topic modeling error (doc2bow expects an array of unicode tokens on input, not a single string)

from nltk.tokenize import RegexpTokenizer
#from stop_words import get_stop_words
from gensim import corpora, models 
import gensim
import os
from os import path
from time import sleep

filename_2 = "buisness1.txt"
file1 = open(filename_2, encoding='utf-8')  
Reader = file1.read()
tdm = []

# Tokenized the text to individual terms and created the stop list
tokens = Reader.split()
#insert stopwords files
stopwordfile = open("StopWords.txt", encoding='utf-8')  

# Use this to read file content as a stream  
readstopword = stopwordfile.read() 
stop_words = readstopword.split() 

for r in tokens:  
    if not r in stop_words: 
        #stopped_tokens = [i for i in tokens if not i in en_stop]
        tdm.append(r)

dictionary = corpora.Dictionary(tdm)
corpus = [dictionary.doc2bow(i) for i in tdm]
sleep(3)
#Implemented the LdaModel
ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=10, id2word = dictionary)
print(ldamodel.print_topics(num_topics=1, num_words=1))

I am trying to remove stop words using a separate txt file which contains stop words. And after I remove the stop words I am appending words of the text file which is not there in the stop words. I am getting the error doc2bow expects an array of unicode tokens on input, not a single string at dictionary = corpora.Dictionary(tdm).

Can anyone help me correct my code

Answer

This is almost certainly a duplicate, but use this instead:

dictionary = corpora.Dictionary([tdm])

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Answer