Flutter Gemini

Google Gemini is a set of cutting-edge large language models (LLMs) designed to be the driving force behind Google's future AI initiatives.

This package provides a powerful bridge between your Flutter application and Google's revolutionary Gemini AI. It empowers you to seamlessly integrate Gemini's capabilities into your app, unlocking possibilities for building innovative, intelligent, and engaging experiences that redefine user interaction.

Features

Set up your API key scroll
Initialize Gemini scroll
Content-based APIs scroll
- Stream Generate Content scroll
- Text-only input scroll
- Text-and-image input scroll
- Multi-turn conversations (chat) scroll
- Count tokens scroll
- Model info scroll
- List models scroll
- EmbedContents and batchEmbedContents scroll
Flutter Gemini widgets scroll

Getting started

To use the Gemini API, you'll need an API key. If you don't already have one, create a key in Google AI Studio. Get an API key.

online demo

https://babakcode.github.io/flutter_gemini

Initialize Gemini

For initialization, you must call the init constructor for Flutter Gemini in the main function.

void main() {

  /// Add this line
  Gemini.init(apiKey: '--- Your Gemini Api Key ---');

  runApp(const MyApp());
}

Now you can create an instance

Content-based APIs

Stream Generate Content

The model usually gives a response once it finishes generating the entire output. To speed up interactions, you can opt not to wait for the complete result and instead use streaming to manage partial results.

final gemini = Gemini.instance;

gemini.streamGenerateContent('Utilizing Google Ads in Flutter')
  .listen((value) {
    print(value.output);
  }).onError((e) {
    log('streamGenerateContent exception', error: e);
  });

Text-only input

This feature lets you perform natural language processing (NLP) tasks such as text completion and summarization.

final gemini = Gemini.instance;

gemini.text("Write a story about a magic backpack.")
  .then((value) => print( value?.output )) /// or value?.content?.parts?.last.text
  .catchError((e) => print(e));

Text-and-image input

If the input contains both text and image, You can send a text prompt with an image to the gemini-pro-vision model to perform a vision-related task. For example, captioning an image or identifying what's in an image.

  final gemini = Gemini.instance;

  final file = File('assets/img.png');
  gemini.textAndImage(
        text: "What is this picture?", /// text
        images: [file.readAsBytesSync()] /// list of images
      )
      .then((value) => log(value?.content?.parts?.last.text ?? ''))
      .catchError((e) => log('textAndImageInput', error: e));

Note that, there are changes on properties

- image: file.readAsBytesSync(), /// image
+ images: [file.readAsBytesSync()] /// list of images

Multi-turn conversations (chat)

Using Gemini, you can build freeform conversations across multiple turns.

  final gemini = Gemini.instance;

  gemini.chat([
    Content(parts: [
      Parts(text: 'Write the first line of a story about a magic backpack.')],
        role: 'user'),
    Content(parts: [ 
      Parts(text: 'In the bustling city of Meadow brook, lived a young girl named Sophie. She was a bright and curious soul with an imaginative mind.')],
        role: 'model'),
    Content(parts: [ 
      Parts(text: 'Can you set it in a quiet village in 1600s France?')], 
        role: 'user'),
    ])
        .then((value) => log(value?.output ?? 'without output'))
        .catchError((e) => log('chat', error: e));

Count tokens

When using long prompts, it might be useful to count tokens before sending any content to the model.

final gemini = Gemini.instance;

gemini.countTokens("Write a story about a magic backpack.")
    .then((value) => print(value)) /// output like: `6` or `null`
    .catchError((e) => log('countTokens', error: e));

Model info

If you GET a model's URL, the API uses the get method to return information about that model such as version, display name, input token limit, etc.

final gemini = Gemini.instance;

gemini.info(model: 'gemini-pro')
    .then((info) => print(info))
    .catchError((e) => log('info', error: e));

List models

If you GET the models directory, it uses the list method to list all of the models available through the API, including both the Gemini and PaLM family models.

final gemini = Gemini.instance;

gemini.listModels()
    .then((models) => print(models)) /// list
    .catchError((e) => log('listModels', error: e));

embedContents and batchEmbedContents

Embedding is a method that transforms information, like text, into a list of floating-point numbers in an array. Gemini enables the representation of text, such as words or sentences, in a vectorized form. This facilitates the comparison of embeddings, allowing for the identification of similarities between texts through mathematical techniques like cosine similarity. For instance, texts with similar subject matter or sentiment should exhibit similar embeddings.

/// `embedContents`
gemini.embedContent('text').then((value) {
  print(value); /// output like: [ 1.3231, 1.33421, -0.123123 ]
});

/// `batchEmbedContents`
gemini.batchEmbedContents(['text 1', 'text 2']).then((value) {
  print(value); /// output like: [ [ 1.3231, 1.33421, -0.123123 ] ]
});

Flutter Gemini Widgets

The GeminiResponseTypeView widget allows you to present your search results using a typing animation, all without the need for utilizing the setState() function!

final Widget result = GeminiResponseTypeView(
  builder: (context, child, response, loading) {

    if (loading) {
      /// show loading animation or use CircularProgressIndicator();
      return Lottie.asset('assets/lottie/ai.json');
    }

    /// The runtimeType of response is String?
    if (response != null) {
      return Markdown(
        data: response,
        selectable: true,
      );
    } else {
      
      /// idle state
      return const Center(child: Text('Search something!'));
    }
  },
);

zmhu / flutter_gemini Goto Github PK

flutter_gemini's Introduction