text: #!/usr/bin/python
  
  from nltk.stem.snowball import SnowballStemmer
  import string
  
  def parseOutText(f):
      """ given an opened email file f, parse out all text below the
          metadata block at the top
          (in Part 2, you will also add stemming capabilities)
          and return a string that contains all the words
          in the email (space-separated) 
          
          example use case:
          f = open("email_file_name.txt", "r")
          text = parseOutText(f)
          
          """
  
  
      f.seek(0)  ### go back to beginning of file (annoying)
      all_text = f.read()
  
      ### split off metadata
      content = all_text.split("X-FileName:")
      words = ""
      if len(content) > 1:
          ### remove punctuation
          text_string = content[1].translate(string.maketrans("", ""), string.punctuation)
  
          ### project part 2: comment out the line below
          words = text_string
  
          ### split the text string into individual words, stem each word,
          ### and append the stemmed word to words (make sure there's a single
          ### space between each stemmed word)
          
  
  
  
  
      return words
  
      
  
  def main():
      ff = open("../text_learning/test_email.txt", "r")
      text = parseOutText(ff)
      print (text)
  
  
  
  if __name__ == '__main__':
      main()
  
  