Random Forest model yields incorrect predictions despite having accuracy of over 99 percent

For a ML course, I am supposed to build a model based on the training set to predict the variable "classe" on a validation set. I removed all unnecessary variables in the training set, used cross validation to prevent over-fitting, and made sure the validation set matched the training set in terms of which columns are removed. When I predict classe in the validation set, it yields all classe A, and I know this is incorrect.

I included the entire script below.

Where did I go wrong?

library(caret)

download.file("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv", "train.csv")
download.file("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv", "test.csv")

train <- read.csv("./train.csv")
val <- read.csv("./test.csv")

#getting rid of columns with NAs
nas <- sapply(train, function(x) sum(is.na(x)))
train <- train[, nas<1900]

#removing near zero variance columns
remove <- nearZeroVar(train)
train <- train[, -remove]

#create partition in our training set
set.seed(8675309)
inTrain <- createDataPartition(train$classe, p = .7, list = FALSE)
training <- train[inTrain,]
testing <- train[-inTrain,]

model <- train(classe ~ ., method = "rf", data = training)

confusionMatrix(predict(model, testing), testing$classe)

#make sure validation set has same features as training set
trainforvalid <- subset(training, select = -classe)
val <- val[, colnames(trainforvalid)]

predict(model, val)
#the above step yields all predictions as classe A