def search_suggestion(search_term, text):
    token = nltk.word_tokenize(text)
    bigrams = ngrams(token, 2)
    trigrams = ngrams(token, 3)
    fourgrams = ngrams(token, 4)
    fivegrams = ngrams(token, 5)
    grams = [ngrams(token, 2),ngrams(token, 3),ngrams(token, 4),ngrams(token, 5)]
    split_term = tuple(search_term.split(' '))
    search_term_length = len(search_term.split(' '))
    counted_grams = Counter(grams[search_term_length-1])
    combined_term = 'No suggested searches'
    matching_terms = [element for element in list(counted_grams.items()) if \
        element[0][:-1] == tuple(split_term)]
    if (len(matching_terms) > 0):
        frequencies = [item[1] for item in matching_terms]
        maximum_frequency = np.max(frequencies)
        highest_frequency_term = [item[0] for item in matching_terms if item[1] == \
          maximum_frequency][0]
        combined_term = ' '.join(highest_frequency_term)
    return(combined_term)
