Генерируйте и редактируйте изображения с помощью Gemini Nano Banana

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
let model = FirebaseAI.firebaseAI(backend: .googleAI()).generativeModel(
  modelName: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: GenerationConfig(responseModalities: [.image])
)

// Provide a text prompt instructing the model to generate an image
let prompt = "Generate an image of the Eiffel tower with fireworks in the background."

// To generate an image, call `generateContent` with the text input
let response = try await model.generateContent(prompt)

// Handle the generated image
guard let inlineDataPart = response.inlineDataParts.first else {
  fatalError("No image data in response.")
}
guard let uiImage = UIImage(data: inlineDataPart.data) else {
  fatalError("Failed to convert data to UIImage.")
}

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "gemini-nano-banana-2.1",
    // Configure the model to respond with images only.
    generationConfig = generationConfig {
responseModalities = listOf(ResponseModality.IMAGE) }
)

// Provide a text prompt instructing the model to generate an image
val prompt = "Generate an image of the Eiffel tower with fireworks in the background."

// To generate image output, call `generateContent` with the text input
val generatedImageAsBitmap = model.generateContent(prompt)
    // Handle the generated image
    .candidates.first().content.parts.filterIsInstance<ImagePart>().firstOrNull()?.image

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel(
    "gemini-nano-banana-2.1",
    // Configure the model to respond with images only.
    new GenerationConfig.Builder()
        .setResponseModalities(Arrays.asList(ResponseModality.IMAGE))
        .build()
);

GenerativeModelFutures model = GenerativeModelFutures.from(ai);

// Provide a text prompt instructing the model to generate an image
Content prompt = new Content.Builder()
        .addText("Generate an image of the Eiffel Tower with fireworks in the background.")
        .build();

// To generate an image, call `generateContent` with the text input
ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) { 
        // iterate over all the parts in the first candidate in the result object
        for (Part part : result.getCandidates().get(0).getContent().getParts()) {
            if (part instanceof ImagePart) {
                ImagePart imagePart = (ImagePart) part;
                // The returned image as a bitmap
                Bitmap generatedImageAsBitmap = imagePart.getImage();
                break;
            }
        }
    }

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

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend, ResponseModality } from "firebase/ai";

// 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 Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(ai, {
  model: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: {
    responseModalities: [ResponseModality.IMAGE],
  },
});

// Provide a text prompt instructing the model to generate an image
const prompt = 'Generate an image of the Eiffel Tower with fireworks in the background.';

// To generate an image, call `generateContent` with the text input
const result = model.generateContent(prompt);

// Handle the generated image
try {
  const inlineDataParts = result.response.inlineDataParts();
  if (inlineDataParts?.[0]) {
    const image = inlineDataParts[0].inlineData;
    console.log(image.mimeType, image.data);
  }
} catch (err) {
  console.error('Prompt or candidate was blocked:', err);
}

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

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

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'gemini-nano-banana-2.1',
  // Configure the model to respond with images only.
  generationConfig: GenerationConfig(responseModalities: [ResponseModalities.image]),
);

// Provide a text prompt instructing the model to generate an image
final prompt = [Content.text('Generate an image of the Eiffel Tower with fireworks in the background.')];

// To generate an image, call `generateContent` with the text input
final response = await model.generateContent(prompt);
if (response.inlineDataParts.isNotEmpty) {
  final imageBytes = response.inlineDataParts[0].bytes;
  // Process the image
} else {
  // Handle the case where no images were generated
  print('Error: No images were generated.');
}

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
var model = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetGenerativeModel(
  modelName: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: new GenerationConfig(
    responseModalities: new[] { ResponseModality.Image })
);

// Provide a text prompt instructing the model to generate an image
var prompt = "Generate an image of the Eiffel Tower with fireworks in the background.";

// To generate an image, call `GenerateContentAsync` with the text input
var response = await model.GenerateContentAsync(prompt);

// Handle the generated image
var imageParts = response.Candidates.First().Content.Parts
                         .OfType<ModelContent.InlineDataPart>()
                         .Where(part => part.MimeType == "image/png");
foreach (var imagePart in imageParts) {
  // Load the Image into a Unity Texture2D object
  UnityEngine.Texture2D texture2D = new(2, 2);
  if (texture2D.LoadImage(imagePart.Data.ToArray())) {
    // Do something with the image
  }
}

