Build an AI Voice Assistant with MIT App Inventor

Table of Contents

1. Introduction
2. Creating a Basic Chatbot App
   • Predefined Responses
   • Speech Recognition
   • If-Else Blocks
   • Converting Speech to Text
   • Converting Response to Speech
3. Integrating OpenAI's GPT
   • Registering an OpenAI Account
   • Upgrading Account for Advanced Usage
   • Generating API Key
   • Authenticating OpenAI
4. Connecting to Chat GPT Servers
   • Making Requests
   • Understanding JSON
   • Converting Response to Dictionary
5. Designing the Chatbot App
   • Adding Web Component
   • Adding Button and Text-to-Speech Component
   • Importing Extensions
   • Adding Images and Animations
6. Implementing Chat GPT Functionality
   • Initializing the Chat Response
   • Getting User Input
   • Contacting Chat GPT Servers
   • Handling Chat GPT Response
   • Converting Response to Speech
7. Packaging and Testing the App
   • Editing APK File
   • Installing and Testing the App
8. Conclusion

Introduction

In this tutorial, we will learn how to Create a chatbot app using MIT App Inventor. We will start with a basic chatbot that has predefined responses and uses speech recognition to understand user input. Then, we will integrate OpenAI's GPT (Generative Pre-trained Transformer) to generate creative responses. We will cover the process of registering an OpenAI account, generating an API key, and authenticating the app. We will also design the user interface, import necessary extensions, and implement the chat GPT functionality. Finally, we will Package the app and test it on an Android device.

Creating a Basic Chatbot App

To create a chatbot app, we need to start with the basic components and functionalities. This includes defining predefined responses, implementing speech recognition, and using if-else blocks to provide appropriate responses Based on user input.

First, let's define the predefined responses. The chatbot app will have a set of predefined responses stored in the code. These responses will be used to provide answers to user queries. For example, if the user asks "How to treat a cold?", the chatbot may respond with "Get plenty of rest and use medications such as ibuprofen or acetaminophen to reduce fever and relieve aches and pains."

Next, we need to implement speech recognition. The app will listen to the user's speech using a speech recognizer component. This component will convert the user's speech into text that can be processed by the app. We will use the speech recognizer to capture the user's query or request.

After capturing the user's input, we will use if-else blocks to check the content of the speech and provide an appropriate response. The app will analyze the user's query and determine the best response based on predefined conditions. For example, if the user asks "Tell me a poem about a cat," the app will check for keywords like "poem" and "cat" and provide a suitable response.

Converting the response to speech is another important step. The app will convert the generated response into speech using a text-to-speech component. This will allow the chatbot to communicate the response to the user using an audio output. The response will be played back to the user using the device's speakers or headphones.

By implementing these basic functionalities, we can create a chatbot app that can understand user queries and provide appropriate responses. However, to enhance the chatbot's capabilities and make it more intelligent, we can integrate OpenAI's GPT.

Integrating OpenAI's GPT

OpenAI's GPT (Generative Pre-trained Transformer) is a powerful AI model that can generate contextual and creative responses to queries. By integrating GPT into our chatbot app, we can enhance its capabilities and provide more advanced responses to user queries.

To integrate GPT, we need to register an OpenAI account. This will allow us to access the GPT servers and make requests using our API key. By upgrading our account, we can unlock advanced usage and increase the capabilities of our chatbot. Upgrading is recommended if we plan to publish the app on the Google Play store, but the free account is sufficient for testing and sharing the app with friends and family.

After registering and upgrading our account, we will generate an API key. This key will be used to authenticate our app and connect to the GPT servers. It is important to keep the API key secure and Never share it with others. In case we forget the key, we will need to generate a new one, as it cannot be retrieved once lost.

To connect to the GPT servers, we will use a web component in MIT App Inventor. This component will allow us to make HTTP requests to the GPT servers and send our speech input as text. The servers will generate a creative response based on the input and return it to our app. We will use the response to provide intelligent answers to user queries.

The response from the GPT servers will be in the form of JSON (JavaScript Object Notation), which is a common method of communicating between different applications. We will convert the response content into a dictionary using a decoder component provided by MIT App Inventor. This will allow us to extract the Relevant information and use it to generate Meaningful responses.

By integrating OpenAI's GPT into our chatbot app, we can enhance its capabilities and provide more creative and intelligent responses to the user's queries. Let's proceed to design the user interface and implement the chat GPT functionality.

Designing the Chatbot App

To create an engaging chatbot app, we need to design a user-friendly interface and incorporate elements that enhance the user experience. This includes adding a web component for displaying responses, a button for user input, and a text-to-speech component for audio output.

We will start by adding a web viewer component to the screen. The web viewer will be used to display the chatbot's responses as text. This will allow the user to easily Read the generated responses and communicate with the chatbot.

Below the web viewer, we will add a button that serves as the user input mechanism. The button will be labeled "Speak Now" and will be used to trigger the speech recognition functionality. When the user clicks the button, the app will capture their speech and process it to generate a response.

To make the button visually appealing, we will customize its appearance. We will set the background color to orange, make the font bold, and set the font size to 20. Additionally, we will round the edges of the button to give it a more polished and modern look.

To convert the generated response into speech, we will add a text-to-speech component to the app. This component will take the text response from the chatbot and convert it into an audio output. This will allow the chatbot to communicate with the user using spoken words.

