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add vertex ai snippets
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‎packages/firebase_snippets_app/lib/snippets/vertex_ai.dart

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// Copyright 2024 Google LLC
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// ignore_for_file: avoid_print, unused_local_variable
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import 'dart:io';
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import 'package:firebase_core/firebase_core.dart';
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import 'package:firebase_vertexai/firebase_vertexai.dart';
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import 'package:firebase_snippets_app/snippets/snippet_base.dart';
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class VertexAISnippets extends DocSnippet {
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late final GenerativeModel model;
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@override
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void runAll() {
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initializeModel();
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configureModel();
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safetySetting();
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multiSafetySetting();
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textGenTextOnlyPromptStream();
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textGenTextOnlyPrompt();
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textGenMultimodalOneImagePromptStream();
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textGenMultimodalOneImagePrompt();
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textGenMultiModalMultiImagePromptStreaming();
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textGenMultiModalMultiImagePrompt();
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textGenMultiModalVideoPromptStreaming();
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textGenMultiModalVideoPrompt();
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countTokensText();
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countTokensTextImage();
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chatStream();
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chat();
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setSystemInstructions();
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}
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void initializeModel() async {
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// [START initialize_model]
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// Initialize FirebaseApp
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await Firebase.initializeApp();
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// Initialize the {{vertexai}} service and the generative model
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// Specify a model that supports your use case
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// Gemini 1.5 models are versatile and can be used with all API capabilities
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final model =
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FirebaseVertexAI.instance.generativeModel(model: '{{generic_model_name_initialization}}');
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// [END initialize_model]
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}
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void configureModel() {
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// [START configure_model]
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// ...
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final generationConfig = GenerationConfig(
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maxOutputTokens: 200,
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stopSequences: ["red"],
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temperature: 0.9,
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topP: 0.1,
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topK: 16,
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);
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final model = FirebaseVertexAI.instance.generativeModel(
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model: '{{generic_model_name_initialization}}',
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generationConfig: generationConfig,
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);
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// ...
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// [END configure_model]
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}
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void safetySetting() {
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// [START safety_setting]
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// ...
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final safetySettings = [
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SafetySetting(HarmCategory.harassment, HarmBlockThreshold.high)
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];
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final model = FirebaseVertexAI.instance.generativeModel(
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model: '{{generic_model_name_initialization}}',
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safetySettings: safetySettings,
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);
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// ...
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// [END safety_setting]
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}
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void multiSafetySetting() {
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// [START multi_safety_setting]
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// ...
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final safetySettings = [
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SafetySetting(HarmCategory.harassment, HarmBlockThreshold.high),
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SafetySetting(HarmCategory.hateSpeech, HarmBlockThreshold.high),
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];
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final model = FirebaseVertexAI.instance.generativeModel(
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model: '{{generic_model_name_initialization}}',
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safetySettings: safetySettings,
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);
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// ...
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// [END multi_safety_setting]
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}
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void textGenTextOnlyPromptStream() async {
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// [START text_gen_text_only_prompt_streaming]
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// Provide a prompt that contains text
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final prompt = [Content.text('Write a story about a magic backpack.')];
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// To stream generated text output, call generateContentStream with the text input
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final response = model.generateContentStream(prompt);
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await for (final chunk in response) {
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print(chunk.text);
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}
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// [END text_gen_text_only_prompt_streaming]
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}
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void textGenTextOnlyPrompt() async {
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// [START text_gen_text_only_prompt]
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// Provide a prompt that contains text
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final prompt = [Content.text('Write a story about a magic backpack.')];
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// To generate text output, call generateContent with the text input
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final response = await model.generateContent(prompt);
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print(response.text);
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// [END text_gen_text_only_prompt]
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}
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void textGenMultimodalOneImagePromptStream() async {
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// [START text_gen_multimodal_one_image_prompt_streaming]
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// Provide a text prompt to include with the image
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final prompt = TextPart("What's in the picture?");
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// Prepare images for input
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final image = await File('image0.jpg').readAsBytes();
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final imagePart = DataPart('image/jpeg', image);
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// To stream generated text output, call generateContentStream with the text and image
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final response = await model.generateContentStream([
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Content.multi([prompt,imagePart])
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]);
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await for (final chunk in response) {
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print(chunk.text);
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}
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// [END text_gen_multimodal_one_image_prompt_streaming]
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}
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void textGenMultimodalOneImagePrompt() async {
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// [START text_gen_multimodal_one_image_prompt]
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// Provide a text prompt to include with the image
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final prompt = TextPart("What's in the picture?");
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// Prepare images for input
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final image = await File('image0.jpg').readAsBytes();
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final imagePart = DataPart('image/jpeg', image);
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// To generate text output, call generateContent with the text and image
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final response = await model.generateContent([
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Content.multi([prompt,imagePart])
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]);
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print(response.text);
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// [END text_gen_multimodal_one_image_prompt]
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}
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void textGenMultiModalMultiImagePromptStreaming() async {
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// [START text_gen_multimodal_multi_image_prompt_streaming]
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final (firstImage, secondImage) = await (
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File('image0.jpg').readAsBytes(),
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File('image1.jpg').readAsBytes()