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
let model = FirebaseAI.firebaseAI(backend: .googleAI()).generativeModel(
  modelName: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: GenerationConfig(responseModalities: [.image])
)

// Provide an image for the model to edit
guard let image = UIImage(named: "scones") else { fatalError("Image file not found.") }

// Provide a text prompt instructing the model to edit the image
let prompt = "Edit this image to make it look like a cartoon"

// To edit the image, call `generateContent` with the image and text input
let response = try await model.generateContent(image, prompt)

// Handle the generated image
guard let inlineDataPart = response.inlineDataParts.first else {
  fatalError("No image data in response.")
}
guard let uiImage = UIImage(data: inlineDataPart.data) else {
  fatalError("Failed to convert data to UIImage.")
}

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "gemini-nano-banana-2.1",
    // Configure the model to respond with images only.
    generationConfig = generationConfig {
responseModalities = listOf(ResponseModality.IMAGE) }
)

// Provide an image for the model to edit
val bitmap = BitmapFactory.decodeResource(context.resources, R.drawable.scones)

// Provide a text prompt instructing the model to edit the image
val prompt = content {
    image(bitmap)
    text("Edit this image to make it look like a cartoon")
}

// To edit the image, call `generateContent` with the prompt (image and text input)
val generatedImageAsBitmap = model.generateContent(prompt)
    // Handle the generated text and image
    .candidates.first().content.parts.filterIsInstance<ImagePart>().firstOrNull()?.image

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel(
    "gemini-nano-banana-2.1",
    // Configure the model to respond with images only.
    new GenerationConfig.Builder()
        .setResponseModalities(Arrays.asList(ResponseModality.IMAGE))
        .build()
);

GenerativeModelFutures model = GenerativeModelFutures.from(ai);

// Provide an image for the model to edit
Bitmap bitmap = BitmapFactory.decodeResource(resources, R.drawable.scones);

// Provide a text prompt instructing the model to edit the image
Content promptcontent = new Content.Builder()
        .addImage(bitmap)
        .addText("Edit this image to make it look like a cartoon")
        .build();

// To edit the image, call `generateContent` with the prompt (image and text input)
ListenableFuture<GenerateContentResponse> response = model.generateContent(promptcontent);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        // iterate over all the parts in the first candidate in the result object
        for (Part part : result.getCandidates().get(0).getContent().getParts()) {
            if (part instanceof ImagePart) {
                ImagePart imagePart = (ImagePart) part;
                Bitmap generatedImageAsBitmap = imagePart.getImage();
                break;
            }
        }
    }

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

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend, ResponseModality } from "firebase/ai";

// 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 Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(ai, {
  model: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: {
    responseModalities: [ResponseModality.IMAGE],
  },
});

// Prepare an image for the model to edit
async function fileToGenerativePart(file) {
  const base64EncodedDataPromise = new Promise((resolve) => {
    const reader = new FileReader();
    reader.onloadend = () => resolve(reader.result.split(',')[1]);
    reader.readAsDataURL(file);
  });
  return {
    type: "inlineData",
    inlineData: { data: await base64EncodedDataPromise, mimeType: file.type },
  };
}

// Provide a text prompt instructing the model to edit the image
const prompt = "Edit this image to make it look like a cartoon";

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

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

// Handle the generated image
try {
  const inlineDataParts = result.response.inlineDataParts();
  if (inlineDataParts?.[0]) {
    const image = inlineDataParts[0].inlineData;
    console.log(image.mimeType, image.data);
  }
} catch (err) {
  console.error('Prompt or candidate was blocked:', err);
}

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

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

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'gemini-nano-banana-2.1',
  // Configure the model to respond with images only.
  generationConfig: GenerationConfig(responseModalities: [ResponseModalities.image]),
);

// Prepare an image for the model to edit
final image = await File('scones.jpg').readAsBytes();
final imagePart = InlineDataPart('image/jpeg', image);

// Provide a text prompt instructing the model to edit the image
final prompt = TextPart("Edit this image to make it look like a cartoon");

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

// Handle the generated image
if (response.inlineDataParts.isNotEmpty) {
  final imageBytes = response.inlineDataParts[0].bytes;
  // Process the image
} else {
  // Handle the case where no images were generated
  print('Error: No images were generated.');
}

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
var model = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetGenerativeModel(
  modelName: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: new GenerationConfig(
    responseModalities: new[] { ResponseModality.Image })
);

