kaiwaehner

from ksql import KSQLAPI
client = KSQLAPI('http://localhost:8088')

client.create_stream(table_name='creditcardfraud_source',
                     columns_type=['Id bigint', 'Timestamp varchar', 'User varchar', 'Time int', 'V1 double', 'V2 double', 'V3 double', 'V4 double', 'V5 double', 'V6 double', 'V7 double', 'V8 double', 'V9 double', 'V10 double', 'V11 double', 'V12 double', 'V13 double', 'V14 double', 'V15 double', 'V16 double', 'V17 double', 'V18 double', 'V19 double', 'V20 double', 'V21 double', 'V22 double', 'V23 double', 'V24 double', 'V25 double', 'V26 double', 'V27 double', 'V28 double', 'Amount double', 'Class string'],
                     topic='creditcardfraud_source',
                     value_format='DELIMITED')

kaiwaehner

import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;

import com.github.megachucky.kafka.streams.machinelearning.TensorflowObjectRecogniser;

kaiwaehner

// Start Kafka Streams Application to process new incoming images from the Input Topic

final KafkaStreams streams = new KafkaStreams(builder.build(), streamsConfiguration);

streams.start();

kaiwaehner

import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.Printed;
import org.tensorflow.DataType;
import org.tensorflow.Graph;
import org.tensorflow.Output;

kaiwaehner

import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;

import com.github.megachucky.kafka.streams.machinelearning.TensorflowObjectRecogniser;

kaiwaehner

// Configure Kafka Streams Application
final String bootstrapServers = args.length > 0 ? args[0] : "localhost:9092";

final Properties streamsConfiguration = new Properties();

// Give the Streams application a unique name. The name must be unique
// in the Kafka cluster against which the application is run.
streamsConfiguration.put(StreamsConfig.APPLICATION_ID_CONFIG, "kafka-streams-tensorflow-serving-gRPC-example");

// Where to find Kafka broker(s).

kaiwaehner

KStream<String, Object> transformedMessage = imageInputLines.mapValues(value -> {

			System.out.println("Image path: " + value);

			imagePath = value;

			TensorflowObjectRecogniser recogniser = new TensorflowObjectRecogniser(server, port);

			System.out.println("Image = " + imagePath);
			InputStream jpegStream;

kaiwaehner

// Start Kafka Streams Application to process new incoming images from the Input Topic

final KafkaStreams streams = new KafkaStreams(builder.build(), streamsConfiguration);

streams.start();

kaiwaehner

import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.KeyValue;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.integration.utils.EmbeddedKafkaCluster;
import org.apache.kafka.streams.integration.utils.IntegrationTestUtils;
import org.apache.kafka.streams.kstream.KStream;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;

kaiwaehner

// Step 1: Load Keras TensorFlow Model using DeepLearning4J API
String simpleMlp = new ClassPathResource("generatedModels/Keras/simple_mlp.h5").getFile().getPath();
System.out.println(simpleMlp.toString());
MultiLayerNetwork model = KerasModelImport.importKerasSequentialModelAndWeights(simpleMlp);