Lastly, we will import the necessary extensions to the app. One of the extensions we will need is the continuous speech recognition extension, which will enable the app to continuously listen for user input without requiring them to tap the button repeatedly. This feature provides a hands-free and more convenient user experience.

We will also import the media extension to handle image and animation files. This will allow us to add visual elements to the app, such as the animated gif file for the chatbot's face. We will use this gif file to animate the chatbot's face and make the app more visually engaging.

By designing the chatbot app with a user-friendly interface, incorporating visual elements, and enabling speech recognition and text-to-speech capabilities, we can create an interactive and engaging user experience. Let's Continue by implementing the chat GPT functionality.

Implementing Chat GPT Functionality

Now that we have designed the user interface, it's time to implement the chat GPT functionality in our app. This involves initializing the chat response, getting user input, contacting the chat GPT servers, handling the response, converting the response to speech, and displaying the response in the web viewer.

We will start by initializing the global variable "chat response" as an empty text block. This variable will hold the generated response from the chat GPT servers. We will use this variable throughout the app to store and retrieve the chatbot's responses.

To capture the user's input, we will use the speech recognizer extension. When the user clicks the "Speak Now" button, the app will invoke the speech recognizer to convert their speech into text. This text will serve as the prompt for the chat GPT servers.

After capturing the user's input, we will contact the chat GPT servers using the web component. We will call the procedure that sends the user's input to the servers and retrieves the response. This procedure will take the user's input as input and return the generated response from the servers.

Once we receive the response from the servers, we will handle it using the "got text" event of the web component. We will save the response as the chat response by using the "set global chat response" block. This will enable us to access the response throughout the app.

To convert the chat response to speech, we will use the text-to-speech component. We will pass the chat response as the message to the component and it will convert the text into audio. This audio output will then be played back to the user using the device's speakers or headphones.

Finally, we will update the web viewer component with the generated response. We will set the web viewer's URL to the talking girl's gif file. This will display the animated gif file in the web viewer and give the impression that the chatbot is talking to the user.

By implementing the chat GPT functionality, we can enhance the chatbot app by enabling it to generate creative and intelligent responses to user queries. The app will capture the user's speech, convert it into text, send it to the chat GPT servers, receive a response, convert it into speech, and display it in the web viewer. Let's proceed to packaging and testing the app.

Packaging and Testing the App

To test the app, we can use the MIT App Inventor companion app, which allows us to test the app on an Android device. However, if we want to install the app on our device or share it with others, we need to edit the APK file and make some changes.

First, we need to download the APK editor if we haven't done so already. This editor will allow us to modify the APK file and customize the app. We will provide a link to download the APK editor in the video description.

Once we have the APK editor, we need to open the APK file that we want to modify. The APK file can be downloaded from the MIT App Inventor Website or exported from the project. We will open the APK file using the APK editor.

Inside the APK editor, we need to open the Android Manifest file. This file contains the configuration settings for the app. We will open the file using a text editor, such as Notepad or Sublime Text.

In the Android Manifest file, we need to add a permission block for audio or microphone. This will enable the app to access the device's microphone and listen for user input. We will add the permission block after the existing "uses-permission" block.

After making the necessary changes, we need to save the Android Manifest file and close it. We should ensure that the changes are saved and the file is closed before proceeding.

Next, we need to save the modified APK file using the APK editor. We can save it with a new name to distinguish it from the original APK file.

Once we have the modified APK file, we can install it on our Android device or share it with others. To install the app, we can transfer the APK file to our device and then open it to initiate the installation process. We may need to enable installation from unknown sources in the device settings.

When starting the app, we should grant microphone permission to the app. This will allow the app to listen for user input and generate responses based on the received speech.

By packaging and testing the app, we can ensure that it functions correctly on an Android device. We can also customize the app by editing the APK file to meet our specific requirements.

Conclusion

In conclusion, we have learned how to create a chatbot app using MIT App Inventor. We started with a basic chatbot app that had predefined responses and used speech recognition to understand user input. Then, we integrated OpenAI's GPT to generate creative responses. We covered the process of registering an OpenAI account, generating an API key, and authenticating the app. We designed the user interface, imported necessary extensions, and implemented the chat GPT functionality. Finally, we packaged the app and tested it on an Android device.

By following this tutorial, You can create your own chatbot app and enhance it with advanced functionalities. The app will be able to understand user queries, generate intelligent responses, and communicate with users using speech. Whether you want to create a personal assistant, a customer support chatbot, or an AI-powered companion, this tutorial will provide you with the necessary tools and knowledge.

Remember to have fun exploring the capabilities of chat GPT and continue learning and experimenting with new technologies. The possibilities are endless, and you can create amazing and innovative apps that make a positive impact in the lives of your users.

Pros:

• The app provides a user-friendly and interactive interface.
• It integrates OpenAI's GPT to generate creative and intelligent responses.
• The chatbot uses speech recognition to understand user queries.
• The app allows customization through the use of extensions.
• The tutorial provides step-by-step instructions and explanations.

Cons:

• The app may not work on iPhones due to limitations with OpenAI's GPT.
• Upgrading the OpenAI account may incur additional costs.
• Editing the APK file requires technical knowledge.
• The tutorial assumes basic familiarity with MIT App Inventor.
• Continuous speech recognition may drain the device's battery faster.