import math
import string


def maybe_is_text(s, thresh=2.5):
    if len(s) == 0:
        return False
    # Calculate the entropy of the string
    entropy = 0
    for c in string.printable:
        p = s.count(c) / len(s)
        if p > 0:
            entropy += -p * math.log2(p)

    # Check if the entropy is within a reasonable range for text
    if entropy > thresh:
        return True
    return False


def maybe_is_code(s):
    if len(s) == 0:
        return False
    # Check if the string contains a lot of non-ascii characters
    if len([c for c in s if ord(c) > 128]) / len(s) > 0.1:
        return True
    return False


def strings_similarity(s1, s2):
    if len(s1) == 0 or len(s2) == 0:
        return 0
    # break the strings into words
    s1 = set(s1.split())
    s2 = set(s2.split())
    # return the similarity ratio
    return len(s1.intersection(s2)) / len(s1.union(s2))


def maybe_is_truncated(s):
    punct = [".", "!", "?", '"']
    if s[-1] in punct:
        return False
    return True


def maybe_is_html(s):
    if len(s) == 0:
        return False
    # check for html tags
    if "<body" in s or "<html" in s or "<div" in s:
        return True