Open Ai in Flutter: Step by Step building a basic language translation application with Text completion

I can provide you with a full tutorial on how to implement the “Language Translation” example using OpenAI Text Completion with Flutter. Here are the steps you can follow:

Step 1: Set up OpenAI API access

To use OpenAI Text Completion API, you need to create an account and obtain an API key. Follow the steps below to create an account and get an API key:

1. Go to the OpenAI website (https://openai.com/) and click on the “Sign Up” button.
2. Fill in your details and click on the “Create Account” button.
3. Once you are logged in, go to the API dashboard (https://beta.openai.com/dashboard/api-keys).
4. Click on the “Create New API Key” button to generate an API key.
5. Copy the API key and save it for later use.

Step 2: Set up the Flutter project

Create a new Flutter project using Android Studio or Visual Studio Code. Then, add the following dependencies to the pubspec.yaml file:

dependencies:
  http: 

The “http” package is used to send HTTP requests to the OpenAI API.

Step 3: Build the UI

Create a new Dart file and name it “translation_screen.dart”. In this file, you can create the UI for the language translation screen. You can use a simple design with two text fields, one for the input text and the other for the translated text. Here is an example of how the UI can look:

import 'package:flutter/material.dart';

class TranslationScreen extends StatefulWidget {
  @override
  _TranslationScreenState createState() => _TranslationScreenState();
}

class _TranslationScreenState extends State<TranslationScreen> {
  final _inputController = TextEditingController();
  final _outputController = TextEditingController();

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(
        title: Text('Language Translation'),
      ),
      body: Padding(
        padding: EdgeInsets.all(16.0),
        child: Column(
          crossAxisAlignment: CrossAxisAlignment.stretch,
          children: [
            TextField(
              controller: _inputController,
              decoration: InputDecoration(
                labelText: 'Input Text',
                border: OutlineInputBorder(),
              ),
              maxLines: null,
            ),
            SizedBox(height: 16.0),
            TextField(
              controller: _outputController,
              decoration: InputDecoration(
                labelText: 'Translated Text',
                border: OutlineInputBorder(),
              ),
              maxLines: null,
            ),
            SizedBox(height: 16.0),
            ElevatedButton(
              onPressed: _translateText,
              child: Text('Translate'),
            ),
          ],
        ),
      ),
    );
  }

  void _translateText() async {
    //TODO: Implement OpenAI API call
  }
}

Step 4: Implement the OpenAI API call

In the _translateText() method, you can send an HTTP request to the OpenAI API to translate the input text. Here is an example of how you can implement the API call:

import 'package:http/http.dart' as http;
import 'dart:convert';

void _translateText() async {
  final apiKey = 'YOUR_API_KEY_HERE';
  final prompt = _inputController.text;
  final model = 'text-davinci-002';
  final url = 'https://api.openai.com/v1/completions';

  final response = await http.post(
    Uri.parse(url),
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer $apiKey',
    },
    body: json.encode({
      'prompt': '$prompt (translate to Spanish)',
      'model': model,
      'temperature': 0.5,
      'max_tokens': 60,
      'n': 1,
      'stop': ['\n']
    }),
  );

  if (response.statusCode == 200) {
    final data = json.decode(response.body);
    final output = data['choices'][0]['text'];
    setState(() {
      _outputController.text = output;
    });
  } else {
    print('Error: ${response.reasonPhrase}');
  }
}

In this code, you first retrieve the API key from the OpenAI dashboard. Then, you get the input text from the text field and set the model to “text-davinci-002”, which is a model that is capable of performing a wide range of natural language tasks, including translation.

You then send an HTTP POST request to the OpenAI API using the http.post() method from the http package. The request contains the following parameters:

• prompt: The input text to be translated, followed by the language to which it should be translated (in this case, “translate to Spanish”).
• model: The ID of the OpenAI model to use for the translation. In this case, we’re using the “text-davinci-002” model.
• temperature: A value between 0 and 1 that controls the randomness of the output. A higher temperature will result in more diverse and creative output, while a lower temperature will result in more predictable and conservative output. In this case, we’re using a temperature of 0.5, which is a good balance between creativity and predictability.
• max_tokens: The maximum number of tokens (words or symbols) to generate in the output. In this case, we’re using a value of 60, which should be sufficient for most translations.
• n: The number of output texts to generate. In this case, we’re generating only one output text.
• stop: A list of tokens that, when generated, will signal the end of the output text. In this case, we’re using the newline character (\n) as the stop token.

If the API call is successful (i.e., if the response status code is 200), you extract the translated text from the response body and update the output text field using the setState() method. If the API call is unsuccessful, you print an error message to the console.

Step 5: Test the app

Now that you have implemented the translation functionality, you can test the app by running it on a device or an emulator. To do so, you can run the following command in your terminal:

flutter run

This will build and run the app on a connected device or emulator. Once the app is running, you can enter some text in the input text field and press the “Translate” button to see the translated output.

You can also experiment with different input texts and model configurations to see how the translation output changes. For example, you can try using a different OpenAI model, adjusting the temperature or maximum token count, or changing the language to which the input text should be translated.

More:

•  OpenAI in Flutter: Advanced Examples of Using Text Completion
• Text completion in OpenAI. What is this? What is text completion used for?

Congratulations! You have successfully implemented a “Language Translation” example using OpenAI Text Completion with Flutter. You can now use this example as a starting point for building more sophisticated natural language processing applications using the OpenAI API.