Spark Streaming with Kafka - scala

Question

I want to do simple machine learning in Spark.
First the application should do some learning from historical data from a file, train the machine learning model and then read input from kafka to give predictions in real time. To do that I believe I should use spark streaming. However, I'm afraid that I don't really understand how spark streaming works.
The code looks like this:
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("test App")
val sc = new SparkContext(conf)
val fromFile = parse(sc, Source.fromFile("my_data_.csv").getLines.toArray)
ML.train(fromFile)
real_time(sc)
}
Where ML is a class with some machine learning things in it and train gives it data to train. There also is a method classify which calculates predictions based on what it learned.
The first part seems to work fine, but real_time is a problem:
def real_time(sc: SparkContext) : Unit = {
val ssc = new StreamingContext(new SparkConf(), Seconds(1))
val topic = "my_topic".split(",").toSet
val params = Map[String, String](("metadata.broker.list", "localhost:9092"))
val dstream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, params, topic)
var lin = dstream.map(_._2)
val str_arr = new Array[String](0)
lin.foreach {
str_arr :+ _.collect()
}
val lines = parse(sc, str_arr).map(i => i.features)
ML.classify(lines)
ssc.start()
ssc.awaitTermination()
}
What I would like it to do is check the Kafka stream and compute it if there are any new lines. This doesn't seem to be the case, I added some prints and it is not printed.
How to use spark streaming, how should it be used in my case?