input: Write a function to predict sentiment of a given text using Naive Bayes classifier with TF-IDF vectorization, and test it using the assert statements.
  assert predict_sentiment("This is a bad day.") == "Negative"
  assert predict_sentiment("I love this product.") == "Positive"
  assert predict_sentiment("The weather is terrible today.") == "Negative"
output: def predict_sentiment(text):
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      # Example training data
      train_texts = [
          "I love this!", "This is an amazing place!", "I feel great about this", "This is wonderful",
          "I hate this!", "This is a bad place!", "I feel terrible about this", "This is awful"
      ]
      train_labels = ["Positive", "Positive", "Positive", "Positive", "Negative", "Negative", "Negative", "Negative"]
  
      # Create a pipeline that combines TfidfVectorizer and MultinomialNB
      model = make_pipeline(TfidfVectorizer(), MultinomialNB())
      
      # Train the model
      model.fit(train_texts, train_labels)
      
      # Predict the sentiment
      predicted_label = model.predict([text])[0]
      
      return predicted_label