gemini_flutter 0.1.1

Discover the ultimate Flutter package for seamless integration with Gemini APIs in your Flutter projects – simplifying API usage like never before!

gemini_flutter

Gemini Flutter is a Dart package that simplifies the integration of Gemini APIs into your Flutter applications. It provides convenient methods for making requests to Gemini models and handling responses.

New Features

Version 0.1.1

1. countTokens() method added

   Users now have the flexibility to externally utilize the countTokens() method. In the past, the only option available for users was to enable token counting by setting the flag to true in API calls.

Version 0.1.0

1. Gemini-Pro-Vision API Method Added

   Introducing the gemini-pro-vision API method to enhance the capabilities of the system. This new method allows users to leverage the advanced features provided by Gemini Pro Vision, opening up opportunities for improved vision-related functionalities.
   Effortlessly manage your image uploads with our system. You can provide the image path, submit the image as a File object, or even directly use base64 encoding – we take care of the rest for you!

2. Token Count Functionality

   A new functionality has been added to count tokens for all methods. Users can enable token counting by setting the countToken parameter in the respective methods.

Getting Started

To use this package, follow the steps below:

1. Add the gemini_flutter dependency to your pubspec.yaml file:

   dependencies:
     gemini_flutter: ^0.0.1+1
   

   Then, run flutter pub get to fetch the dependency.

2. In your Dart code, import the package:

   import 'package:gemini_flutter/gemini_flutter.dart';
   

3. Initialize the GeminiHandler in your main function:

   void main() {
     GeminiHandler().initialize(
       apiKey: "YOUR_API_KEY",
       temperature: 0.7,
       topK: 50,
       topP: 0.8,
       outputLength: 100,
     );
   
     runApp(MyApp());
   }
   

   Make sure to replace "YOUR_API_KEY" with your actual Gemini API key. You can also customize other parameters as needed.

Parameters

• temperature: This parameter controls the randomness of the generated text. A higher temperature (e.g., 0.9) produces more diverse and creative output, while a lower value (e.g., 0.1) tends to make the output more focused and deterministic.

• topP: This parameter is used in nucleus sampling to control the diversity of the generated text. It represents the cumulative probability of the top-k candidates to sample from. In your example, it is set to 0.95.

• topK: This parameter is the number of top candidates from which to sample during nucleus sampling. In your example, it is set to 40.

• outputLength: This parameter specifies the desired length of the generated text. In your example, it is set to 1024 characters.

Usage

Text Generation

void generateText() async {
  final response = await GeminiHandler().geminiPro(
    text: "Write a long story about a magic backpack.",
    temprature: 0.9,
    topP: 0.95,
    topK: 40,
    outputLength: 1024,
  );

  if (response != null) {
    print("Generated Text: ${response.generatedText}");
  }
}

Inside the function, the geminiPro method of the GeminiHandler class is called. This method appears to generate text using the Gemini API. It takes various parameters:

• text: The prompt for text generation ("Write a long story about a magic backpack.").
• temperature: A parameter controlling the randomness of the generated text (0.9 in this case).
• topP: A parameter for nucleus sampling, controlling diversity (0.95 in this case).
• topK: Number of top candidates to sample from during nucleus sampling (40 in this case).
• outputLength: The desired length of the generated text (1024 characters).
• countTokens: Logs the number of tokens that will be consumed by this prompt.

Gemini-pro-vision

Generate text from an image or let gemini pro tell you whats in your image using the geminiProVision method:

void generateTextFromImage() async {
  final response = await GeminiHandler().geminiProVision(
    text: "Write a long story about the image.",
    path: "path/to/your/image.jpg",
    temprature: 0.9,
    topP: 0.95,
    topK: 40,
    outputLength: 1024,
  );

  if (response != null) {
    print("Generated Text: ${response.generatedText}");
  }
}

The above code snippet GeminiHandler class to perform text generation with the geminiProVision method. Here's an explanation of the parameters:

• text: This parameter expects a string representing the input text or prompt for generating the output. In your example, it is set to "Write a long story about the image."

• path: This parameter specifies the path to the image file. In your example, it is set to "path/to/your/image.jpg." It seems to indicate that the text generation is somehow related to the content or context of the image. pack.").

• imageBase64: This prameter takes the image as base64 format which is the form that Gemini APIs accept the image in.

• file: This prameter takes image as File object, all the other conversions are handle in the package

• temperature: A parameter controlling the randomness of the generated text (0.9 in this case).

• topP: A parameter for nucleus sampling, controlling diversity (0.95 in this case).

• topK: Number of top candidates to sample from during nucleus sampling (40 in this case).

• outputLength: The desired length of the generated text (1024 characters).

• countTokens: Logs the number of tokens that will be consumed by this prompt.