VGG16SparkJavaWebApp.java

package org.deeplearning4j.VGGwebDemo;

import org.nd4j.linalg.factory.Nd4j;
import org.deeplearning4j.nn.modelimport.keras.trainedmodels.Utils.ImageNetLabels;
import org.datavec.image.loader.NativeImageLoader;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.trainedmodels.TrainedModels;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.VGG16ImagePreProcessor;
import javax.servlet.MultipartConfigElement;
import java.io.File;
import java.io.InputStream;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.StandardCopyOption;
import static spark.Spark.options;
import static spark.Spark.get;
import static spark.Spark.post;
import static spark.Spark.staticFiles;
import java.util.Collections;
import java.util.Map;
import java.util.TreeMap;
import org.nd4j.shade.jackson.databind.ObjectMapper;

/**
 * Created by tomhanlon on 1/25/17.
 */
public class VGG16SparkJavaWebApp {
    public static void main(String[] args) throws Exception {

	File locationToSave = new File("vgg16.zip");
	ComputationGraph vgg16 = ModelSerializer.restoreComputationGraph(locationToSave);


	// make upload directory
	File uploadDir = new File("upload");
	uploadDir.mkdir(); // create the upload directory if it doesn't exist

	// form
	        String form = "<form method='post' action='getPredictions' enctype='multipart/form-data'>\n" +
		                    "    <input type='file' name='uploaded_file'>\n" +
		                    "    <button>Upload picture</button>\n" +
		    "</form>";

		staticFiles.location("/Users/tomhanlon/SkyMind/webcontent");// Static files
		//CorsFilter.apply();
		//options("/", (req, res) -> {
		//Appease something
		//   });
		options("/*", (req, res) -> "Hello World");
		get("/hello", (req, res) -> "Hello World");
		get("VGGpredict", (req, res) -> form);
		//post("getPredictions",(req, res) -> "GET RESULTS");

		post("/getPredictions", (req, res) -> {

			Path tempFile = Files.createTempFile(uploadDir.toPath(), "", "");

			req.attribute("org.eclipse.jetty.multipartConfig", new MultipartConfigElement("/temp"));

			try (InputStream input = req.raw().getPart("uploaded_file").getInputStream()) { // getPart needs to use same "name" as input field in form
			    Files.copy(input, tempFile, StandardCopyOption.REPLACE_EXISTING);
			}

			//logInfo(req, tempFile);
			//return "<h1>You uploaded this image:<h1><img src='" + tempFile.getFileName() + "'>";

			long pipelineTime = System.currentTimeMillis();

			File file = tempFile.toFile();
			//File file = new File(path);
			NativeImageLoader loader = new NativeImageLoader(224, 224, 3);
			INDArray image = loader.asMatrix(file);
			file.delete();
			DataNormalization scaler = new VGG16ImagePreProcessor();

			scaler.transform(image);
			//System.out.print(image);

			long ffTime = System.currentTimeMillis();
			INDArray[] output = vgg16.output(false,image);
			//ffTime = ffTime - System.currentTimeMillis();
			ffTime = System.currentTimeMillis() - ffTime;

			// sort to get top 5
			INDArray[] sorted = Nd4j.sortWithIndices(output[0], 1, false);
			// sorted map for results
			//Map<Float, String> map = new TreeMap<Float, String>(Collections.reverseOrder());
			//VGGResults vggResults = new VGGResults(label,pred);
			VGGResults[] vggResultsArray  = new VGGResults[5];

			// finish benchmark
			//pipelineTime = pipelineTime - System.currentTimeMillis();
			pipelineTime = System.currentTimeMillis() - pipelineTime;


			// Get top 5
			for (int i = 0; i < 5; i++) {

			    // Get prediction percent
			    Float prediction = sorted[1].getFloat(i) * 100;

			    // extract label for prediction
			    String Label = ImageNetLabels.getLabel(sorted[0].getInt(i));

			    // put both in Result array
			    vggResultsArray[i] = new VGGResults(Label, prediction);

			}

			// Jackson obect mapper
			// ##### I AM HERE #####
			ObjectMapper mapper = new ObjectMapper();
			String predictions = mapper.writeValueAsString(vggResultsArray);
			//String predictions = mapper.writeValueAsString(map);
			      String predictionmunge = "{" +
				            "\"data\":" + predictions +
				            ", \"performance\":{ \"feedforward\":" + ffTime + ",\"total\":" + pipelineTime + "}" +
				  ", \"network\":{ \"parameters\":" + vgg16.numParams() + ",\"layers\":" + vgg16.getNumLayers() + "}}";
			      // return "<h4> '" + predictions  + "' </h4>" +
			      //        "Would you like to try another" +
			      //        form;
			      return predictionmunge ;
			      //return "<h1>Your image is: '" + tempFile.getName(1).toString() + "' </h1>";

			      /*
			// Jackson obect mapper
			// ##### I AM HERE #####
			ObjectMapper mapper = new ObjectMapper();
			String predictions = mapper.writeValueAsString(vggResultsArray);
			//String predictions = mapper.writeValueAsString(map);
			            String predictionmunge = "{" +
					                    "\"data\":" + predictions +
					", \"performance\":{ \"feedforward\":" + ffTime + ",\"total\":" + pipelineTime + "}}";
				    // return "<h4> '" + predictions  + "' </h4>" +
				    //        "Would you like to try another" +
				    //        form;
				    return predictionmunge ;
				    //return "<h1>Your image is: '" + tempFile.getName(1).toString() + "' </h1>";
				    */
		    });


    }

}

Notes:
Running on 7.3 snapshot, built last night, modifying the build of libnd4j at the moment on advice of Justin