import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
import re


nltk.download('punkt')  # For tokenization
nltk.download('averaged_perceptron_tagger')  # For POS tagging (optional)


def sub_explitives(textfile, selection):

    replacetext = "person"

    # text = word_tokenize(textfile)
    # print(text)
    # sentences = sent_tokenize(textfile)

    if selection == "B":
        target_word = r"\bbitch\b"
    elif selection == "N":
        target_word = r"\bnigg\b"
    elif selection == "all":
        target_word = r"\bshit\b"
    else:
        target_word = None
        
    print("selection:", selection, "target_word:", target_word)

    if target_word:
        print("target word was found, ", target_word)
        print(text)
        textfile = re.sub(target_word, replacetext, textfile, flags=re.IGNORECASE)
    return textfile