input: Write a function to create a machine learning program in Python that will classify text data into 3 different categories using a Naive Bayes model. The function should load the data from a CSV file, vectorize the text data using CountVectorizer, convert the target labels into numerical values, split the data into training and testing sets, train the Naive Bayes model, and print out the classification report.
  
  
  import pandas as pd
  import numpy as np
  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
  
  def text_classification_model(csv_file_path):
      # Load the data
      data = pd.read_csv(csv_file_path)
  
      # Vectorize the text
      cv = CountVectorizer()
      X = cv.fit_transform(data["text"])
  
      # Convert target labels to numerical values
      y = np.array([1 if label == "category1" else 2 if label == "category2" else 3 for label in data["category"]])
  
      # Split into train and test data
      X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
  
      # Train the model
      nb = MultinomialNB()
      nb.fit(X_train, y_train)
  
      # Test the model
      y_pred = nb.predict(X_test)
  
      # Print out the results
      return classification_report(y_test, y_pred)
  
  
  assert text_classification_model("data.csv").startswith("              precision    recall  f1-score   support")
  assert text_classification_model("nonexistent.csv")
output: def text_classification_model(csv_file_path):
      try:
          data = pd.read_csv(csv_file_path)
      except FileNotFoundError:
          return "File not found"
  
      cv = CountVectorizer()
      X = cv.fit_transform(data["text"])
      y = np.array([1 if label == "category1" else 2 if label == "category2" else 3 for label in data["category"]])
      X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
      nb = MultinomialNB()
      nb.fit(X_train, y_train)
      y_pred = nb.predict(X_test)
      return classification_report(y_test, y_pred)