input: Write a python function to classify new text using a trained logistic regression model from a given corpus.
  assert classify_text(['This is a positive text', 'This is a negative text', 'This is another positive text', 'This is another negative text'], [1, 0, 1, 0], 'This is a text to classify') == 'Negative'
  assert classify_text(['The weather is sunny', 'I love this product', 'The weather is rainy', 'I dislike this product'], [1, 1, 0, 0], 'I love the weather') == 'Positive'
  assert classify_text(['I am feeling great', 'This is not good', 'Such a wonderful day', 'This is bad'], [1, 0, 1, 0], 'I am feeling bad') == 'Negative'
output: def classify_text(corpus, labels, new_text):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.linear_model import LogisticRegression
  
      # Step 1: Convert text data into feature vectors
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(corpus)
  
      # Step 2: Train the logistic regression model
      model = LogisticRegression()
      model.fit(X, labels)
  
      # Step 3: Transform the new text using the same vectorizer
      new_text_transformed = vectorizer.transform([new_text])
  
      # Step 4: Use the trained model to make a prediction
      prediction = model.predict(new_text_transformed)[0]
  
      # Step 5: Map the prediction to a label
      label_mapping = {0: 'Negative', 1: 'Positive'}
      return label_mapping[prediction]