使用 Gemini 摘要本機影片檔案

這個範例示範如何使用 Gemini 摘要本機影片檔案。

程式碼範例

Go

在試用這個範例之前，請先按照「使用用戶端程式庫的 Vertex AI 快速入門導覽課程」中的 Go 設定說明操作。詳情請參閱 Vertex AI Go API 參考文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import (
	"context"
	"fmt"
	"io"
	"os"

	genai "google.golang.org/genai"
)

// generateWithLocalVideo shows how to generate text using a local video input.
func generateWithLocalVideo(w io.Writer) error {
	ctx := context.Background()

	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		HTTPOptions: genai.HTTPOptions{APIVersion: "v1"},
	})
	if err != nil {
		return fmt.Errorf("failed to create genai client: %w", err)
	}

	// Read local video file content
	data, err := os.ReadFile("describe_video_content.mp4")
	if err != nil {
		return fmt.Errorf("failed to read local video: %w", err)
	}

	modelName := "gemini-2.5-flash"
	contents := []*genai.Content{
		{
			Role: "user",
			Parts: []*genai.Part{
				{Text: `Write a short and engaging blog post based on this video.`},
				{InlineData: &genai.Blob{
					MIMEType: "video/mp4",
					Data:     data,
				}},
			},
		},
	}

	resp, err := client.Models.GenerateContent(ctx, modelName, contents, nil)
	if err != nil {
		return fmt.Errorf("failed to generate content: %w", err)
	}

	respText := resp.Text()
	fmt.Fprintln(w, respText)

	// Example response:
	// Finding Your Flow: The Focused Ascent
	//
	// Ever watched someone scale an indoor climbing wall and been captivated by their precision and power? This video perfectly captures that intense focus and calculated movement.
	//
	// Our climber isn't just pulling himself up; he's engaging in a dynamic dance with gravity. Every reach, every foot placement, every clip of the rope is a deliberate part of solving the route's puzzle. You can almost feel the concentration as his eyes scan for the next optimal hold, his muscles working in unison to propel him upwards.
	//
	// Indoor climbing....
	// ...

	return nil
}

Java

在試用這個範例之前，請先按照「使用用戶端程式庫的 Vertex AI 快速入門導覽課程」中的 Java 設定說明操作。詳情請參閱 Vertex AI Java API 參考文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.HttpOptions;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;

public class TextGenerationWithLocalVideo {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String modelId = "gemini-2.5-flash";
    generateContent(modelId);
  }

  // Generates text with local video input
  public static String generateContent(String modelId) throws IOException {
    // Client Initialization. Once created, it can be reused for multiple requests.
    try (Client client =
        Client.builder()
            .location("global")
            .vertexAI(true)
            .httpOptions(HttpOptions.builder().apiVersion("v1").build())
            .build()) {

      // Read content from the local video.
      byte[] videoData = Files.readAllBytes(Paths.get("resources/describe_video_content.mp4"));

      GenerateContentResponse response =
          client.models.generateContent(
              modelId,
              Content.fromParts(
                  Part.fromBytes(videoData, "video/mp4"),
                  Part.fromText("Write a short and engaging blog post based on this video.")),
              null);

      System.out.print(response.text());
      // Example response:
      // More Than Just a Climb: Finding Your Flow on the Wall
      // There's something captivating about watching a climber in their element. This short clip
      // offers a perfect glimpse into the focused world of indoor climbing, where precision meets
      // power...
      return response.text();
    }
  }
}

Node.js

在試用這個範例之前，請先按照「使用用戶端程式庫的 Vertex AI 快速入門導覽課程」中的 Node.js 設定說明操作。詳情請參閱 Vertex AI Node.js API 參考文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

const {GoogleGenAI} = require('@google/genai');
const fs = require('fs');

const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT;
const GOOGLE_CLOUD_LOCATION = process.env.GOOGLE_CLOUD_LOCATION || 'global';

async function generateText(
  projectId = GOOGLE_CLOUD_PROJECT,
  location = GOOGLE_CLOUD_LOCATION
) {
  const client = new GoogleGenAI({
    vertexai: true,
    project: projectId,
    location: location,
  });

  const videoContent = fs.readFileSync('test-data/describe_video_content.mp4');

  const response = await client.models.generateContent({
    model: 'gemini-2.5-flash-lite',
    contents: [
      {
        inlineData: {
          data: videoContent.toString('base64'),
          mimeType: 'video/mp4',
        },
      },
      {text: 'Write a short and engaging blog post based on this video.'},
    ],
    config: {
      mediaResolution: 'MEDIA_RESOLUTION_LOW',
    },
  });

  console.log(response.text);

  // Example response:
  // Okay, here's a short and engaging blog post based on the climbing video:
  // **Title: Conquering the Wall: A Glimpse into the World of Indoor Climbing**
  // ...

  return response.text;
}

Python

在試用這個範例之前，請先按照「使用用戶端程式庫的 Vertex AI 快速入門導覽課程」中的 Python 設定說明操作。詳情請參閱 Vertex AI Python API 參考文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

from google import genai
from google.genai.types import HttpOptions, Part

client = genai.Client(http_options=HttpOptions(api_version="v1"))
model_id = "gemini-2.5-flash"

# Read local video file content
with open("test_data/describe_video_content.mp4", "rb") as fp:
    # Video source: https://storage.googleapis.com/cloud-samples-data/generative-ai/video/describe_video_content.mp4
    video_content = fp.read()

response = client.models.generate_content(
    model=model_id,
    contents=[
        Part.from_text(text="hello-world"),
        Part.from_bytes(data=video_content, mime_type="video/mp4"),
        "Write a short and engaging blog post based on this video.",
    ],
)

print(response.text)
# Example response:
# Okay, here's a short and engaging blog post based on the climbing video:
# **Title: Conquering the Wall: A Glimpse into the World of Indoor Climbing**
# ...

後續步驟

如要搜尋及篩選其他 Google Cloud 產品的程式碼範例，請參閱Google Cloud 瀏覽器範例。