Error while making prediction using GBM model and mrsdeploy package

Question

I am facing issue while calling predict.gbm using mrsdeploy function after publishing model as service.Below are two codes, one is demo code wherein predict function for glm model works fine both locally and after publishing service but GBM code works only locally and not after publishing the model

### -------------------------------------

library(mrsdeploy)

library(gbm)

-----------------Demo Code works fine both locally and after publishing service--------------

carsModel <- glm(formula = am ~ hp + wt, data = mtcars, family = binomial)

#function that can use the model
manualTransmission <- function(hp, wt) {
   newdata <- data.frame(hp = hp, wt = wt)
   predict(carsModel, newdata, type = "response")
}
# test function locally by printing results
print(manualTransmission(120, 2.8)) 

output=> 0.6418125    

remoteLogin("http://localhost:12800",
            username = "admin", 
            password = "****",
            session = FALSE)


api <- publishService(
   "mtService",
   code = manualTransmission,
   model = carsModel,
   inputs = list(hp = "numeric", wt = "numeric"),
   outputs = list(answer = "numeric"),
   v = "v1.0.0"
)
#test model prediction using api call
result <- api$manualTransmission(120, 2.8)

print(result$output("answer")) 
output=> 0.6418125
#Demo Code Ends


###################GBM Code
#load  model object
new_model <- readRDS("C:\\Users\\c11421\\Desktop\\new_model.RDS")

# Produce a prediction function that can use the model
salarypred <- function(PEAAK_EMI_TS,CITY_TIER,TOTAL_SANC_AMT_CC,TIME_SINCE_FIRST_CC,MIN_LIVE_AMT_BY_TENNURE_BL_AND_P,CUSTOMER_COMPANY_CATEGORY_PL,TS_AVG_SANC_CLOSED_PL,AVG_LIVE_AMT_BY_TENNURE_THICK,MIN_SANC_LIVE_REV,AGE_AT_POS,AVG_PAID_LIVE_HL,TIME_SINCE_LAST_LIVE_CC,MIN_MOB_BL_AND_PL,TS_MIN_SANC_CLOSED_AL,MIN_LIVE_AMT_BY_TENNURE_AL,AVG_TENNURE_BL_AND_PL,RESTYPE_RENTED,GENDER
) {
  newdata <- data.frame(PEAAK_EMI_TS=PEAAK_EMI_TS , CITY_TIER=CITY_TIER , TOTAL_SANC_AMT_CC=TOTAL_SANC_AMT_CC , TIME_SINCE_FIRST_CC=TIME_SINCE_FIRST_CC , MIN_LIVE_AMT_BY_TENNURE_BL_AND_P=MIN_LIVE_AMT_BY_TENNURE_BL_AND_P , CUSTOMER_COMPANY_CATEGORY_PL=CUSTOMER_COMPANY_CATEGORY_PL , TS_AVG_SANC_CLOSED_PL=TS_AVG_SANC_CLOSED_PL , AVG_LIVE_AMT_BY_TENNURE_THICK=AVG_LIVE_AMT_BY_TENNURE_THICK , MIN_SANC_LIVE_REV=MIN_SANC_LIVE_REV , AGE_AT_POS=AGE_AT_POS , AVG_PAID_LIVE_HL=AVG_PAID_LIVE_HL , TIME_SINCE_LAST_LIVE_CC=TIME_SINCE_LAST_LIVE_CC , MIN_MOB_BL_AND_PL=MIN_MOB_BL_AND_PL , TS_MIN_SANC_CLOSED_AL=TS_MIN_SANC_CLOSED_AL , MIN_LIVE_AMT_BY_TENNURE_AL=MIN_LIVE_AMT_BY_TENNURE_AL , AVG_TENNURE_BL_AND_PL=AVG_TENNURE_BL_AND_PL , RESTYPE_RENTED=RESTYPE_RENTED , GENDER=GENDER)

  predict.gbm(new_model, newdata, type = "response",n.trees=2722)
}

# test function locally by printing results
print(salarypred(10.61928526,'Tier1',12.16838635,166,-999,'zzzzz',-999,71,82603,42,-999,166,-999,945000,-999,-999,0,'MALE'))  

output=>10.8409

remoteLogin("http://localhost:12800", 
            username = "admin", 
            password = "*****",
            session = FALSE)


 api <- publishService(
   "salService",
   code = salarypred,
   model = new_model,
  inputs = list(PEAAK_EMI_TS= "numeric",CITY_TIER= "character",TOTAL_SANC_AMT_CC= "numeric",TIME_SINCE_FIRST_CC= "numeric",MIN_LIVE_AMT_BY_TENNURE_BL_AND_P= "numeric",CUSTOMER_COMPANY_CATEGORY_PL="character",TS_AVG_SANC_CLOSED_PL= "numeric",AVG_LIVE_AMT_BY_TENNURE_THICK= "numeric",MIN_SANC_LIVE_REV= "numeric",AGE_AT_POS= "numeric",AVG_PAID_LIVE_HL= "numeric",TIME_SINCE_LAST_LIVE_CC= "numeric",MIN_MOB_BL_AND_PL= "numeric",TS_MIN_SANC_CLOSED_AL= "numeric",MIN_LIVE_AMT_BY_TENNURE_AL= "numeric",AVG_TENNURE_BL_AND_PL= "numeric",RESTYPE_RENTED= "numeric",GENDER="character"),
   outputs = list(answer = "numeric"),
   v = "v1.0.0"
 )


#test model prediction using api call
result <- api$salarypred(10.61928526,'Tier1',12.16838635,166,-999,'zzzzz',-999,71,82603,42,-999,166,-999,945000,-999,-999,0,'MALE')

print(result$output("answer"))

 output=>NULL

result[["errorMessage"]]

#Error Message as below
"Error in predict.gbm(new_model, newdata, type = "response", n.trees = 2722) : could not find function "predict.gbm"answer could not be returned."