Seed data selection problem in deep learning that generates writing

Text generation deep learning code. I'd like to use the text I specified instead of selecting a random text seed here. How do I modify it? Also, do I really need to write and specify text in get_file() when I first import a file?

library(keras)
library(stringr)
path <- get_file(
  "nietzsche.txt",
  origin = "https://s3.amazonaws.com/text-datasets/nietzsche.txt"
)
text <- tolower(readChar(path, file.info(path)$size))
cat("Corpus length:", nchar(text), "\n")

maxlen <- 50  # Length of extracted character sequences
step <- 3  # We sample a new sequence every `step` characters

text_indexes <- seq(1, nchar(text) - maxlen, by = step)
# This holds our extracted sequences
sentences <- str_sub(text, text_indexes, text_indexes + maxlen - 1)
# This holds the targets (the follow-up characters)
next_chars <- str_sub(text, text_indexes + maxlen, text_indexes + maxlen)
cat("Number of sequences: ", length(sentences), "\n")
# List of unique characters in the corpus
chars <- unique(sort(strsplit(text, "")[[1]]))
cat("Unique characters:", length(chars), "\n")
# Dictionary mapping unique characters to their index in `chars`
char_indices <- 1:length(chars) 
names(char_indices) <- chars
# Next, one-hot encode the characters into binary arrays.
cat("Vectorization...\n") 
x <- array(0L, dim = c(length(sentences), maxlen, length(chars)))
y <- array(0L, dim = c(length(sentences), length(chars)))
for (i in 1:length(sentences)) {
  sentence <- strsplit(sentences[[i]], "")[[1]]
  for (t in 1:length(sentence)) {
    char <- sentence[[t]]
    x[i, t, char_indices[[char]]] <- 1
  }
  next_char <- next_chars[[i]]
  y[i, char_indices[[next_char]]] <- 1
}



model <- keras_model_sequential() %>% 
  layer_lstm(units = 128, input_shape = c(maxlen, length(chars))) %>% 
  layer_dense(units = length(chars), activation = "softmax")


optimizer <- optimizer_rmsprop(lr = 0.01)
model %>% compile(
  loss = "categorical_crossentropy", 
  optimizer = optimizer
)  

sample_next_char <- function(preds, temperature = 1.0) {
  preds <- as.numeric(preds)
  preds <- log(preds) / temperature
  exp_preds <- exp(preds)
  preds <- exp_preds / sum(exp_preds)
  which.max(t(rmultinom(1, 1, preds)))
}

for (epoch in 1:60) {
  
  cat("epoch", epoch, "\n")
  
  # Fit the model for 1 epoch on the available training data
  model %>% fit(x, y, batch_size = 128, epochs = 1) 
  
  # Select a text seed at random
  start_index <- sample(1:(nchar(text) - maxlen - 1), 1)  
  seed_text <- str_sub(text, start_index, start_index + maxlen - 1)
  
  cat("--- Generating with seed:", seed_text, "\n\n")
  
  for (temperature in c(0.2, 0.5, 1.0, 1.2)) {
    
    cat("------ temperature:", temperature, "\n")
    cat(seed_text, "\n")
    
    generated_text <- seed_text
    
    # We generate 400 characters
    for (i in 1:400) {
      
      sampled <- array(0, dim = c(1, maxlen, length(chars)))
      generated_chars <- strsplit(generated_text, "")[[1]]
      for (t in 1:length(generated_chars)) {
        char <- generated_chars[[t]]
        sampled[1, t, char_indices[[char]]] <- 1
      }
      
      preds <- model %>% predict(sampled, verbose = 0)
      next_index <- sample_next_char(preds[1,], temperature)
      next_char <- chars[[next_index]]
      
      generated_text <- paste0(generated_text, next_char)
      generated_text <- substring(generated_text, 2)
      
      cat(next_char)
    }
    cat("\n\n")
  }
}