How do I use predict.randomforest with inputs from Shiny app in R

I am have created a shiny app that shows how a random forest model works.

I want to use inputs from Shiny app's interface to make a classification.

This are examples of inputs

    tabItem(tabName = "randomForest",
              
              fluidRow(
                h1("Select Parameters for Decision Tree")
              ),
              
              fluidRow(
                box(plotOutput(outputId = 'randomForest', 
                               height = "700px", width = "500px")),
                
                box(
                  title = "Fixed Acidity",
                  sliderInput("fixed", "TBD",
                              min = 0, max = 2, value = 1)),
                
                box(
                  title = "Volatile Acidity",
                  sliderInput("vol", "Gaseous acids that contribute to the smell and taste of vinegar in wine.",
                              min = 0, max = 2, value = 1)),
                box(
                  title = "Citric Acidity",
                  sliderInput("citric", "TBD",
                              min = 0, max = 1, value = 0.5)),
                box(
                  title = "Residual Sugar",
                  sliderInput("resugar", "TBD",
                              min = 0, max = 16, value = 8)),

I have built a random forest model already and want to use predict.randomforest to make classifications. But I am not sure how to use predict.randomforest together with inputs from the Shiny app.

The random forest code

Random forest model "randomForest"
  set.seed(234)
  train.index=createDataPartition(data$quality,p=0.7,list=FALSE)
  train=wine[train.index,]
  test=wine[-train.index,]
  wine.rf <- randomForest(quality~.,data=train,
                         # mtry=4,importance=input$importance,
                          #ntree=input$ntree)
  pred.rf=predict(wine.rf,newdata=test[,-ncol(test)])
  mean(pred.rf==test[,ncol(test)])
  
  detach(package:rattle)
  importance(wine.rf)

Thank you