// Prepare an image for the model to edit
var imageFile = System.IO.File.ReadAllBytes(System.IO.Path.Combine(
  UnityEngine.Application.streamingAssetsPath, "scones.jpg"));
var image = ModelContent.InlineData("image/jpeg", imageFile);

// Provide a text prompt instructing the model to edit the image
var prompt = ModelContent.Text("Edit this image to make it look like a cartoon.");

// To edit the image, call `GenerateContent` with the image and text input
var response = await model.GenerateContentAsync(new [] { prompt, image });

// Handle the generated image
var imageParts = response.Candidates.First().Content.Parts
                         .OfType<ModelContent.InlineDataPart>()
                         .Where(part => part.MimeType == "image/png");
foreach (var imagePart in imageParts) {
  // Load the Image into a Unity Texture2D object
  Texture2D texture2D = new Texture2D(2, 2);
  if (texture2D.LoadImage(imagePart.Data.ToArray())) {
    // Do something with the image
  }
}

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
let model = FirebaseAI.firebaseAI(backend: .googleAI()).generativeModel(
  modelName: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: GenerationConfig(responseModalities: [.image])
)

// Provide the video as `Data` with the appropriate MIME type.
let video = InlineDataPart(data: try Data(contentsOf: videoURL), mimeType: "video/mp4")

// Provide a text prompt instructing the model to generate an image.
let prompt = "Generate a poster image that captures the key themes of this video."

// To generate an image, call `generateContent` with the video and text input.
let response = try await model.generateContent(video, prompt)

// Handle the generated image.
guard let inlineDataPart = response.inlineDataParts.first else {
  fatalError("No image data in response.")
}
guard let uiImage = UIImage(data: inlineDataPart.data) else {
  fatalError("Failed to convert data to UIImage.")
}

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "gemini-nano-banana-2.1",
    // Configure the model to respond with images only.
    generationConfig = generationConfig {
responseModalities = listOf(ResponseModality.IMAGE) }
)

val contentResolver = applicationContext.contentResolver
contentResolver.openInputStream(videoUri).use { stream ->
  stream?.let {
    val bytes = stream.readBytes()

    // Provide a prompt that includes the video specified above and text.
    val prompt = content {
        inlineData(bytes, "video/mp4")
        text("Generate a poster image that captures the key themes of this video.")
    }

    // Call generateContent with the prompt (video and text input).
    val response = model.generateContent(prompt)

    // Handle the generated image.
    val generatedImageAsBitmap = response.candidates.first().content.parts
        .filterIsInstance<ImagePart>().firstOrNull()?.image
  }
}

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel(
    "gemini-nano-banana-2.1",
    // Configure the model to respond with images only.
    new GenerationConfig.Builder()
        .setResponseModalities(Arrays.asList(ResponseModality.IMAGE))
        .build()
);

GenerativeModelFutures model = GenerativeModelFutures.from(ai);

ContentResolver resolver = getApplicationContext().getContentResolver();
ByteArrayOutputStream byteArrayOutputStream = new ByteArrayOutputStream();
try (java.io.InputStream inputStream = resolver.openInputStream(videoUri)) {
    if (inputStream != null) {
        int bytesRead;
        byte[] buffer = new byte[16384];
        while ((bytesRead = inputStream.read(buffer)) != -1) {
            byteArrayOutputStream.write(buffer, 0, bytesRead);
        }
    }
} catch (IOException e) {
    e.printStackTrace();
}

// Provide a prompt that includes the video specified above and text.
Content promptContent = new Content.Builder()
        .addInlineData(byteArrayOutputStream.toByteArray(), "video/mp4")
        .addText("Generate a poster image that captures the key themes of this video.")
        .build();

// Call generateContent with the prompt (video and text input).
ListenableFuture<GenerateContentResponse> response = model.generateContent(promptContent);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        // We didn't get any images back from the model.
        if (result.getCandidates().isEmpty()) {
            return;
        }
        // Handle the generated image.
        for (Part part : result.getCandidates().get(0).getContent().getParts()) {
            if (part instanceof ImagePart) {
                ImagePart imagePart = (ImagePart) part;
                Bitmap generatedImageAsBitmap = imagePart.getImage();
                break;
            }
        }
    }

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

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend, ResponseModality } from "firebase/ai";

// 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 Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(ai, {
  model: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: {
    responseModalities: [ResponseModality.IMAGE],
  },
});

// 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 instructing the model to generate an image.
  const prompt = "Generate a poster image that captures the key themes of this video.";

