.name}
description={item.type}
titleNumberOfLines={1}
titleStyle={styles.listTitle}
descriptionStyle={styles.listDescription}
descriptionNumberOfLines={1}
onPress={() => navigation.navigate('Chat', { channel: item })}
/>
)}
/>
</View>
);
}

const styles = StyleSheet.create({
container: {
backgroundColor: '#f5f5f5',
flex: 1
},
listTitle: {
fontSize: 22
},
listDescription: {
fontSize: 16
}
});