text: import pandas as pd
  from sklearn.model_selection import train_test_split
  
  from services.globals import *
  
  
  def remove_stopwords(text, stopwords):
      useful = [w for w in text if w not in stopwords]
      return useful
  
  
  def getDoc(document):
      d = []
      for doc in document:
          d.append(getStem(doc))
      return d
  
  
  def getStem(review):
      review = review.lower()
      tokens = tokenizer.tokenize(review)
      removed_stopwords = [w for w in tokens if w not in stop_words]
      stemmed_words = [ps.stem(token) for token in removed_stopwords]
      clean_review = ' '.join(stemmed_words)
      return clean_review
  
  
  def check(content):
      df = pd.read_csv('df.csv')
      data = df.to_numpy()
      y = data[:, 1]
      X = data[:, 0]
      stemmed_doc = getDoc(X)
      cv = CountVectorizer()
      vc = cv.fit_transform(stemmed_doc)
      X = vc.toarray()
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
      model.fit(X_train, y_train)
      y_pred = model.predict(cv.transform(getDoc([content])))
      print(y_pred)
      return y_pred
  