ООП-программирование на Python для парсинга веб-страниц и извлечения ключевых слов не работает из-за ошибок в синтаксисе кода [закрыто]

Question

Я пытаюсь включить этот фрагмент кода в синтаксис ООП. Код хорошо работает вне схемы ООП. Мне нужно поместить неопределенные переменные, взятые из ранее вызванных функций, внутри объектов, чтобы он работал, но я не знаю, как этого добиться! Любая помощь или идеи были бы действительно полезны.

       import spacy
 import string 
 from spacy.lang.en.stop_words import STOP_WORDS
 import en_core_web_sm
 import textacy
 from url_extractor import RecursiveScraper
 import requests
 from bs4 import BeautifulSoup
 import itertools
 from sklearn.feature_extraction.text import TfidfVectorizer
 import pandas as pd

nlp = spacy.load("en_core_web_sm")

class KeywordExtractor: #  this is the class
    rscraper = RecursiveScraper(input()) # this is the url web scraper from another code imported (it was a class there and I imported here as a library) it asks for a url
    rscraper.scrape()
    print(rscraper.urls)
    new= list(rscraper.urls) # here we scrape all the sub-urls from the main url
    for i in new:
       print(i)

    def get_Text():
       r = requests.get(i) # undefined variable 1
       soup = BeautifulSoup(r.content, "html.parser") # here we het the content of each url separately
       for script in soup(["script", "style"]):
            script.extract()  
            text = soup.get_text()
            lines = (line.strip() for line in text.splitlines())
            chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
            text = '\n'.join(chunk for chunk in chunks if chunk)
            new = text.encode("utf-8")
            final=new.decode("utf-8")
            new1=final.lower()
            s="".join([w for w in new1 if w not in string.punctuation])
        return s # here we return the content as a string without punctiations


    def remove_verbs():
        punctuations = string.punctuation
        stopwords = list(STOP_WORDS)
        nlp = spacy.load('en_core_web_sm')
        docx = nlp(s) # undefined variable 2
        nouns1 = []
        for token in docx:
            if token.is_stop != True and token.is_punct != True and token.pos_ == 'VERB':
                nouns1.append(token)
        list_of_strings1  = [y.text for y in nouns1] # we add the verbs in the stopword list

        for z in list_of_strings1:
            if z not in stopwords:
                stopwords.append(z)

        no_stop_txt = []
        for token in docx:
            if token.text not in stopwords:
               no_stop_txt.append(token.text)
        return no_stop_txt # we append every verb of the text data in the stopword list

        str1=" ".join(no_stop_txt) # turns spacy,token.token into a string
        res = "".join(filter(lambda x: not x.isdigit(), str1)) # we remove digits from the string
        res=[res]  # turns string to list to be read from the tfidf
   

    def compute_tfidf():
        vectorizer = TfidfVectorizer(max_df=1, min_df=1, stop_words=None, use_idf=True, norm=None)
        transformed_documents = vectorizer.fit_transform(res) # compute tfidf from the list with undefined variable 3 
        feature_names = vectorizer.get_feature_names()



 if __name__=="__main__":
    keywords = KeywordExtractor()
    keywords.compute_tfidf # no idea what to do here and how to fill the missing parts and undefined variables!