from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

def sentiment_analysis_LR(input):
    # Assuming you have a Logistic Regression model and TfidfVectorizer in the pipeline
    input = preprocess_text(input)

    vectorizer = model_LR.named_steps['tfidfvectorizer']
    lr_classifier = model_LR.named_steps['logisticregression']

    # Transform the user input using the TF-IDF vectorizer
    user_input_tfidf = vectorizer.transform([input])

    # Make predictions
    user_pred = lr_classifier.predict(user_input_tfidf)

    # Display the prediction
    if user_pred[0] == 0:
        return 0
    else:
        return 1

def sentiment_analysis_NB(input):
  input = preprocess_text(input)

  vectorizer = model_NB.named_steps['tfidf']
  nb_classifier = model_NB.named_steps['nb']

  # Transform the user input using the TF-IDF vectorizer
  user_input_tfidf = vectorizer.transform([input])

  # Make predictions
  user_pred = nb_classifier.predict(user_input_tfidf)

  # Display the prediction
  if user_pred[0] == 0:
      return 0
  else:
      return 1