How to install Kong Gateway using Docker

To install Kong Gateway, you can follow these steps: 

 Step 1: Choose the installation method: 

  

     Kong Gateway offers different installation methods depending on your operating system and

     requirements. 

    You can choose from Docker, package managers (e.g., Homebrew, Yum, Apt), or manual installation.

     For simplicity, let's go with the Docker installation method.

 Step 2: Install Docker: If you don't have Docker installed, visit the Docker website

              (https://www.docker.com/) and follow the instructions to install Docker for your specific

               operating system. 

 Step 3: Pull the Kong Gateway Docker image: 

 

             Open a terminal or command prompt. Run the following command to pull the Kong Gateway

              Docker image from Docker Hub:

docker pull kong/kong-gateway

Step 4: Run Kong Gateway container: Once the image is pulled, run the following command to start a

             Kong Gateway

docker run -d --name kong-gateway \
  -e "KONG_DATABASE=off" \
  -e "KONG_PROXY_ACCESS_LOG=/dev/stdout" \
  -e "KONG_ADMIN_ACCESS_LOG=/dev/stdout" \
  -e "KONG_PROXY_ERROR_LOG=/dev/stderr" \
  -e "KONG_ADMIN_ERROR_LOG=/dev/stderr" \
  -e "KONG_ADMIN_LISTEN=0.0.0.0:8001" \
  -e "KONG_PROXY_LISTEN=0.0.0.0:8000" \
  -p 8000:8000 \
  -p 8001:8001 \
  kong/kong-gateway

This command starts a Kong Gateway container named "kong-gateway" with the necessary environment variables and port mappings. 

 The -p option maps the container's ports to the host machine, allowing access to Kong Gateway's admin API (port 8001) and proxy API (port 8000). 

 The -e options set various environment variables like the database type (KONG_DATABASE=off disables the database), log configurations, and listen addresses.

 Step 5: Verify Kong Gateway installation: After running the container, wait for a few moments to allow

              Kong Gateway to initialize. 

You can check the logs of the container using the following command:

docker logs kong-gateway

Look for any error messages or indications that Kong Gateway has started successfully. 

 Step 6: Access Kong Gateway admin API: 

 Once Kong Gateway is running, you can access its admin API to configure and manage your Kong Gateway instance. 

Open a web browser and go to http://localhost:8001. You should see the Kong Gateway admin API homepage if everything is working correctly.

 Congratulations! You have successfully installed Kong Gateway using Docker. 

You can now proceed with configuring Kong Gateway and integrating it with your applications as needed

How to Implement Image classification using TensorFlow maven and Java

Here is an example of using TensorFlow with Java and Maven to perform image classification: 

 1.Create a new Maven project in your favorite IDE. 

 2. Add the TensorFlow Java dependency to your project by adding the following to your pom.xml file:

    
      <dependencies>
    <dependency>
        <groupId>org.tensorflow</groupId>
        <artifactId>tensorflow</artifactId>
        <version>2.7.0</version>
    </dependency>
</dependencies>
    

3. Create a new class, for example ImageClassifier.java, and add the following code:

import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import javax.imageio.ImageIO;
import org.tensorflow.DataType;
import org.tensorflow.Graph;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.TensorFlow;

public class ImageClassifier {
    private static byte[] loadImage(String path) throws IOException {
        BufferedImage img = ImageIO.read(new File(path));
        int height = img.getHeight();
        int width = img.getWidth();
        int channels = 3;
        byte[] data = new byte[height * width * channels];
        int pixel = 0;
        for (int i = 0; i < height; i++) {
            for (int j = 0; j < width; j++) {
                int rgb = img.getRGB(j, i);
                data[pixel++] = (byte) ((rgb >> 16) & 0xFF);
                data[pixel++] = (byte) ((rgb >> 8) & 0xFF);
                data[pixel++] = (byte) (rgb & 0xFF);
            }
        }
        return data;
    }

    public static void main(String[] args) throws Exception {
        // Load the TensorFlow library
        try (Graph g = new Graph()) {
           byte[] graphBytes = TensorFlowModelLoader.load("path/to/model.pb");
            g.importGraphDef(graphBytes);

            // Create a new session to run the graph
            try (Session s = new Session(g)) {
                // Load the image data
                String imagePath = "path/to/image.jpg";
                byte[] imageBytes = loadImage(imagePath);

                // Create a tensor from the image data
                Tensor inputTensor = Tensor.create(new long[]
                                   {1, imageBytes.length}, ByteBuffer.wrap(imageBytes));

                // Run the graph on the input tensor
                Tensor outputTensor = s.runner()
                        .feed("input", inputTensor)
                        .fetch("output")
                        .run()
                        .get(0);

                // Print the predicted label
                DataType outputDataType = outputTensor.dataType();
                long[] outputShape = outputTensor.shape();
                Object[] output = new Object[outputTensor.numElements()];
                outputTensor.copyTo(output);
                System.out.println("Prediction: " + output[0]);
            }
        }
    }
}

4. Replace the path/to/model.pb and path/to/image.jpg with the actual paths to your model and image files. 

 5. Run the ImageClassifier class, and it should print out the predicted label for the input image.