Dimension error when making a neural network with Keras in R

I am trying to build a neural network using keras in R. I am trying to use a dataset comprised of certain variables from another dataset. I think I have made an error in data partition, but I am not too sure what is the error.

For context, I have used feature selection methods to identify the most important features. Then, I created a dataset comprised of the most important features. I am now trying to use this dataset to train a neural network.

I am getting the following error:

Error in py_call_impl(callable, dots$args, dots$keywords) : 
 ValueError: Data cardinality is ambiguous:
 x sizes: 3505
 y sizes: 626
  Make sure all arrays contain the same number of samples.

I am using the following R code:

#Loading Libraries 

library(dplyr)
library(faux)
library(caret)
library(randomForest)
library(ROSE)
library(e1071)
library(corrplot)
library(tidymodels)
library(tidyverse)
library(class)
library(caTools)
library(DataExplorer)
library(glmnet)
library(xgboost)
library(class)
library(pROC)
library(neuralnet)
library(keras)

hc <- data$hc
cwtfp <- data$cwtfp 
irr <- data$irr
pl_c <- data$pl_c
pl_n <- data$pl_n
forestry_change <- data$forestry_change
agriculture_change <- data$agriculture_change
growthbucket_factor_imp <- data$growthbucket_factor

 data_important <- data.frame(hc, cwtfp, irr, pl_c, pl_n, forestry_change,      agriculture_change, growthbucket_factor_imp)

 #Splitting the important dataset

 set.seed(1)
 sample <- sample.split(data_important$growthbucket_factor, SplitRatio = 0.7)
 train_imp <- subset(data_important, sample == TRUE)
 test_imp <- subset(data_important, sample == FALSE)

 #Dense Neural Network 

 #data preparation

 data_important_matrix <- as.matrix(data_important)
 dimnames(data_important_matrix) <- NULL

 #Partition
 set.seed(1234)
 ind <- sample(2, nrow(data_important_matrix), replace = T, prob = c(0.7,0.3))
 training_nn <- data_important_matrix[ind==1, 1:7]
 test_nn <- data_important_matrix[ind==2, 1:7]
 trainingtarget_nn <- data_important_matrix[ind==1, 8]
 testtarget_nn <- data_important_matrix[ind==2, 8]

 #Data processing

 training_nn <- as.numeric(unlist(training_nn))
 test_nn <-  as.numeric(unlist(training_nn))
                          
 #one hot encoding

 trainnnLabels <- to_categorical(trainingtarget_nn)
 testnnLabels <- to_categorical(testtarget_nn)

 #Creating the model specification

 model <- keras_model_sequential() 
 model %>%
  layer_dense(units = 8, activation = 'relu', input_shape = c(8)) %>%
  layer_dense(units = 4, activation = 'relu') %>%
  layer_dense(units = 1, activation = 'softmax')

  summary(model)

 #Compile
 model %>% compile(loss = 'binary_crossentropy',
                   optimizer = 'adam',
                   metrics = 'accuracy')

 #Fitting the model 

 history <- model %>%
   fit(training_nn,
       trainnnLabels,
       epoch = 200,
       batch_size = 32,
       validation_split = 0.2)