使用 Gemini API 分析圖片檔案

您可以要求 Gemini 模型分析您提供的圖片檔案，無論是內嵌 (以 base64 編碼) 或透過網址皆可。使用 Firebase AI Logic 時，您可以直接從應用程式提出這項要求。

這項功能可協助您執行下列操作：

  • 建立圖片說明或回答圖片相關問題
  • 根據圖片撰寫短篇故事或詩詞
  • 偵測圖像中的物件，並傳回物件的定界框座標
  • 為一組圖片加上標籤或分類，標示情緒、風格或其他特徵

本指南說明如何從輸入的圖片生成文字，但您也可以從輸入的圖片生成圖片。

跳至程式碼範例

跳至串流回應的程式碼


如要瞭解其他圖片處理選項，請參閱其他指南
生成結構化輸出內容 多輪對話 在裝置端分析圖片 生成圖片

事前準備

按一下 Gemini API 供應商，即可在這個頁面查看供應商專屬內容和程式碼。

如果尚未完成，請參閱入門指南，瞭解如何設定 Firebase 專案、將應用程式連結至 Firebase、新增 SDK、為所選Gemini API供應商初始化後端服務，以及建立 GenerativeModel 執行個體。

如要測試及反覆調整提示，建議使用 Google AI Studio。

支援這項功能的機型

本指南說明如何從圖片輸入內容生成文字，適用於下列 Gemini 模型：

  • gemini-3.1-pro-preview
  • gemini-3.8-flash (以及舊版 gemini-3.7-flash、gemini-3.6-flash 和 gemini-3.5-flash)
  • gemini-3.5-flash-lite (和舊版 gemini-3.1-flash-lite)

一般用途 Gemini 2.5 模型支援這項功能，但都已淘汰。

從圖片檔案 (以 base64 編碼) 生成文字

嘗試這個範例前，請先完成本指南的「事前準備」一節，設定專案和應用程式。
在該節中，您也會點選所選Gemini API供應商的按鈕，以便在本頁面查看供應商專屬內容。

您可以透過文字和圖片提示，要求 Gemini 模型生成文字，方法是提供每個輸入檔案的 mimeType 和檔案本身。請參閱本頁面稍後的輸入檔案規定和建議。

Swift

您可以呼叫 generateContent() ，從文字和圖片的多模態輸入內容生成文字。

單一檔案輸入


import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case.
let model = ai.generativeModel(modelName: "gemini-3.8-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.")

輸入多個檔案


import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case.
let model = ai.generativeModel(modelName: "gemini-3.8-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

您可以呼叫 generateContent() ，從文字和圖片的多模態輸入內容生成文字。

如果是 Kotlin，這個 SDK 中的方法是暫停函式，需要從 Coroutine 範圍呼叫。

單一檔案輸入


// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
                        .generativeModel("gemini-3.8-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 = model.generateContent(prompt)
print(response.text)

輸入多個檔案

如果是 Kotlin，這個 SDK 中的方法是暫停函式，需要從 Coroutine 範圍呼叫。 

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
                        .generativeModel("gemini-3.8-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 = model.generateContent(prompt)
print(response.text)

Java

您可以呼叫 generateContent() ，從文字和圖片的多模態輸入內容生成文字。

如果是 Java，這個 SDK 中的方法會傳回 ListenableFuture。

單一檔案輸入


// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
        .generativeModel("gemini-3.8-flash");

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);


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);

輸入多個檔案


// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
        .generativeModel("gemini-3.8-flash");

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);


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

您可以呼叫 generateContent() ，從文字和圖片的多模態輸入內容生成文字。

單一檔案輸入


import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } 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-3.8-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 do you see?";

  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();

輸入多個檔案


import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } 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-3.8-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

您可以呼叫 generateContent() ，從文字和圖片的多模態輸入內容生成文字。

單一檔案輸入


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

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

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model =
      FirebaseAI.googleAI().generativeModel(model: 'gemini-3.8-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);

輸入多個檔案


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

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

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model =
      FirebaseAI.googleAI().generativeModel(model: 'gemini-3.8-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);

Unity

您可以呼叫 GenerateContentAsync() ，從文字和圖片的多模態輸入內容生成文字。

單一檔案輸入


using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(modelName: "gemini-3.8-flash");


// Convert a Texture2D into InlineDataParts
var grayImage = ModelContent.InlineData("image/png",
      UnityEngine.ImageConversion.EncodeToPNG(UnityEngine.Texture2D.grayTexture));

// Provide a text prompt to include with the image
var prompt = ModelContent.Text("What's in this picture?");

// To generate text output, call GenerateContentAsync and pass in the prompt
var response = await model.GenerateContentAsync(new [] { grayImage, prompt });
UnityEngine.Debug.Log(response.Text ?? "No text in response.");

輸入多個檔案


using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(modelName: "gemini-3.8-flash");


// Convert Texture2Ds into InlineDataParts
var blackImage = ModelContent.InlineData("image/png",
      UnityEngine.ImageConversion.EncodeToPNG(UnityEngine.Texture2D.blackTexture));
var whiteImage = ModelContent.InlineData("image/png",
      UnityEngine.ImageConversion.EncodeToPNG(UnityEngine.Texture2D.whiteTexture));

// Provide a text prompt to include with the images
var prompt = ModelContent.Text("What's different between these pictures?");

// To generate text output, call GenerateContentAsync and pass in the prompt
var response = await model.GenerateContentAsync(new [] { blackImage, whiteImage, prompt });
UnityEngine.Debug.Log(response.Text ?? "No text in response.");

瞭解如何選擇適合用途和應用程式的模型， 。

逐句顯示回覆

嘗試這個範例前，請先完成本指南的「事前準備」一節，設定專案和應用程式。
在該節中，您也會點選所選Gemini API供應商的按鈕，以便在本頁面查看供應商專屬內容。

您不必等待模型生成完整結果，而是使用串流處理部分結果，即可加快互動速度。如要串流回覆，請呼叫 generateContentStream。


輸入圖片檔案的規定和建議

請注意，以內嵌資料形式提供的檔案在傳輸過程中會編碼為 base64，這會增加要求的大小。如果要求過大，就會收到 HTTP 413 錯誤。

如要進一步瞭解下列事項，請參閱「支援的輸入檔案和規定」頁面：

支援的圖片 MIME 類型

Gemini 多模態模型支援下列圖片 MIME 類型：

  • PNG - image/png
  • JPEG - image/jpeg
  • WebP - image/webp

每項要求的限制

圖片的像素數量沒有具體限制，不過，系統會縮小較大的圖片，並加上邊框，以符合 3072 x 3072 的最大解析度，同時保留原始長寬比。

每項要求可包含的檔案數量上限：3,000 個圖片檔案


你還可以做些什麼？

試試其他功能

瞭解如何控管內容生成功能

您也可以使用 Google AI Studio 測試提示和模型設定，甚至取得生成的程式碼片段。

進一步瞭解支援的機型

瞭解各種用途適用的模型，以及這些模型的配額和價格。


提供有關 Firebase AI Logic 的使用體驗意見回饋