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

  // Call generateContent with the video and text input.
  const result = await model.generateContent([prompt, videoPart]);

  // Handle the generated image.
  try {
    const inlineDataParts = result.response.inlineDataParts();
    if (inlineDataParts?.[0]) {
      const image = inlineDataParts[0].inlineData;
      console.log(image.mimeType, image.data);
    }
  } catch (err) {
    console.error('Prompt or candidate was blocked:', err);
  }
}

run();

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

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

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'gemini-nano-banana-2.1',
  // Configure the model to respond with images only.
  generationConfig: GenerationConfig(responseModalities: [ResponseModalities.image]),
);

// Prepare video for input.
final video = await File('video0.mp4').readAsBytes();
final videoPart = InlineDataPart('video/mp4', video);

// Provide a text prompt instructing the model to generate an image.
final prompt = TextPart("Generate a poster image that captures the key themes of this video.");

// Call generateContent with the video and text input.
final response = await model.generateContent([
  Content.multi([prompt, videoPart])
]);

// Handle the generated image.
if (response.inlineDataParts.isNotEmpty) {
  final imageBytes = response.inlineDataParts.first.bytes;
  // Process the image.
} else {
  // Handle the case where no images were generated.
  print('Error: No images were generated.');
}

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
var model = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetGenerativeModel(
  modelName: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: new GenerationConfig(
    responseModalities: new[] { ResponseModality.Image })
);

// Prepare a video for input.
var videoFile = System.IO.File.ReadAllBytes(System.IO.Path.Combine(
  UnityEngine.Application.streamingAssetsPath, "yourVideo.mp4"));
var video = ModelContent.InlineData("video/mp4", videoFile);

// Provide a text prompt instructing the model to generate an image.
var prompt = ModelContent.Text("Generate a poster image that captures the key themes of this video.");

// Call GenerateContentAsync with the video and text input.
var response = await model.GenerateContentAsync(new [] { prompt, video });

// Handle the generated image.
var imageParts = response.Candidates.First().Content.Parts
                         .OfType<ModelContent.InlineDataPart>()
                         .Where(part => part.MimeType == "image/png");
foreach (var imagePart in imageParts) {
  // Load the Image into a Unity Texture2D object.
  Texture2D texture2D = new Texture2D(2, 2);
  if (texture2D.LoadImage(imagePart.Data.ToArray())) {
    // Do something with the image.
  }
}

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
let model = FirebaseAI.firebaseAI(backend: .googleAI()).generativeModel(
  modelName: "gemini-nano-banana-2.1",
  // Configure the model to respond with text and images.
  generationConfig: GenerationConfig(responseModalities: [.text, .image])
)

// Provide a text prompt instructing the model to generate interleaved text and images
let prompt = """
Generate an illustrated recipe for a paella.
Create images to go alongside the text as you generate the recipe
"""

// To generate interleaved text and images, call `generateContent` with the text input
let response = try await model.generateContent(prompt)

// Handle the generated text and image
guard let candidate = response.candidates.first else {
  fatalError("No candidates in response.")
}
for part in candidate.content.parts {
  switch part {
  case let textPart as TextPart:
    // Do something with the generated text
    let text = textPart.text
  case let inlineDataPart as InlineDataPart:
    // Do something with the generated image
    guard let uiImage = UIImage(data: inlineDataPart.data) else {
      fatalError("Failed to convert data to UIImage.")
    }
  default:
    fatalError("Unsupported part type: \(part)")
  }
}

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "gemini-nano-banana-2.1",
    // Configure the model to respond with text and images.
    generationConfig = generationConfig {
responseModalities = listOf(ResponseModality.TEXT, ResponseModality.IMAGE) }
)

// Provide a text prompt instructing the model to generate interleaved text and images
val prompt = """
    Generate an illustrated recipe for a paella.
    Create images to go alongside the text as you generate the recipe
    """.trimIndent()

// To generate interleaved text and images, call `generateContent` with the text input
val responseContent = model.generateContent(prompt).candidates.first().content

// The response will contain image and text parts interleaved
for (part in responseContent.parts) {
    when (part) {
        is ImagePart -> {
            // ImagePart as a bitmap
            val generatedImageAsBitmap: Bitmap? = part.asImageOrNull()
        }
        is TextPart -> {
            // Text content from the TextPart
            val text = part.text
        }
    }
}

