Generate text from a video

This sample demonstrates how to use the Gemini API to generate text from a video.

Explore further

For detailed documentation that includes this code sample, see the following:

• Send multimodal prompt requests

Code sample

C#

Before trying this sample, follow the C# setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI C# API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

using Google.Api.Gax.Grpc;
using Google.Cloud.AIPlatform.V1;
using System.Collections.Generic;
using System.Text;
using System.Threading.Tasks;

public class MultimodalVideoInput
{
    public async Task<string> GenerateContent(
        string projectId = "your-project-id",
        string location = "us-central1",
        string publisher = "google",
        string model = "gemini-1.0-pro-vision"
    )
    {
        // Create client
        var predictionServiceClient = new PredictionServiceClientBuilder
        {
            Endpoint = $"{location}-aiplatform.googleapis.com"
        }.Build();

        // Prompt
        string prompt = "What's in the video?";
        string videoUri = "gs://cloud-samples-data/video/animals.mp4";

        // Initialize request argument(s)
        var content = new Content
        {
            Role = "USER"
        };
        content.Parts.AddRange(new List<Part>()
        {
            new() {
                Text = prompt
            },
            new() {
                FileData = new() {
                    MimeType = "video/mp4",
                    FileUri = videoUri
                }
            }
        });

        var generateContentRequest = new GenerateContentRequest
        {
            Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}"
        };
        generateContentRequest.Contents.Add(content);

        // Make the request, returning a streaming response
        using PredictionServiceClient.StreamGenerateContentStream response = predictionServiceClient.StreamGenerateContent(generateContentRequest);

        StringBuilder fullText = new();

        // Read streaming responses from server until complete
        AsyncResponseStream<GenerateContentResponse> responseStream = response.GetResponseStream();
        await foreach (GenerateContentResponse responseItem in responseStream)
        {
            fullText.Append(responseItem.Candidates[0].Content.Parts[0].Text);
        }
        return fullText.ToString();
    }
}

Go

Before trying this sample, follow the Go setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Go API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import (
	"context"
	"errors"
	"fmt"
	"io"
	"mime"
	"path/filepath"

	"cloud.google.com/go/vertexai/genai"
)

// generateMultimodalContent generates a response into w, based upon the prompt
// and video provided.
// video is a Google Cloud Storage path starting with "gs://"
func generateMultimodalContent(w io.Writer, prompt, video, projectID, location, modelName string) error {
	// prompt := "What is in this video?"
	// video := "gs://cloud-samples-data/video/animals.mp4"
	// location := "us-central1"
	// modelName := "gemini-1.0-pro-vision"
	ctx := context.Background()

	client, err := genai.NewClient(ctx, projectID, location)
	if err != nil {
		return fmt.Errorf("unable to create client: %v", err)
	}
	defer client.Close()

	model := client.GenerativeModel(modelName)
	model.SetTemperature(0.4)

	// Given a video file URL, prepare video file as genai.Part
	part := genai.FileData{
		MIMEType: mime.TypeByExtension(filepath.Ext(video)),
		FileURI:  video,
	}

	res, err := model.GenerateContent(ctx, part, genai.Text(prompt))
	if err != nil {
		return fmt.Errorf("unable to generate contents: %v", err)
	}

	if len(res.Candidates) == 0 ||
		len(res.Candidates[0].Content.Parts) == 0 {
		return errors.New("empty response from model")
	}

	fmt.Fprintf(w, "generated response: %s\n", res.Candidates[0].Content.Parts[0])
	return nil
}

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.vertexai.VertexAI;
import com.google.cloud.vertexai.api.GenerateContentResponse;
import com.google.cloud.vertexai.generativeai.ContentMaker;
import com.google.cloud.vertexai.generativeai.GenerativeModel;
import com.google.cloud.vertexai.generativeai.PartMaker;
import com.google.cloud.vertexai.generativeai.ResponseHandler;
import java.io.IOException;

public class MultimodalVideoInput {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-google-cloud-project-id";
    String location = "us-central1";
    String modelName = "gemini-1.0-pro-vision";

    multimodalVideoInput(projectId, location, modelName);
  }

  // Analyzes the given video input.
  public static void multimodalVideoInput(String projectId, String location, String modelName)
      throws IOException {
    // Initialize client that will be used to send requests. This client only needs
    // to be created once, and can be reused for multiple requests.
    try (VertexAI vertexAI = new VertexAI(projectId, location)) {
      String videoUri = "gs://cloud-samples-data/video/animals.mp4";

      GenerativeModel model = new GenerativeModel(modelName, vertexAI);
      GenerateContentResponse response = model.generateContent(
          ContentMaker.fromMultiModalData(
              "What is in the video?",
              PartMaker.fromMimeTypeAndData("video/mp4", videoUri)
          ));

      String output = ResponseHandler.getText(response);
      System.out.println(output);
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

const {VertexAI} = require('@google-cloud/vertexai');

/**
 * TODO(developer): Update these variables before running the sample.
 */
async function sendMultiModalPromptWithVideo(
  projectId = 'PROJECT_ID',
  location = 'us-central1',
  model = 'gemini-1.0-pro-vision'
) {
  // Initialize Vertex with your Cloud project and location
  const vertexAI = new VertexAI({project: projectId, location: location});

  const generativeVisionModel = vertexAI.getGenerativeModel({
    model: model,
  });

  // Pass multimodal prompt
  const request = {
    contents: [
      {
        role: 'user',
        parts: [
          {
            fileData: {
              fileUri: 'gs://cloud-samples-data/video/animals.mp4',
              mimeType: 'video/mp4',
            },
          },
          {
            text: 'What is in the video?',
          },
        ],
      },
    ],
  };

  // Create the response
  const response = await generativeVisionModel.generateContent(request);
  // Wait for the response to complete
  const aggregatedResponse = await response.response;
  // Select the text from the response
  const fullTextResponse =
    aggregatedResponse.candidates[0].content.parts[0].text;

  console.log(fullTextResponse);
}

What's next

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