java框架和人工智能集成的最佳实践？

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.web.bind.annotation.*;
import org.tensorflow.keras.models.load_model;
import org.tensorflow.keras.preprocessing.image.load_img;
import org.tensorflow.keras.preprocessing.image.img_to_array;

import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;

@SpringBootApplication
public class ImageClassifierApplication {

    public static void main(String[] args) {
        SpringApplication.run(ImageClassifierApplication.class, args);
    }

    // POST endpoint to handle image classification
    @PostMapping("/classify")
    public String classify(@RequestParam("image") MultipartFile image) {
        // Load the TensorFlow model
        Path modelPath = Paths.get("model.h5");
        CNN model = load_model(modelPath.toString());

        // Convert image to numpy array
        byte[] bytes = image.getBytes();
        PIL.Image img = PIL.Image.open(BytesIO(bytes))
        img_array = img_to_array(img) / 255.0
        img_array = np.expand_dims(img_array, axis=0)

        // Predict the image class
        predictions = model.predict(img_array)
        predicted_class = np.argmax(predictions[0])

        // Return the predicted class
        return predicted_class;
    }
}