add initial dockerfile

app/Dockerfile

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+FROM trestletech/plumber
+MAINTAINER Carlos Catania (AKA Harpo) "[email protected]"
+RUN apt install -y python-pip virtualenv
+RUN R -e "install.packages('keras')"
+RUN R -e "library(keras);install_keras()"
+COPY . /app
+WORKDIR /app
+CMD ["/app/launchservice.R"]
+

app/dga-classifier-service.R

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+library(keras)
+library(plumber)
+
+# loading the DGA model for classifier 
+#model<-load_model_hdf5("/home/harpo/Dropbox/ongoing-work/git-repos/dga-wb-r/docker/app/pmodel.h5")
+model<-load_model_hdf5("/app/asai-2019_model.h5")
+modelid="cacic-2018-model"
+
+valid_characters <- "$abcdefghijklmnopqrstuvwxyz0123456789-_."
+valid_characters_vector <- strsplit(valid_characters,split="")[[1]]
+tokens <- 0:length(valid_characters_vector)
+names(tokens) <- valid_characters_vector
+
+
+# testing function
+#* @get /echo
+function(msg="Hi!"){
+  list(msg = paste("The message is: ", msg))
+}
+
+# DGA prediction function
+#* @get /predict
+#* @serializer unboxedJSON
+function(domain){
+  domain_encoded <-
+	   
+	  	sapply( 
+			unlist(strsplit(tolower(domain),split="")), function(x) tokens [[x]] 
+			) 
+	  		
+		       #	{tokens[[x]] })
+  domain_encoded<-pad_sequences(t(domain_encoded),maxlen=45,padding='post', truncating='post')
+
+  prediction<-predict(model,domain_encoded)
+  return(list(modelid=modelid,domain=domain,class=ifelse(prediction[1]>0.9,1,0),probability=prediction[1]))
+}

app/launchservice.R

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+library(plumber)
+r <- plumb("/app/dga-classifier-service.R") 
+#r <- plumb("/home/harpo/Dropbox/ongoing-work/git-repos/dga-wb-r/docker/app/dga-classifier-service.R") 
+
+r$run(host = "0.0.0.0",port=8002)