# версия полной проверки с проверкой русской орфографии

import os
from difflib import SequenceMatcher
from tqdm import tqdm
import datetime
import requests

# download stopwords corpus, you need to run it once
import nltk
#nltk.download("stopwords")
from nltk.corpus import stopwords
import pymorphy2
from string import punctuation


# ------------------------------- НАСТРОЙКИ ------------
# директория файла (на уровень выше, для структуры репозиториев 2 сем. 2022-23)
BASE_DIR = os.path.abspath(os.path.dirname(__file__))
# проверяемая директория
LECTION_DIR = os.path.join(BASE_DIR, "Лекции")

# ссылка для проверки
url = "http://213.155.192.79:3001/u21deev/ISRPO_Deev/src/3cf20668f4a06cec9cb60377eca8fd575ebb67a6/%d0%9b%d0%b5%d0%ba%d1%86%d0%b8%d0%b8/DeevStartUP.md"

# ------------------------------- / НАСТРОЙКИ ------------


url = url.replace("src", "raw")










#Create lemmatizer and stopwords list
morph = pymorphy2.MorphAnalyzer()
russian_stopwords = stopwords.words("russian")

#Preprocess function
def preprocess_text(text):
    translator = str.maketrans(punctuation, ' '*len(punctuation))
    words = text.translate(translator)
    words = words.lower().split()
    
    # очистка от прилегающего к слову мусора (слово, "или так")
    clear_words = []
    for word in words:
        clear_word = ""
        for s in word:
            if not s in punctuation:
                clear_word = clear_word + s
        clear_words.append(clear_word)
    tokens = []
    tokens = [morph.parse(token)[0].normal_form for token in clear_words if token not in russian_stopwords\
            and token != " " \
            and token.strip() not in punctuation \
            ]

    text = " ".join(tokens)    
    return tokens, text



#Preprocess function
import language_tool_python
tool = language_tool_python.LanguageTool('ru-RU')
def orfo_text(tokens):
    bad_tokens_n = 0
    for token in tokens:
        matches = tool.check(token)
        if len(matches)>0:
            bad_tokens_n += 1
            #print(matches[0].ruleId)

    return bad_tokens_n
    
print()

now = datetime.datetime.now().strftime('%d-%m-%Y %H:%M')
out_str = f"Время проверки: {now} \n"
# print(out_str)

response = requests.get(url)
post_html = response.text

post_list = post_html.split("\n")

# проверяем правильность оформления 1й строки
header_exist = True
line_1 = post_list[0].strip()
line_1 = line_1.replace(chr(65279), "")
if (line_1[0:2]) != "# ":
    print(f"Заголовок статьи не найден: '{line_1[0:1]} {line_1[1:2]}' вместо '# '")
    print(f"{ord(line_1[0:1])} {ord(line_1[1:2])} вместо {ord('#')} {ord(' ')}")
    header_exist = False

# наличие вопросов и списка литературы
quest_exist = False
source_exist = False
for post_line in post_list:
    if (post_line[0:2] == "##"):
        if ("Вопросы" in post_line):
            quest_exist = True
        if ("Список" in post_line) and ("литературы" in post_line):
            source_exist = True
if not (quest_exist):
    print("Вопросы не найдены")
if not (source_exist):
    print("Список литературы не найден")


header_text = line_1.replace("# ", "")
header_text = header_text.replace(".", "")
header_text = header_text.strip()
header_text = header_text.strip()
print(f"Заголовок: {header_text}")

# ищем другие лекции по этой теме
readme_path = os.path.join(BASE_DIR, LECTION_DIR, "README.md")
try:
    with open(readme_path, encoding="utf-8") as f:
        readme_html = f.read()
except:
    with open(readme_path, encoding="cp1251") as f:
        readme_html = f.read()



post_tokens, post_uniq_text = preprocess_text(post_html)
print(f"количество уникальных слов: {len(set(post_tokens))}")

bad_tokens_n = orfo_text(post_tokens)
bad_tokens_stat = int(bad_tokens_n / len(post_tokens) * 10000) / 100
print(f"процент ошибок: {bad_tokens_stat}%")
print()



min_ratio = 1000
min_ratio_name = ""

readme_list = readme_html.split("\n")
for readme_str in tqdm(readme_list):
    readme_str = readme_str.strip()

    if len(readme_str)>0:

        # строка с ссылкой на лекцию
        if "[" in readme_str:
            variant_name, t = readme_str.split("]")
            variant_name = variant_name.strip("[")
            t, variant_uri = readme_str.split("(")
            variant_uri = variant_uri.replace("),", "")
            variant_uri = variant_uri.replace(")", "")
            variant_uri = variant_uri.strip()
                
            if (not "youtube" in variant_uri) and (not "habr" in variant_uri):
                variant_path = os.path.join(BASE_DIR, LECTION_DIR, variant_uri)
                try:
                    with open(variant_path, encoding="utf-8") as f:
                        variant_html = f.read()
                except:
                    with open(variant_path, encoding="cp1251") as f:
                        variant_html = f.read()

                variant_tokens, variant_uniq_text = preprocess_text(variant_html)

                # пересечение множеств 
                min_tokens_len = min([len(set(post_tokens)), len(set(variant_tokens))])
                c = list(set(post_tokens) & set(variant_tokens))
                ratio = (1 - (len(c) / min_tokens_len)) * 100

                if min_ratio > ratio:
                    min_ratio = ratio
                    min_ratio_name = readme_str

print(f"min_ratio: {min_ratio:.2f}%")
print(f"{min_ratio_name}")