Analyze image files using the Gemini API

You can ask a Gemini model to analyze image files that you provide either inline (base64-encoded) or via URL. When you use Vertex AI in Firebase, you can make this request directly from your app.

With this capability, you can do things like:

  • Create captions or answer questions about images
  • Write a short story or a poem about an image
  • Detect objects in an image and return bounding box coordinates for them
  • Label or categorize a set of images for sentiment, style, or other characteristic

Jump to code samples Jump to code for streamed responses


See other guides for additional options for working with images
Generate structured output Multi-turn chat Generate images

Before you begin

If you haven't already, complete the getting started guide, which describes how to set up your Firebase project, connect your app to Firebase, add the SDK, initialize the Vertex AI service, and create a GenerativeModel instance.

For testing and iterating on your prompts and even getting a generated code snippet, we recommend using Vertex AI Studio.

Send image files (base64-encoded) & receive text

Make sure that you've completed the Before you begin section of this guide before trying this sample.

You can ask a Gemini model to generate text by prompting with text and images—providing each input file's mimeType and the file itself. Find requirements and recommendations for input files later on this page.

Swift

You can call generateContent() to generate text from multimodal input of text and images.

Single file input 

import FirebaseVertexAI

// Initialize the Vertex AI service
let vertex = VertexAI.vertexAI()

// Create a `GenerativeModel` instance with a model that supports your use case
let model = vertex.generativeModel(modelName: "gemini-2.0-flash")

guard let image = UIImage(systemName: "bicycle") else { fatalError() }

// Provide a text prompt to include with the image
let prompt = "What's in this picture?"

// To generate text output, call generateContent and pass in the prompt
let response = try await model.generateContent(image, prompt)
print(response.text ?? "No text in response.")

Multiple file input

import FirebaseVertexAI

// Initialize the Vertex AI service
let vertex = VertexAI.vertexAI()

// Create a `GenerativeModel` instance with a model that supports your use case
let model = vertex.generativeModel(modelName: "gemini-2.0-flash")

guard let image1 = UIImage(systemName: "car") else { fatalError() }
guard let image2 = UIImage(systemName: "car.2") else { fatalError() }

// Provide a text prompt to include with the images
let prompt = "What's different between these pictures?"

// To generate text output, call generateContent and pass in the prompt
let response = try await model.generateContent(image1, image2, prompt)
print(response.text ?? "No text in response.")

Kotlin

You can call generateContent() to generate text from multimodal input of text and images.

For Kotlin, the methods in this SDK are suspend functions and need to be called from a Coroutine scope.

Single file input 

// Initialize the Vertex AI service and create a `GenerativeModel` instance
// Specify a model that supports your use case
val generativeModel = Firebase.vertexAI.generativeModel("gemini-2.0-flash")

// Loads an image from the app/res/drawable/ directory
val bitmap: Bitmap = BitmapFactory.decodeResource(resources, R.drawable.sparky)

// Provide a prompt that includes the image specified above and text
val prompt = content {
  image(bitmap)
  text("What developer tool is this mascot from?")
}

// To generate text output, call generateContent with the prompt
val response = generativeModel.generateContent(prompt)
print(response.text)

Multiple file input

For Kotlin, the methods in this SDK are suspend functions and need to be called from a Coroutine scope. 
// Initialize the Vertex AI service and create a `GenerativeModel` instance
// Specify a model that supports your use case
val generativeModel = Firebase.vertexAI.generativeModel("gemini-2.0-flash")

// Loads an image from the app/res/drawable/ directory
val bitmap1: Bitmap = BitmapFactory.decodeResource(resources, R.drawable.sparky)
val bitmap2: Bitmap = BitmapFactory.decodeResource(resources, R.drawable.sparky_eats_pizza)

// Provide a prompt that includes the images specified above and text
val prompt = content {
  image(bitmap1)
  image(bitmap2)
  text("What is different between these pictures?")
}

// To generate text output, call generateContent with the prompt
val response = generativeModel.generateContent(prompt)
print(response.text)

Java

You can call generateContent() to generate text from multimodal input of text and images.

For Java, the methods in this SDK return a ListenableFuture.

Single file input 

// Initialize the Vertex AI service and create a `GenerativeModel` instance
// Specify a model that supports your use case
GenerativeModel gm = FirebaseVertexAI.getInstance()
        .generativeModel("gemini-2.0-flash");
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Bitmap bitmap = BitmapFactory.decodeResource(getResources(), R.drawable.sparky);

// Provide a prompt that includes the image specified above and text
Content content = new Content.Builder()
        .addImage(bitmap)
        .addText("What developer tool is this mascot from?")
        .build();

// To generate text output, call generateContent with the prompt
ListenableFuture<GenerateContentResponse> response = model.generateContent(content);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        String resultText = result.getText();
        System.out.println(resultText);
    }

    @Override
    public void onFailure(Throwable t) {
        t.printStackTrace();
    }
}, executor);

Multiple file input

// Initialize the Vertex AI service and create a `GenerativeModel` instance
// Specify a model that supports your use case
GenerativeModel gm = FirebaseVertexAI.getInstance()
        .generativeModel("gemini-2.0-flash");
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Bitmap bitmap1 = BitmapFactory.decodeResource(getResources(), R.drawable.sparky);
Bitmap bitmap2 = BitmapFactory.decodeResource(getResources(), R.drawable.sparky_eats_pizza);

// Provide a prompt that includes the images specified above and text
Content prompt = new Content.Builder()
    .addImage(bitmap1)
    .addImage(bitmap2)
    .addText("What's different between these pictures?")
    .build();

// To generate text output, call generateContent with the prompt
ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        String resultText = result.getText();
        System.out.println(resultText);
    }

    @Override
    public void onFailure(Throwable t) {
        t.printStackTrace();
    }
}, executor);

Web

You can call generateContent() to generate text from multimodal input of text and images.