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel(
    "gemini-nano-banana-2.1",
    // Configure the model to respond with text and images.
    new GenerationConfig.Builder()
        .setResponseModalities(Arrays.asList(ResponseModality.TEXT, ResponseModality.IMAGE))
        .build()
);

GenerativeModelFutures model = GenerativeModelFutures.from(ai);

// Provide a text prompt instructing the model to generate interleaved text and images
Content prompt = new Content.Builder()
        .addText("Generate an illustrated recipe for a paella.\n" +
                 "Create images to go alongside the text as you generate the recipe")
        .build();

// To generate interleaved text and images, call `generateContent` with the text input
ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        Content responseContent = result.getCandidates().get(0).getContent();
        // The response will contain image and text parts interleaved
        for (Part part : responseContent.getParts()) {
            if (part instanceof ImagePart) {
                // ImagePart as a bitmap
                Bitmap generatedImageAsBitmap = ((ImagePart) part).getImage();
            } else if (part instanceof TextPart){
                // Text content from the TextPart
                String text = ((TextPart) part).getText();
            }
        }
    }

    @Override
    public void onFailure(Throwable t) {
        System.err.println(t);
    }
}, executor);

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend, ResponseModality } from "firebase/ai";

// 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 Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(ai, {
  model: "gemini-nano-banana-2.1",
  // Configure the model to respond with text and images.
  generationConfig: {
    responseModalities: [ResponseModality.TEXT, ResponseModality.IMAGE],
  },
});

// Provide a text prompt instructing the model to generate interleaved text and images
const prompt = 'Generate an illustrated recipe for a paella.\n.' +
  'Create images to go alongside the text as you generate the recipe';

// To generate interleaved text and images, call `generateContent` with the text input
const result = await model.generateContent(prompt);

// Handle the generated text and image
try {
  const response = result.response;
  if (response.candidates?.[0].content?.parts) {
    for (const part of response.candidates?.[0].content?.parts) {
      if (part.type === "text") {
        // Do something with the text
        console.log(part.text);
      }
      if (part.type === "inlineData") {
        // Do something with the image
        const image = part.inlineData;
        console.log(image.mimeType, image.data);
      }
    }
  }

} catch (err) {
  console.error('Prompt or candidate was blocked:', err);
}

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

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

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'gemini-nano-banana-2.1',
  // Configure the model to respond with text and images.
  generationConfig: GenerationConfig(responseModalities: [ResponseModalities.text, ResponseModalities.image]),
);

// Provide a text prompt instructing the model to generate interleaved text and images
final prompt = [Content.text(
  'Generate an illustrated recipe for a paella\n ' +
  'Create images to go alongside the text as you generate the recipe'
)];

// To generate interleaved text and images, call `generateContent` with the text input
final response = await model.generateContent(prompt);

// Handle the generated text and image
final parts = response.candidates.firstOrNull?.content.parts;
if (parts != null && parts.isNotEmpty) {
  for (final part in parts) {
    if (part is TextPart) {
      // Do something with text part
      final text = part.text;
    }
    if (part is InlineDataPart) {
      // Process image
      final imageBytes = part.bytes;
    }
  }
} else {
  // Handle the case where no images were generated
  print('Error: No images were generated.');
}

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
var model = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetGenerativeModel(
  modelName: "gemini-nano-banana-2.1",
  // Configure the model to respond with text and images.
  generationConfig: new GenerationConfig(
    responseModalities: new[] { ResponseModality.Text, ResponseModality.Image })
);

// Provide a text prompt instructing the model to generate interleaved text and images
var prompt = "Generate an illustrated recipe for a paella \n" +
  "Create images to go alongside the text as you generate the recipe";

// To generate interleaved text and images, call `GenerateContentAsync` with the text input
var response = await model.GenerateContentAsync(prompt);

// Handle the generated text and image
foreach (var part in response.Candidates.First().Content.Parts) {
  if (part is ModelContent.TextPart textPart) {
    if (!string.IsNullOrWhiteSpace(textPart.Text)) {
      // Do something with the text
    }
  } else if (part is ModelContent.InlineDataPart dataPart) {
    if (dataPart.MimeType == "image/png") {
      // Load the Image into a Unity Texture2D object
      UnityEngine.Texture2D texture2D = new(2, 2);
      if (texture2D.LoadImage(dataPart.Data.ToArray())) {
        // Do something with the image
      }
    }
  }
}

