input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization and n-grams. The function should generate features for sentiment analysis and use a classification model to assign sentiments to the reviews. The function should return a classification report based on the sentiment predictions.
  assert sentiment_analysis('reviews.csv')=='classification report as a string'
  assert sentiment_analysis('another_reviews.csv')=='classification report as a string'
  assert sentiment_analysis('nonexistent_file.csv')=='File not found or invalid file format'
  assert sentiment_analysis('')=='Invalid file path'
  assert sentiment_analysis(None)=='Invalid input'
output: 
  import pandas as pd
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.model_selection import train_test_split
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.metrics import classification_report
  
  def sentiment_analysis(file_path):
      if file_path is None:
          return 'Invalid input'
      if not file_path:
          return 'Invalid file path'
      try:
          df = pd.read_csv(file_path)
      except FileNotFoundError:
          return 'File not found or invalid file format'
      except pd.errors.EmptyDataError:
          return 'File not found or invalid file format'
      
      df['review'] = df['review'].fillna('')
      df['sentiment'] = df['sentiment'].fillna('neutral')
      
      X_train, X_test, y_train, y_test = train_test_split(df['review'], df['sentiment'], test_size=0.2, random_state=42)
      
      vectorizer = TfidfVectorizer(ngram_range=(1, 2))
      X_train_tfidf = vectorizer.fit_transform(X_train)
      X_test_tfidf = vectorizer.transform(X_test)
      
      model = MultinomialNB()
      model.fit(X_train_tfidf, y_train)
      
      y_pred = model.predict(X_test_tfidf)
      
      return classification_report(y_test, y_pred)
  