input: def sentiment_analysis_report(filename: str) -> str:
      """ Perform sentiment analysis on product reviews by reading the reviews from a CSV file,
      preprocess the text data using techniques like tokenization, stopwords removal, and lemmatization.
      Generate features using CountVectorizer. Train a Multinomial Naive Bayes model to classify reviews and 
      generate a classification report for the sentiment predictions.
      >>> sentiment_analysis_report('reviews.csv')
      '              precision    recall  f1-score   support\n\n           0       0.88      0.83      0.86       103\n           1       0.85      0.90      0.87       120\n\n    accuracy                           0.86       223\n   macro avg       0.86      0.86      0.86       223\nweighted avg       0.86      0.86      0.86       223\n'
      """
output:     import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      from nltk.stem import WordNetLemmatizer
      import string
  
      # Load the dataset
      df = pd.read_csv(filename)
  
      # Preprocess the text data
      def preprocess_text(text):
          text = text.lower()
          text = ''.join([char for char in text if char not in string.punctuation])
          tokens = word_tokenize(text)
          tokens = [word for word in tokens if word not in stopwords.words('english')]
          lemmatizer = WordNetLemmatizer()
          tokens = [lemmatizer.lemmatize(word) for word in tokens]
          return ' '.join(tokens)
  
      df['review'] = df['review'].apply(preprocess_text)
  
      # Generate features using CountVectorizer
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(df['review'])
      y = df['sentiment']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train a Multinomial Naive Bayes model
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Predict the sentiment on the test set
      y_pred = model.predict(X_test)
  
      # Generate the classification report
      report = classification_report(y_test, y_pred, output_dict=False)
  
      return report