Single file input 

import { initializeApp } from "firebase/app";
import { getVertexAI, getGenerativeModel } from "firebase/vertexai";

// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Vertex AI service
const vertexAI = getVertexAI(firebaseApp);

// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(vertexAI, { model: "gemini-2.0-flash" });

// Converts a File object to a Part object.
async function fileToGenerativePart(file) {
  const base64EncodedDataPromise = new Promise((resolve) => {
    const reader = new FileReader();
    reader.onloadend = () => resolve(reader.result.split(',')[1]);
    reader.readAsDataURL(file);
  });
  return {
    inlineData: { data: await base64EncodedDataPromise, mimeType: file.type },
  };
}

async function run() {
  // Provide a text prompt to include with the image
  const prompt = "What's different between these pictures?";

  const fileInputEl = document.querySelector("input[type=file]");
  const imagePart = await fileToGenerativePart(fileInputEl.files[0]);

  // To generate text output, call generateContent with the text and image
  const result = await model.generateContent([prompt, imagePart]);

  const response = result.response;
  const text = response.text();
  console.log(text);
}

run();

Multiple file input

import { initializeApp } from "firebase/app";
import { getVertexAI, getGenerativeModel } from "firebase/vertexai";

// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Vertex AI service
const vertexAI = getVertexAI(firebaseApp);

// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(vertexAI, { model: "gemini-2.0-flash" });

// Converts a File object to a Part object.
async function fileToGenerativePart(file) {
  const base64EncodedDataPromise = new Promise((resolve) => {
    const reader = new FileReader();
    reader.onloadend = () => resolve(reader.result.split(',')[1]);
    reader.readAsDataURL(file);
  });
  return {
    inlineData: { data: await base64EncodedDataPromise, mimeType: file.type },
  };
}

async function run() {
  // Provide a text prompt to include with the images
  const prompt = "What's different between these pictures?";

  // Prepare images for input
  const fileInputEl = document.querySelector("input[type=file]");
  const imageParts = await Promise.all(
    [...fileInputEl.files].map(fileToGenerativePart)
  );

  // To generate text output, call generateContent with the text and images
  const result = await model.generateContent([prompt, ...imageParts]);

  const response = result.response;
  const text = response.text();
  console.log(text);
}

run();

Dart

You can call generateContent() to generate text from multimodal input of text and images.

Single file input 

import 'package:firebase_vertexai/firebase_vertexai.dart';
import 'package:firebase_core/firebase_core.dart';
import 'firebase_options.dart';

await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Vertex AI service and create a `GenerativeModel` instance
// Specify a model that supports your use case
final model =
      FirebaseVertexAI.instance.generativeModel(model: 'gemini-2.0-flash');

// Provide a text prompt to include with the image
final prompt = TextPart("What's in the picture?");
// Prepare images for input
final image = await File('image0.jpg').readAsBytes();
final imagePart = InlineDataPart('image/jpeg', image);

// To generate text output, call generateContent with the text and image
final response = await model.generateContent([
  Content.multi([prompt,imagePart])
]);
print(response.text);

Multiple file input

import 'package:firebase_vertexai/firebase_vertexai.dart';
import 'package:firebase_core/firebase_core.dart';
import 'firebase_options.dart';

await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Vertex AI service and create a `GenerativeModel` instance
// Specify a model that supports your use case
final model =
      FirebaseVertexAI.instance.generativeModel(model: 'gemini-2.0-flash');

final (firstImage, secondImage) = await (
  File('image0.jpg').readAsBytes(),
  File('image1.jpg').readAsBytes()
).wait;
// Provide a text prompt to include with the images
final prompt = TextPart("What's different between these pictures?");
// Prepare images for input
final imageParts = [
  InlineDataPart('image/jpeg', firstImage),
  InlineDataPart('image/jpeg', secondImage),
];

// To generate text output, call generateContent with the text and images
final response = await model.generateContent([
  Content.multi([prompt, ...imageParts])
]);
print(response.text);

Learn how to choose a model and optionally a location appropriate for your use case and app.

Stream the response

Make sure that you've completed the Before you begin section of this guide before trying this sample.

You can achieve faster interactions by not waiting for the entire result from the model generation, and instead use streaming to handle partial results. To stream the response, call generateContentStream.


Requirements and recommendations for input image files

See "Supported input files and requirements for the Vertex AI Gemini API" to learn detailed information about the following:

Supported image MIME types

Gemini multimodal models support the following image MIME types:

Image MIME type Gemini 2.0 Flash Gemini 2.0 Flash‑Lite
PNG - image/png
JPEG - image/jpeg
WebP - image/webp

Limits per request

There isn't a specific limit to the number of pixels in an image. However, larger images are scaled down and padded to fit a maximum resolution of 3072 x 3072 while preserving their original aspect ratio.

Here's the maximum number of image files allowed in a prompt request:

  • Gemini 2.0 Flash and Gemini 2.0 Flash‑Lite: 3000 images


What else can you do?

  • Learn how to count tokens before sending long prompts to the model.
  • Set up Cloud Storage for Firebase so that you can include large files in your multimodal requests and have a more managed solution for providing files in prompts. Files can include images, PDFs, video, and audio.
  • Start thinking about preparing for production, including setting up Firebase App Check to protect the Gemini API from abuse by unauthorized clients. Also, make sure to review the production checklist.

Try out other capabilities

Learn how to control content generation

You can also experiment with prompts and model configurations using Vertex AI Studio.

Learn more about the supported models

Learn about the models available for various use cases and their quotas and pricing.


Give feedback about your experience with Vertex AI in Firebase