input: Write a python function to perform text classification using MultinomialNB and CountVectorizer. 
  assert text_classification_accuracy('data.csv') >= 0.8
  assert text_classification_precision('data.csv') >= 0.75
  assert text_classification_recall('data.csv') >= 0.75
output: def text_classification_accuracy(file_path):
      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 accuracy_score
  
      data = pd.read_csv(file_path)
      X = data['text']
      y = data['label']
  
      vectorizer = CountVectorizer()
      X_vect = vectorizer.fit_transform(X)
  
      X_train, X_test, y_train, y_test = train_test_split(X_vect, y, test_size=0.2, random_state=42)
  
      model = MultinomialNB()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
  
      return accuracy_score(y_test, y_pred)
  
  def text_classification_precision(file_path):
      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 precision_score
  
      data = pd.read_csv(file_path)
      X = data['text']
      y = data['label']
  
      vectorizer = CountVectorizer()
      X_vect = vectorizer.fit_transform(X)
  
      X_train, X_test, y_train, y_test = train_test_split(X_vect, y, test_size=0.2, random_state=42)
  
      model = MultinomialNB()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
  
      return precision_score(y_test, y_pred)
  
  def text_classification_recall(file_path):
      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 recall_score
  
      data = pd.read_csv(file_path)
      X = data['text']
      y = data['label']
  
      vectorizer = CountVectorizer()
      X_vect = vectorizer.fit_transform(X)
  
      X_train, X_test, y_train, y_test = train_test_split(X_vect, y, test_size=0.2, random_state=42)
  
      model = MultinomialNB()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
  
      return recall_score(y_test, y_pred)