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
let model = FirebaseAI.firebaseAI(backend: .googleAI()).generativeModel(
  modelName: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: GenerationConfig(responseModalities: [.image])
)

// Initialize the chat
let chat = model.startChat()

guard let image = UIImage(named: "scones") else { fatalError("Image file not found.") }

// Provide an initial text prompt instructing the model to edit the image
let prompt = "Edit this image to make it look like a cartoon"

// To generate an initial response, send a user message with the image and text prompt
let response = try await chat.sendMessage(image, prompt)

// Inspect the generated image
guard let inlineDataPart = response.inlineDataParts.first else {
  fatalError("No image data in response.")
}
guard let uiImage = UIImage(data: inlineDataPart.data) else {
  fatalError("Failed to convert data to UIImage.")
}

// Follow up requests do not need to specify the image again
let followUpResponse = try await chat.sendMessage("But make it old-school line drawing style")

// Inspect the edited image after the follow up request
guard let followUpInlineDataPart = followUpResponse.inlineDataParts.first else {
  fatalError("No image data in response.")
}
guard let followUpUIImage = UIImage(data: followUpInlineDataPart.data) else {
  fatalError("Failed to convert data to UIImage.")
}

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "gemini-nano-banana-2.1",
    // Configure the model to respond with images only.
    generationConfig = generationConfig {
responseModalities = listOf(ResponseModality.IMAGE) }
)

// Provide an image for the model to edit
val bitmap = BitmapFactory.decodeResource(context.resources, R.drawable.scones)

// Create the initial prompt instructing the model to edit the image
val prompt = content {
    image(bitmap)
    text("Edit this image to make it look like a cartoon")
}

// Initialize the chat
val chat = model.startChat()

// To generate an initial response, send a user message with the image and text prompt
var response = chat.sendMessage(prompt)
// Inspect the returned image
var generatedImageAsBitmap = response
    .candidates.first().content.parts.filterIsInstance<ImagePart>().firstOrNull()?.image

// Follow up requests do not need to specify the image again
response = chat.sendMessage("But make it old-school line drawing style")
generatedImageAsBitmap = response
    .candidates.first().content.parts.filterIsInstance<ImagePart>().firstOrNull()?.image

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel(
    "gemini-nano-banana-2.1",
    // Configure the model to respond with images only.
    new GenerationConfig.Builder()
        .setResponseModalities(Arrays.asList(ResponseModality.IMAGE))
        .build()
);

GenerativeModelFutures model = GenerativeModelFutures.from(ai);

// Provide an image for the model to edit
Bitmap bitmap = BitmapFactory.decodeResource(resources, R.drawable.scones);

// Initialize the chat
ChatFutures chat = model.startChat();

// Create the initial prompt instructing the model to edit the image
Content prompt = new Content.Builder()
        .setRole("user")
        .addImage(bitmap)
        .addText("Edit this image to make it look like a cartoon")
        .build();

// To generate an initial response, send a user message with the image and text prompt
ListenableFuture<GenerateContentResponse> response = chat.sendMessage(prompt);
// Extract the image from the initial response
ListenableFuture<@Nullable Bitmap> initialRequest = Futures.transform(response, result -> {
    for (Part part : result.getCandidates().get(0).getContent().getParts()) {
        if (part instanceof ImagePart) {
            ImagePart imagePart = (ImagePart) part;
            return imagePart.getImage();
        }
    }
    return null;
}, executor);

// Follow up requests do not need to specify the image again
ListenableFuture<GenerateContentResponse> modelResponseFuture = Futures.transformAsync(
        initialRequest,
        generatedImage -> {
            Content followUpPrompt = new Content.Builder()
                    .addText("But make it old-school line drawing style")
                    .build();
            return chat.sendMessage(followUpPrompt);
        },
        executor);

// Add a final callback to check the reworked image
Futures.addCallback(modelResponseFuture, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        for (Part part : result.getCandidates().get(0).getContent().getParts()) {
            if (part instanceof ImagePart) {
                ImagePart imagePart = (ImagePart) part;
                Bitmap generatedImageAsBitmap = imagePart.getImage();
                break;
            }
        }
    }

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

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend, ResponseModality } from "firebase/ai";

// 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 Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(ai, {
  model: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: {
    responseModalities: [ResponseModality.IMAGE],
  },
});

