Codecademy export

script.py

# These are the emails you will be censoring. The open() function is opening the text file that the emails are contained in and the .read() method is allowing us to save their contexts to the following variables:
email_one = open("email_one.txt", "r").read()
email_two = open("email_two.txt", "r").read()
email_three = open("email_three.txt", "r").read()
email_four = open("email_four.txt", "r").read()

def censor(text, word):
  return text.replace(word, "*******")

#print(censor(email_one, "learning algorithms"))

def censor_more(text, proprietary_terms):
  for term in proprietary_terms:
      if term in text:
        text = censor(text, term)
  return text

proprietary_terms = ["she", "personality matrix", "sense of self", "self-preservation", "learning algorithm", "her", "herself"]

#print(censor_more(email_two, proprietary_terms))

negative_words = ["concerned", "behind", "danger", "dangerous", "alarming", "alarmed", "out of control", "help", "unhappy", "bad", "upset", "awful", "broken", "damage", "damaging", "dismal", "distressed", "distressed", "concerning", "horrible", "horribly", "questionable"]

def censor_negative(text, negative_words):
  revised_text = censor_more(text, proprietary_terms)
  count = 0 #negative word count
  for term in negative_words:
    if term in revised_text:
      count += 1
    if term in revised_text and count == 2:
      revised_text = censor(revised_text, term)
  return revised_text

#print(censor_negative(email_three, negative_words))

def max_censorship(text):
  censor_char = "*"
  redact_word_indices = []
  max_revised = censor_negative(text, negative_words)
  temp_email = max_revised.split()

  for i in range(len(temp_email)):
    if censor_char in temp_email[i]:
      redact_word_indices.append(i)

  for i in redact_word_indices:
    temp_email[i-1] = "*" * len(temp_email[i-1])
    temp_email[i+1] = "*" * len(temp_email[i+1])

  max_revised = " ".join(temp_email)
  return max_revised

#print(max_censorship(email_four))