input: Write a python function to predict sentiment for given sentences using a neural network model.
  assert predict_sentiment(["I am good", "It's terrible", "What a wonderful day!"])==["positive", "negative", "positive"]
  assert predict_sentiment(["I hate this", "Love it", "Not bad"])==["negative", "positive", "positive"]
  assert predict_sentiment(["Awful experience", "Best service ever", "Could be better"])==["negative", "positive", "negative"]
output: def predict_sentiment(sentences):
      # Dummy model and tokenizer for example
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.linear_model import LogisticRegression
  
      # Example training data for the dummy model
      train_sentences = ["I am happy", "I am sad", "This is good", "This is bad", "I love this", "I hate this"]
      train_labels = ["positive", "negative", "positive", "negative", "positive", "negative"]
  
      # Create a simple vectorizer and logistic regression model
      vectorizer = CountVectorizer()
      X_train = vectorizer.fit_transform(train_sentences)
      model = LogisticRegression()
      model.fit(X_train, train_labels)
  
      # Transform the input sentences and predict
      X_input = vectorizer.transform(sentences)
      predictions = model.predict(X_input)
  
      return predictions.tolist()