import re
from math import sqrt

def inf_verbs(text):
    verbs_dic = {}
    with open('verbs.txt', 'r') as txt:
        setp1 = txt.readlines()
    for i in setp1:
        tmp = i[:-2].split(',')
        #verbs_dic[tmp[0]] = [elem for elem in tmp[1:]]
        for l in tmp[1:]:
            verbs_dic[l] = tmp[0]
    words = text.split()
    for i in range(len(words)):
        try:
            words[i] = verbs_dic[words[i]]
        except:
            None
    return ' '.join(words)


def remove_punctuation(text):
    text2 = ''
    for i in range(len(text)):
        text2 += text[i] if text[i].isalpha() else ''
    return text2

def remove_pronouns(text):
    pronouns = [
        "je", "tu", "il", "elle", "nous", "vous", "ils", "elles",
        "me", "te", "se", "le", "la", "lui", "les", "leur", "y", "en",
        "moi", "toi", "soi", "toi-même", "vous-même", "lui-même", "elle-même",
        "eux-même", "elles-même", "nous-même", "me-même", "te-même", "on", "qu'il", "qu'ils", "qu'elle", "qu'elles"
    ]
    pattern = r'\b(?:' + '|'.join(pronouns) + r')\b'
    return re.sub(pattern, '', text, flags=re.IGNORECASE)

def remove_miscellaneous(text):
    prepositions = [
        "de", "du", "des", "d'", "le", "la", "les", "l'", "un", "une", "au",
        "aux", "en", "dans", "sur", "sous", "vers", "à", "chez", "par", "pour",
        "avec", "sans", "entre", "pendant", "depuis", "devant", "derrière",
        "près", "loin", "jusque", "jusqu'à", "à côté de", "et", "ou", "qu", "s'",
        "m'", "t'", "n'", "a", "c'", "t'", "n'y"
    ]
    pattern = r'\b(?:' + '|'.join(prepositions) + r')\b'
    return re.sub(pattern, '', text, flags=re.IGNORECASE)

def remove_line_breaks(text):
    return text.replace('\n', ' ')

def text_to_vector(text, all_words):
    words = text.split()
    word_count = {}
    for word in words:
        word_count[word] = word_count.get(word, 0) + 1
    return [word_count.get(word, 0) for word in all_words]

def cosine_similarity(vec1, vec2):
    dot_product, norm1, norm2 = 0, 0, 0
    for x, y in zip(vec1, vec2):
        dot_product += x * y
        norm1 += x ** 2
        norm2 += y ** 2
    return dot_product / (sqrt(norm1) * sqrt(norm2))

def cosine_similarity_rec(vec1, vec2):
    def recur_part(vec1, vec2):
        if vec1 == []:
            return [0, 0, 0]
        suivant = recur_part(vec1[1:], vec2[1:])
        return [vec1[0] * vec2[0] + suivant[0], vec1[0] ** 2 + suivant[1], vec2[0] ** 2 + suivant[2]]
    result = recur_part(vec1, vec2)
    for i in (1, 2):
        result[i] = 1 if result[i] == 0 else result[i]
    return result[0] / (sqrt(result[1]) * sqrt(result[2]))

def load_text(name):
    with open(f'{name}.txt', 'r') as file:
        return file.read()

text1 = load_text('')
text2 = load_text('')

text1_cleaned = inf_verbs(remove_line_breaks(remove_miscellaneous(remove_pronouns(remove_punctuation(text1.lower())))))
text2_cleaned = inf_verbs(remove_line_breaks(remove_miscellaneous(remove_pronouns(remove_punctuation(text2.lower())))))

all_words = set(text1_cleaned.split()).union(set(text2_cleaned.split()))
vec1 = text_to_vector(text1_cleaned, all_words)
vec2 = text_to_vector(text2_cleaned, all_words)

similarity = cosine_similarity(vec1, vec2)
print(f"Les textes ont un score de similarité de {round(similarity, 3)}. Ils ont été convertit en deux vecteurs de {len(vec1)} dimensions.")