Creating an Android Quiz Application with OpenAI API6 min read

Intro

In this guide, I will explain how to set up a connection to the OpenAI API in an Android project. To be precise, I will focus on text completion. OpenAI API contains more features – see the API overview.

The idea here is to create a Quiz Application. Based on a given subject, API will generate 10 questions. Each question will have 4 possible answers. The correct answer will be marked, too. With the sprinkle of magic, GPT will act as a backend for our application.

Here is the repository link.

Architecture

I will use an MVVM architecture with StateFlow and Compose for UI. When it comes to OpenAI connection, there is no official library for Android as of today. Because of that, I am using an unofficial library, which also works with coroutines and Kotlin Multiplatform.

Of course, you can also connect to the API manually using any HTTP client, such as Retrofit, Ktor, or OkHttp. If you want to go down this road, here’s the API reference.

I will include the logic in a single ViewModel, which might not be an ideal solution, but it is good enough for a project of this size. The ViewModel will receive an event and update the state via StateFlow. Activity will collect this StateFlow, and render the adequate UI.

Let’s break it down

The general flow looks like this:

  1. The user enters the subject
  2. The app connects to the backend and receives a set of questions and answers
  3. Questions get displayed one after another
  4. When questions are answered, a summary screen is shown

So, we need 3 screens:

  1. InputSubjectScreen, where the user will enter the subject
  2. QuestionScreen, where the question will be displayed
  3. SummaryScreen, where the score will be displayed

Implementing logic and model

State

The screens listed below can be translated into possible UI states. The state is represented by a sealed class.

Event

We can create another sealed class for events that users might trigger:

ViewModel

Base ViewModel structure contains StateFlow and onEvent() function. Here, we will also hold generated questions and the question index, which will tell which question is currently displayed. Question class is not defined yet, we will get there in a second!

Getting questions from the API

When a subject is entered, we update the state to display the loading screen and then connect to the OpenAI API to get the questions. With this prompt, we should get a JSON response. I am using gpt-3.5-turbo model because of the response time, but you can use gpt-4. It might take more time to complete, but the quality of the response is a lot better.

You need to provide your own API key (marked in the code as API_KEY). If you are not sure how to do it, visit this link.

A word of warning!

There’s no guarantee that the model will always return the correct JSON structure, even with the “Return JSON and nothing else” phrase in the prompt. I never had this issue with gpt-4, but it happened a few times with gpt-3.5-turbo. Remember to handle this case if you want to use this API connection in the production environment.

Let’s go through the remaining events

When the question is answered, the answer is saved in the Question model. Then, if there are any questions left, the next question is shown. If not, the amount of correct answers is summed up, and a summary screen is shown.

The easiest event to handle is when the quiz is restarted. The question index is restarted back to 0, and the state is updated, so the user should see the first screen again.

Model

Time for the missing part – we need classes that will represent the Question and the Answer. I defined the structure in the prompt above. Here is the relevant part:

Basing on this, I created adequate model classes:

userAnswerId is not a part of API response – it is used to save the answer picked by a user. That is why it does not have a @SerializedName annotation.

The harder part is done! Now we can move to the visual part of the application.

Implementing User Interface

Activity and navigation

The Activity serves as an entry point and renders the screen based on the collected state from our ViewModel.

Input Subject Screen

This is the first screen that the user sees. It accepts a function as a parameter to run when the subject is entered, which is simply passing an event to the ViewModel. The screen contains a TextField and a Button.

Loading Screen

The loading screen is a stateless screen with Text and an Indicator. Easy one.

Question Screen

The question screen is responsible for showing the question and possible answers. It does accept the Question and the action to execute when the question is answered.

Summary Screen

And finally, the summary screen shows the final score and contains a button that allows the user to restart the quiz. This is done, like above, by sending an event to the ViewModel.

Ideas for improvement

I tried to keep the code as simple as possible for learning purposes, but obviously, there is plenty of room for improvement. Here are some ideas:

Handle parsing error

As I mentioned, it is not always guaranteed that we receive a correct JSON structure from the API. It would be wise to handle this case with an elegant error screen. Also, it is possible that the response will be correct, but it will have an additional sentence at the end, especially with gpt-3.5-turbo and older models. Because of that, you can try to search for JSON in the response, extract it and ignore the rest.

Handle timeouts

Another idea is to handle the timeout error. Sometimes, getting the response will take some time, especially when the API is overloaded. gpt-3.5-turbo is visibly faster than gpt-4. The timeout value can be set in MainViewModel:62:

Instantly Marking the Correct Answer After Selection

Right now, the result is visible at the end of the quiz, on the Summary Screen. Marking the correct answer instantly might improve the user experience, providing faster feedback.

Outro

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