How to Build a Multilingual AI Voice Assistant in FlutterFlow (OpenAI Text-To-Speech App Tutorial)
TLDRIn this tutorial, the creator demonstrates how to build a multilingual AI bot using Flutter Flow and Open AI's API. The bot operates as a web app, allowing users to interact with it via voice commands and select their preferred language. The bot processes the user's input, displays a waveform, and provides responses in a conversational manner. The development process is streamlined, taking about 30 minutes, and the app is designed with a dark theme for a visually appealing interface. The tutorial covers setting up Firebase, configuring the UI, managing state, and integrating API calls to deliver a functional AI assistant that can respond to user queries in multiple languages.
Takeaways
- 🚀 The video demonstrates building a multilingual AI bot using Flutter Flow and Open AI's API.
- 🌐 The AI bot can be deployed as a web application and supports language selection.
- 🎤 Users can interact with the bot via a microphone, asking questions and receiving responses.
- 📊 The bot displays a waveform to visually represent the audio input and response.
- ⏰ The entire development process can be completed in approximately 30 minutes from scratch.
- 🔧 The project utilizes Flutter Flow's UI and API integration capabilities.
- 🔗 Firebase is used for project setup, including authentication and database configuration.
- 🎨 Custom actions and API calls are implemented to handle text recording, processing, and audio playback.
- 🗣️ The AI bot can respond in different languages, with language selection provided in the user interface.
- 🔄 The bot's functionality includes starting and stopping text recording, fetching speech responses, and playing audio.
- 🔍 Debugging features are integrated for testing API calls and handling responses.
Q & A
What is the primary purpose of the AI bot built in the video?
-The AI bot is designed to be a multilingual assistant that can be used as a website or web deployment, allowing users to interact with it via voice commands and receive responses in different languages.
How long does it take to build the AI bot as demonstrated in the video?
-The AI bot can be built in around 30 minutes using Flutter Flow, starting from scratch.
What is the role of Open AI's API in the AI bot development?
-Open AI's API is used to handle the AI assistant's functionality, such as processing user queries and returning responses, as well as generating and playing audio responses.
How does the AI bot support multilingual functionality?
-The AI bot allows users to select their preferred language, and it processes and returns responses in the chosen language, making it multilingual.
What is the significance of the waveform in the AI bot's interface?
-The waveform is used to visually represent the audio as the AI assistant speaks, providing feedback to the user that the AI is actively processing and responding to their input.
How does the video guide the setup of the Firebase project for the AI bot?
-The video instructs the user to create a new project in Firebase, connect it to the Flutter Flow project, and paste the Firebase ID to establish the connection.
What are the steps to customize the theme and appearance of the AI bot's interface?
-The video demonstrates how to adjust colors, fonts, and enable dark mode, as well as how to disable the safe area and set up the layout with a column and stack widgets.
How does the video address the recording and stopping of user input for the AI bot?
-The video shows how to implement a page state to track whether the user is recording, and how to use conditional builders to display a record button when not recording and a stop button when recording.
What is the role of the custom actions in the AI bot's functionality?
-Custom actions are used to start and stop text recording, fetch speech from Open AI, and play the AI's response as an audio message.
How does the video ensure the AI bot's response is concise and conversational?
-The video sets a parameter in the API call to Open AI to return a response that can be read aloud in 13 to 15 seconds, keeping the AI's responses short and conversational.
What is the process for publishing the AI bot as a web app?
-The video guides the user through setting up web publishing in Flutter Flow, connecting the project to Firebase, and finally publishing the web app.
Outlines
🚀 Building a Multilingual AI Bot in Flutter Flow
This video tutorial guides viewers through the process of creating a multilingual AI bot using Flutter Flow and Open AI's API. The bot is designed as a web application, and the tutorial demonstrates how to record responses, select languages, and showcase a waveform. The project is built from scratch in about 30 minutes, using a single page in Flutter Flow and API calls to Open AI.
📱 Setting Up the Flutter Flow Project
The video explains how to set up a new Flutter Flow project, including configuring Firebase for web deployment and adjusting the project's theme to a dark mode. The tutorial also covers disabling the dark mode, setting up primary and secondary background colors, and preparing the page layout with a column and a bottom bar. It emphasizes the importance of structuring the site for a better user experience.
🎤 Adding Recording and Playback Functionality
The paragraph details the process of adding a recording button to the AI bot's interface. It involves using a conditional Builder and page State to manage the recording and playback states. The tutorial also shows how to add a waveform animation to visually represent the audio input and how to set up the UI elements, such as the record and stop buttons, to interact with the user's microphone.
🗣️ Implementing Speech-to-Text and Text-to-Speech
This part of the tutorial focuses on implementing speech-to-text functionality by adding custom actions to start and stop text recording. It also explains how to fetch speech from Open AI and play it back as an audio message. The video script includes instructions for setting up the API call, handling the response, and converting it into an audio file for playback.
🌐 Adding Multilingual Support and Language Selector
The video demonstrates how to add multilingual support to the AI bot by setting up language options in Flutter Flow. It shows how to integrate a language selector widget and pass the selected language code to the Open AI API call. The tutorial also covers how to translate the project's text into different languages and how to use the response and test tab in Flutter Flow to test the API call.
🕒 Timing the Audio Playback with a Timer
This section explains how to set up a timer to control the display of the waveform animation during audio playback. It involves updating the page state to show the waveform, starting the timer, and resetting it once the audio duration has elapsed. The tutorial also includes steps for updating the app state and clearing the speech-to-text response after the audio has played.
🌐 Publishing the AI Bot Web Application
The final part of the tutorial shows how to publish the AI bot web application. It guides viewers through the web publishing settings in Flutter Flow and demonstrates the successful deployment of the project. The video ends with a live demonstration of the bot's functionality in both English and French, highlighting its responsiveness and the ability to clone and customize the project.
Mindmap
Keywords
💡Flutter
💡AI Bot
💡OpenAI API
💡Multilingual
💡Web Deployment
💡Firebase
💡State Management
💡Custom Actions
💡API Call
💡Waveform
💡Timer
Highlights
Introduction to building a multilingual AI bot using Flutter Flow and OpenAI's API.
Demonstration of the AI bot's functionality with a simple arithmetic query.
Overview of the bot's language selection feature and waveform display during responses.
Guide to starting a new Flutter Flow project and configuring Firebase.
Explanation of enabling web deployment and setting up Firebase within Flutter Flow.
Details on customizing the application's theme and colors for a web deploy.
Instructions for adjusting layout and UI elements, including disabling dark mode.
Steps to add and customize containers and widgets within the Flutter Flow project.
Usage of stack and alignment tools for UI structuring.
Integration of conditional builders for dynamic UI changes based on user actions.
Explanation of implementing recording functionality using custom actions.
Setting up API calls to OpenAI for fetching responses.
Conversion of text responses from OpenAI into playable audio.
Demonstration of the app's responsiveness and multilingual capabilities.
Conclusion with an invitation to clone the project and explore Flutter Flow's community.
