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library(keras)
library(plumber)
library(reticulate)
# 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")
hfhub <- reticulate::import('huggingface_hub')
model <- hfhub$from_pretrained_keras("harpomaxx/dga-detector")
modelid="cacic-2018-model"
valid_characters <- "$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]))
}
#* @assets ./static /
list()
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