// Prepare an image for the model to edit
async function fileToGenerativePart(file) {
  const base64EncodedDataPromise = new Promise((resolve) => {
    const reader = new FileReader();
    reader.onloadend = () => resolve(reader.result.split(',')[1]);
    reader.readAsDataURL(file);
  });
  return {
    type: "inlineData",
    inlineData: { data: await base64EncodedDataPromise, mimeType: file.type },
  };
}

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

// Provide an initial text prompt instructing the model to edit the image
const prompt = "Edit this image to make it look like a cartoon";

// Initialize the chat
const chat = model.startChat();

// To generate an initial response, send a user message with the image and text prompt
const result = await chat.sendMessage([prompt, imagePart]);

// Request and inspect the generated image
try {
  const inlineDataParts = result.response.inlineDataParts();
  if (inlineDataParts?.[0]) {
    // Inspect the generated image
    const image = inlineDataParts[0].inlineData;
    console.log(image.mimeType, image.data);
  }
} catch (err) {
  console.error('Prompt or candidate was blocked:', err);
}

// Follow up requests do not need to specify the image again
const followUpResult = await chat.sendMessage("But make it old-school line drawing style");

// Request and inspect the returned image
try {
  const followUpInlineDataParts = followUpResult.response.inlineDataParts();
  if (followUpInlineDataParts?.[0]) {
    // Inspect the generated image
    const followUpImage = followUpInlineDataParts[0].inlineData;
    console.log(followUpImage.mimeType, followUpImage.data);
  }
} catch (err) {
  console.error('Prompt or candidate was blocked:', err);
}

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

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

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'gemini-nano-banana-2.1',
  // Configure the model to respond with images only.
  generationConfig: GenerationConfig(responseModalities: [ResponseModalities.image]),
);

// Prepare an image for the model to edit
final image = await File('scones.jpg').readAsBytes();
final imagePart = InlineDataPart('image/jpeg', image);

// Provide an initial text prompt instructing the model to edit the image
final prompt = TextPart("Edit this image to make it look like a cartoon");

// Initialize the chat
final chat = model.startChat();

// To generate an initial response, send a user message with the image and text prompt
final response = await chat.sendMessage([
  Content.multi([prompt,imagePart])
]);

// Inspect the returned image
if (response.inlineDataParts.isNotEmpty) {
  final imageBytes = response.inlineDataParts[0].bytes;
  // Process the image
} else {
  // Handle the case where no images were generated
  print('Error: No images were generated.');
}

// Follow up requests do not need to specify the image again
final followUpResponse = await chat.sendMessage([
  Content.text("But make it old-school line drawing style")
]);

// Inspect the returned image
if (followUpResponse.inlineDataParts.isNotEmpty) {
  final followUpImageBytes = response.inlineDataParts[0].bytes;
  // Process the image
} else {
  // Handle the case where no images were generated
  print('Error: No images were generated.');
}

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
var model = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetGenerativeModel(
  modelName: "gemini-nano-banana-2.1",
  // Configure the model to respond with images only.
  generationConfig: new GenerationConfig(
    responseModalities: new[] { ResponseModality.Image })
);

// Prepare an image for the model to edit
var imageFile = System.IO.File.ReadAllBytes(System.IO.Path.Combine(
  UnityEngine.Application.streamingAssetsPath, "scones.jpg"));
var image = ModelContent.InlineData("image/jpeg", imageFile);

// Provide an initial text prompt instructing the model to edit the image
var prompt = ModelContent.Text("Edit this image to make it look like a cartoon.");

// Initialize the chat
var chat = model.StartChat();

// To generate an initial response, send a user message with the image and text prompt
var response = await chat.SendMessageAsync(new [] { prompt, image });

// Inspect the returned image
var imageParts = response.Candidates.First().Content.Parts
                         .OfType<ModelContent.InlineDataPart>()
                         .Where(part => part.MimeType == "image/png");
// Load the image into a Unity Texture2D object
UnityEngine.Texture2D texture2D = new(2, 2);
if (texture2D.LoadImage(imageParts.First().Data.ToArray())) {
  // Do something with the image
}

// Follow up requests do not need to specify the image again
var followUpResponse = await chat.SendMessageAsync("But make it old-school line drawing style");

// Inspect the returned image
var followUpImageParts = followUpResponse.Candidates.First().Content.Parts
                         .OfType<ModelContent.InlineDataPart>()
                         .Where(part => part.MimeType == "image/png");
// Load the image into a Unity Texture2D object
UnityEngine.Texture2D followUpTexture2D = new(2, 2);
if (followUpTexture2D.LoadImage(followUpImageParts.First().Data.ToArray())) {
  // Do something with the image
}

// ...

