How to integrate and connect Baidu AI interface in Java projects

How to integrate and connect Baidu AI interfaces in Java projects

Abstract: Baidu AI open platform provides a rich set of AI capability interfaces, such as speech recognition and image recognition. , natural language processing, etc. This article will introduce how to integrate Baidu AI interface in Java projects, and show how to connect through code examples.

1. Preparation

1. Register a Baidu AI open platform account and create an application, and obtain the API Key and Secret Key.
2. Introducing Baidu AI SDK into Java projects can be introduced through Maven or Gradle.

2. Speech recognition interface docking
Baidu AI’s speech recognition interface supports multiple languages ​​​​and audio formats. The following is an example that demonstrates how to use Baidu AI SDK to connect to the speech recognition interface.

import com.baidu.aip.speech.AipSpeech;
import org.json.JSONObject;

public class SpeechRecognitionExample {
    // 配置百度AI的API Key和Secret Key
    private static final String APP_ID = "your_app_id";
    private static final String API_KEY = "your_api_key";
    private static final String SECRET_KEY = "your_secret_key";

    public static void main(String[] args) {
        // 创建AipSpeech客户端
        AipSpeech client = new AipSpeech(APP_ID, API_KEY, SECRET_KEY);

        // 设置语音识别参数
        HashMap<String, Object> options = new HashMap<>();
        options.put("dev_pid", 1537); // 设置语种为普通话

        // 语音识别
        String filePath = "path_to_audio_file";
        JSONObject res = client.asr(filePath, "pcm", 16000, options);

        // 打印识别结果
        System.out.println(res.toString());
    }
}

3. Image recognition interface docking
Baidu AI’s image recognition interface supports image classification, object detection, image review and other functions. The following is an example that demonstrates how to use Baidu AI SDK to connect to the image recognition interface.

import com.baidu.aip.imageclassify.AipImageClassify;
import org.json.JSONObject;
import java.util.HashMap;

public class ImageRecognitionExample {
    // 配置百度AI的API Key和Secret Key
    private static final String APP_ID = "your_app_id";
    private static final String API_KEY = "your_api_key";
    private static final String SECRET_KEY = "your_secret_key";

    public static void main(String[] args) {
        // 创建AipImageClassify客户端
        AipImageClassify client = new AipImageClassify(APP_ID, API_KEY, SECRET_KEY);

        // 设置图像识别参数
        HashMap<String, String> options = new HashMap<>();
        options.put("baike_num", "5"); // 返回百科信息的个数

        // 图像识别
        String filePath = "path_to_image_file";
        JSONObject res = client.advancedGeneral(filePath, options);

        // 打印识别结果
        System.out.println(res.toString());
    }
}

4. Natural language processing interface docking
Baidu AI’s natural language processing interface supports text classification, lexical analysis, sentiment analysis and other functions. The following is an example that demonstrates how to use Baidu AI SDK to connect to the natural language processing interface.

import com.baidu.aip.nlp.AipNlp;
import org.json.JSONObject;
import java.util.HashMap;

public class NaturalLanguageProcessingExample {
    // 配置百度AI的API Key和Secret Key
    private static final String APP_ID = "your_app_id";
    private static final String API_KEY = "your_api_key";
    private static final String SECRET_KEY = "your_secret_key";

    public static void main(String[] args) {
        // 创建AipNlp客户端
        AipNlp client = new AipNlp(APP_ID, API_KEY, SECRET_KEY);

        // 设置自然语言处理参数
        HashMap<String, Object> options = new HashMap<>();
        options.put("mode", 1); // 启用词法分析模式

        // 自然语言处理
        String text = "百度是一家全球领先的人工智能公司";
        JSONObject res = client.lexer(text, options);

        // 打印处理结果
        System.out.println(res.toString());
    }
}

5. Summary
This article introduces how to integrate and connect Baidu AI interface in Java projects, and demonstrates the interface docking process of speech recognition, image recognition and natural language processing through code examples. Readers can flexibly apply these interfaces in projects according to specific needs to achieve more interesting functions.

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