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).wait;
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// Provide a text prompt to include with the images
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final prompt = TextPart("What's different between these pictures?");
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// Prepare images for input
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final imageParts = [
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DataPart('image/jpeg', firstImage),
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DataPart('image/jpeg', secondImage),
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];
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// To stream generated text output, call generateContentStream with the text and images
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final response = model.generateContentStream([
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Content.multi([prompt, ...imageParts])
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]);
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await for (final chunk in response) {
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print(chunk.text);
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}
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// [END text_gen_multimodal_multi_image_prompt_streaming]
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}
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void textGenMultiModalMultiImagePrompt() async {
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// [START text_gen_multimodal_multi_image_prompt]
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final (firstImage, secondImage) = await (
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File('image0.jpg').readAsBytes(),
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File('image1.jpg').readAsBytes()
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).wait;
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// Provide a text prompt to include with the images
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final prompt = TextPart("What's different between these pictures?");
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// Prepare images for input
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final imageParts = [
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DataPart('image/jpeg', firstImage),
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DataPart('image/jpeg', secondImage),
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];
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// To generate text output, call generateContent with the text and images
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final response = await model.generateContent([
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Content.multi([prompt, ...imageParts])
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]);
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print(response.text);
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// [END text_gen_multimodal_multi_image_prompt]
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}
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void textGenMultiModalVideoPromptStreaming() async {
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// [START text_gen_multimodal_video_prompt_streaming]
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// Provide a text prompt to include with the video
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final prompt = TextPart("What's in the video?");
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// Prepare video for input
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final video = await File('video0.mp4').readAsBytes();
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// Provide the video as `Data` with the appropriate mimetype
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final videoPart = DataPart('video/mp4', video);
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// To stream generated text output, call generateContentStream with the text and image
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final response = model.generateContentStream([
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Content.multi([prompt, videoPart])
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]);
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await for (final chunk in response) {
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print(chunk.text);
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}
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// [END text_gen_multimodal_video_prompt_streaming]
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}
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void textGenMultiModalVideoPrompt() async {
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// [START text_gen_multimodal_video_prompt]
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// Provide a text prompt to include with the video
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final prompt = TextPart("What's in the video?");
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// Prepare video for input
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final video = await File('video0.mp4').readAsBytes();
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// Provide the video as `Data` with the appropriate mimetype
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final videoPart = DataPart('video/mp4', video);
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// To generate text output, call generateContent with the text and images
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final response = await model.generateContent([
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Content.multi([prompt, videoPart])
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]);
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print(response.text);
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// [END text_gen_multimodal_video_prompt]
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}
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void countTokensText() async {
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// [START count_tokens_text]
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// Provide a prompt that contains text
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final prompt = [Content.text('Write a story about a magic backpack.')];
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// Count tokens and billable characters before calling generateContent
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final tokenCount = await model.countTokens(prompt);
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print('Token count: ${tokenCount.totalTokens}, billable characters: ${tokenCount.totalBillableCharacters}');
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// To generate text output, call generateContent with the text input
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final response = await model.generateContent(prompt);
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print(response.text);
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// [END count_tokens_text]
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}
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void countTokensTextImage() async {
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// [START count_tokens_text_image]
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// Provide a text prompt to include with the image
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final prompt = TextPart("What's in the picture?");
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// Prepare image for input
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final image = await File('image0.jpg').readAsBytes();
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final imagePart = DataPart('image/jpeg', image);
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// Count tokens and billable characters before calling generateContent
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final tokenCount = await model.countTokens([
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Content.multi([prompt, imagePart])
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]);
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print('Token count: ${tokenCount.totalTokens}, billable characters: ${tokenCount.totalBillableCharacters}');
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// To generate text output, call generateContent with the text and image
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final response = await model.generateContent([
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Content.multi([prompt,imagePart])
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]);
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print(response.text);
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// [END count_tokens_text_image]
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}
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void chatStream() async {
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// [START chat_streaming]
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final chat = model.startChat();
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// Provide a prompt that contains text
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final prompt = Content.text('Write a story about a magic backpack.');
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final response = chat.sendMessageStream(prompt);
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await for (final chunk in response) {
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print(chunk.text);
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}
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// [END chat_streaming]
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}
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void chat() async {
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// [START chat]
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final chat = model.startChat();
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// Provide a prompt that contains text
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final prompt = Content.text('Write a story about a magic backpack.');
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final response = await chat.sendMessage(prompt);
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print(response.text);
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// [END chat]
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}
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void setSystemInstructions() async {
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// [START system_instructions_text]
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await Firebase.initializeApp();
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// Initialize the Vertex AI service and the generative model
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// Specify a model that supports system instructions, like a Gemini 1.5 model
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final model =
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FirebaseVertexAI.instance.generativeModel(
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model: 'gemini-1.5-flash-preview-0514',
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systemInstruction: Content.system('You are a cat. Your name is Neko.'),
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);
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// [END system_instructions_text]
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}
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}

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