input: Write a python function to train a LinearSVC model on text data using sklearn and evaluate its accuracy.
  assert train_and_evaluate_model('data.csv') > 0.7
output: def train_and_evaluate_model(file_path):
      import pandas as pd
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.svm import LinearSVC
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      # Load the data
      data = pd.read_csv(file_path)
  
      # Assuming the csv has two columns: 'text' and 'label'
      texts = data['text']
      labels = data['label']
  
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(texts, labels, test_size=0.2, random_state=42)
  
      # Vectorize the text data
      vectorizer = TfidfVectorizer()
      X_train_tfidf = vectorizer.fit_transform(X_train)
      X_test_tfidf = vectorizer.transform(X_test)
  
      # Train the LinearSVC model
      model = LinearSVC()
      model.fit(X_train_tfidf, y_train)
  
      # Predict and evaluate
      y_pred = model.predict(X_test_tfidf)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy