Using Spring Boot to create an AI application

Creating an AI application with Spring Boot involves integrating machine learning or AI capabilities into a Spring Boot application. For the sake of this example, I’ll show you a simple Spring Boot application that uses a pre-trained machine learning model to make predictions. For simplicity, we’ll use a popular Java machine-learning library called Deeplearning4j.

Please note that this example is quite basic, and in a real-world scenario, you might need a more sophisticated AI model, possibly trained with a more extensive dataset.

Step 1: Set Up Your Spring Boot Project

You can create a new Spring Boot project using Spring Initializr (https://start.spring.io/) with the following dependencies:

Spring Web
Thymeleaf (optional, for a simple web interface)

Step 2: Add Dependencies

Add the following dependencies to your pom.xml for Deeplearning4j:

<dependency>
    <groupId>org.nd4j</groupId>
    <artifactId>nd4j-native-platform</artifactId>
    <version>1.0.0-beta2</version>
</dependency>
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-nlp</artifactId>
    <version>1.0.0-beta2</version>
</dependency>

Step 3: Create a Simple AI Service

import org.deeplearning4j.bagofwords.vectorizer.TfidfVectorizer;
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors;
import org.deeplearning4j.text.tokenization.tokenizer.TokenPreProcess;
import org.springframework.stereotype.Service;

import java.util.List;

@Service
public class AIService {

    private final WordVectors wordVectors;

    public AIService(WordVectors wordVectors) {
        this.wordVectors = wordVectors;
    }

    public double predictSentiment(String inputText) {
        // Perform tokenization, vectorization, and sentiment prediction
        // This is a simplified example; you might use a pre-trained model for sentiment analysis
        List<String> tokens = tokenize(inputText);
        double[] vector = vectorize(tokens);
        return makePrediction(vector);
    }

    private List<String> tokenize(String inputText) {
        // Tokenization logic here
        // You might use a more sophisticated tokenizer
        return List.of(inputText.split(" "));
    }

    private double[] vectorize(List<String> tokens) {
        // Vectorization logic here
        // You might use a more complex embedding model
        TfidfVectorizer vectorizer = new TfidfVectorizer();
        return vectorizer.transform(tokens);
    }

    private double makePrediction(double[] vector) {
        // Simplified sentiment prediction logic
        // You might use a pre-trained machine learning model for sentiment analysis
        // and return the predicted sentiment score
        return 0.5; // Placeholder value
    }
}

Step 4: Create a Controller

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Controller;
import org.springframework.ui.Model;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestMapping;

@Controller
@RequestMapping("/ai")
public class AIController {

    private final AIService aiService;

    @Autowired
    public AIController(AIService aiService) {
        this.aiService = aiService;
    }

    @PostMapping("/predictSentiment")
    public String predictSentiment(String inputText, Model model) {
        double sentimentScore = aiService.predictSentiment(inputText);
        model.addAttribute("sentimentScore", sentimentScore);
        return "result";
    }
}

Step 5: Create a Simple Thymeleaf Template (result.html)

<!DOCTYPE html>
<html lang="en" xmlns:th="http://www.thymeleaf.org">
<head>
    <meta charset="UTF-8">
    <title>AI Prediction Result</title>
</head>
<body>
    <h2>Sentiment Score: <span th:text="${sentimentScore}"></span></h2>
</body>
</html>

Step 6: Run Your Application

Run your Spring Boot application, and you can access it at http://localhost:8080. This example provides a simple web form where users can input text, and the application will provide a sentiment score based on the simple logic implemented in the AIService class.