// Specify aspect ratio and image size (both optional) in an image config.
let imageConfig = ImageConfig(aspectRatio: .landscape16x9, imageSize: .size2K)

let generationConfig = GenerationConfig(
  // Configure the model to respond with text (optional) and images (required).
  // Include the text modality only if you want interleaved text and images in the response.
  responseModalities: [.text, .image],
  imageConfig: imageConfig
)

// Make sure you initialize your chosen Gemini API backend service
let model = FirebaseAI.firebaseAI().generativeModel(
  modelName: "gemini-nano-banana-2.1",
  generationConfig: generationConfig
)

// ...

// ...

val config = generationConfig {
    // Configure the model to respond with text (optional) and images (required).
    // Include the text modality only if you want interleaved text and images in the response.
    responseModalities = listOf(ResponseModality.TEXT, ResponseModality.IMAGE)
    // Specify aspect ratio and image size (both optional) in an image config.
    imageConfig = imageConfig {
        aspectRatio = AspectRatio.LANDSCAPE_16x9
        imageSize = ImageSize.SIZE_2K
    }
}

// Make sure you initialize your chosen Gemini API backend service
val model = Firebase.ai.generativeModel(
    modelName = "gemini-nano-banana-2.1",
    generationConfig = config
)

// ...

// ...

GenerationConfig config = new GenerationConfig.Builder()
    // Configure the model to respond with text (optional) and images (required).
    // Include the text modality only if you want interleaved text and images in the response.
    .setResponseModalities(Arrays.asList(ResponseModality.TEXT, ResponseModality.IMAGE))
    // Specify aspect ratio and image size (both optional) in an image config.
    .setImageConfig(
        ImageConfig.builder()
            .setAspectRatio(AspectRatio.LANDSCAPE_16x9)
            .setImageSize(ImageSize.SIZE_2K)
            .build()
    )
    .build();

// Make sure you initialize your chosen Gemini API backend service
GenerativeModel model = FirebaseAI.getInstance().generativeModel(
    "gemini-nano-banana-2.1",
    config
);

// ...

import {
  getGenerativeModel,
  ResponseModality,
  ImageConfigAspectRatio,
  ImageConfigImageSize
} from "firebase/ai";

// ...

const generationConfig = {
  // Configure the model to respond with text (optional) and images (required).
  // Include the text modality only if you want interleaved text and images in the response.
  responseModalities: [ResponseModality.TEXT, ResponseModality.IMAGE],
  // Specify aspect ratio and image size (both optional) in an image config.
  imageConfig: {
    aspectRatio: ImageConfigAspectRatio.LANDSCAPE_16x9,
    imageSize: ImageConfigImageSize.SIZE_2K
  }
};

// Make sure you initialize your chosen Gemini API backend service
const model = getGenerativeModel(ai, {
  model: "gemini-nano-banana-2.1",
  generationConfig
});

// ...

// ...

final generationConfig = GenerationConfig(
  // Configure the model to respond with text (optional) and images (required).
  // Include the text modality only if you want interleaved text and images in the response.
  responseModalities: [ResponseModalities.text, ResponseModalities.image],
  // Specify aspect ratio and image size (both optional) in an image config.
  imageConfig: ImageConfig(
    aspectRatio: ImageAspectRatio.landscape16x9,
    imageSize: ImageSize.size2K,
  ),
);

// Make sure you initialize your chosen Gemini API backend service
final model = FirebaseAI.instance.generativeModel(
  model: 'gemini-nano-banana-2.1,
  generationConfig: generationConfig,
);

// ...

// ...

var generationConfig = new GenerationConfig(
  // Configure the model to respond with text (optional) and images (required).
  // Include the text modality only if you want interleaved text and images in the response.
  responseModalities: new[] { ResponseModality.Text, ResponseModality.Image },
  // Specify aspect ratio and image size (both optional) in an image config.
  imageConfig: new ImageConfig(
    aspectRatio: ImageConfig.AspectRatio.Landscape16x9,
    imageSize: ImageConfig.ImageSize.Size2K)
);

// Make sure you initialize your chosen Gemini API backend service.
var model = FirebaseAI.GetInstance().GetGenerativeModel(
  modelName: "gemini-nano-banana-2.1",
  generationConfig: generationConfig
);

// ...