old_contents: ## compute_input.py
  
  import sys,json
  #import nltk
  #Read data from stdin
  def read_in():
      lines = sys.stdin.readlines()
      print(lines)
      # Since our input would only be having one line, parse our JSON data from that
      return json.loads(lines[0])
  
  def main():
      print("Hello World")
      # #get our data as an array from read_in()
      # from nltk.tokenize import word_tokenize
      # from nltk.corpus import stopwords
      # #from nltk.tag import StanfordNERTagger
      # lines = read_in()
      # tokens = word_tokenize(lines)
      # stop_words = set(stopwords.words('english'))
      # clean_tokens = [w for w in tokens if not w in stop_words]
      # tagged = nltk.pos_tag(clean_tokens)
      # # st = StanfordNERTagger('./controllers/english.all.3class.distsim.crf.ser.gz',
  	# # 				   './controllers/stanford-ner.jar',
  	# # 				   encoding='utf-8')
      # # classified_text = st.tag(clean_tokens)
  
      # # print(classified_text)                   
      # print(nltk.ne_chunk(tagged))
     
  # Start process
  if __name__ == '__main__':
      main()
  
new_contents: ## compute_input.py
  
  import sys
  import json
  import nltk
  #import nltk
  #Read data from stdin
  def read_in():
      lines = sys.stdin.readlines()
      print(lines)
      # Since our input would only be having one line, parse our JSON data from that
      return json.loads(lines[0])
  
  def main():
      print("Hello World")
      # #get our data as an array from read_in()
      # from nltk.tokenize import word_tokenize
      # from nltk.corpus import stopwords
      # #from nltk.tag import StanfordNERTagger
      # lines = read_in()
      # tokens = word_tokenize(lines)
      # stop_words = set(stopwords.words('english'))
      # clean_tokens = [w for w in tokens if not w in stop_words]
      # tagged = nltk.pos_tag(clean_tokens)
      # # st = StanfordNERTagger('./controllers/english.all.3class.distsim.crf.ser.gz',
  	# # 				   './controllers/stanford-ner.jar',
  	# # 				   encoding='utf-8')
      # # classified_text = st.tag(clean_tokens)
  
      # # print(classified_text)                   
      # print(nltk.ne_chunk(tagged))
     
  # Start process
  if __name__ == '__main__':
      main()
  
subject: change in nltk path