input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization and n-grams, and use a classification model to assign sentiments to the reviews. If the model successfully trains and predicts sentiments, return the classification report, otherwise return the string "Error".
  assert perform_sentiment_analysis('reviews.csv')==classification_report(y_test, predictions)
  assert perform_sentiment_analysis('invalid.csv')=="Error"
output: def perform_sentiment_analysis(file_path):
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          from sklearn.feature_extraction.text import CountVectorizer
          from sklearn.naive_bayes import MultinomialNB
          from sklearn.metrics import classification_report
          
          df = pd.read_csv(file_path)
          X = df['review']
          y = df['sentiment']
          
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          vectorizer = CountVectorizer(ngram_range=(1, 2))
          X_train_vec = vectorizer.fit_transform(X_train)
          X_test_vec = vectorizer.transform(X_test)
          
          model = MultinomialNB()
          model.fit(X_train_vec, y_train)
          
          predictions = model.predict(X_test_vec)
          return classification_report(y_test, predictions)
      except Exception as e:
          return "Error"