"function is not appropriate"

I am trying to use the "caret" library to train a multiclass classification algorithm.

I found the following website which supposedly contains a function that can be used for multiclass classification problem:Error metrics for multi-class problems in R: beyond Accuracy and Kappa | R-bloggers

require(compiler)
multiClassSummary <- cmpfun(function (data, lev = NULL, model = NULL){
  
  #Load Libraries
  require(Metrics)
  require(caret)
  
  #Check data
  if (!all(levels(data[, "pred"]) == levels(data[, "obs"]))) 
    stop("levels of observed and predicted data do not match")
  
  #Calculate custom one-vs-all stats for each class
  prob_stats <- lapply(levels(data[, "pred"]), function(class){
    
    #Grab one-vs-all data for the class
    pred <- ifelse(data[, "pred"] == class, 1, 0)
    obs  <- ifelse(data[,  "obs"] == class, 1, 0)
    prob <- data[,class]

    #Calculate one-vs-all AUC and logLoss and return
    cap_prob <- pmin(pmax(prob, .000001), .999999)
    prob_stats <- c(auc(obs, prob), logLoss(obs, cap_prob))
    names(prob_stats) <- c('ROC', 'logLoss')
    return(prob_stats) 
  })
  prob_stats <- do.call(rbind, prob_stats)
  rownames(prob_stats) <- paste('Class:', levels(data[, "pred"]))
  
  #Calculate confusion matrix-based statistics
  CM <- confusionMatrix(data[, "pred"], data[, "obs"])
  
  #Aggregate and average class-wise stats
  #Todo: add weights
  class_stats <- cbind(CM$byClass, prob_stats)
  class_stats <- colMeans(class_stats)

  #Aggregate overall stats
  overall_stats <- c(CM$overall)
 
  #Combine overall with class-wise stats and remove some stats we don't want 
  stats <- c(overall_stats, class_stats)
  stats <- stats[! names(stats) %in% c('AccuracyNull', 
    'Prevalence', 'Detection Prevalence')]
  
  #Clean names and return
  names(stats) <- gsub('[[:blank:]]+', '_', names(stats))
  return(stats)
  
})

I would like to use this function to train a "Decision Tree" model using the "F1 Score" (or any metric suitable for a multiclass problem). For example:

library(caret)
library(plyr)
library(C50)
library(dplyr)
library(compiler)


train.control <- trainControl(method = "repeatedcv", number = 10, repeats = 3, 
                              summaryFunction = multiClassSummary, classProbs = TRUE)


train_model <- train(my_data$response ~., data = my_data, method = "C5.0",
                 trControl=train.control ,
                 preProcess = c("center", "scale"),
                 tuneLength = 15,
                 metric = "F1")

But this produces the following error:

Error in ctrl$summaryFunction(testOutput, lev, method):
Your outcome has 4 levels. The prSummary() function isn't appropriate.

Can someone please show me how to fix this error? Thanks!