input: Write a function to generate a program in Python to evaluate the accuracy of a machine-learning model. The function should fit the model on training data, predict on test data, and calculate the accuracy using scikit-learn's accuracy_score function. The function should return the calculated accuracy as a string.
  
  assert evaluate_model_accuracy() == "Accuracy: 0.95"
  assert evaluate_model_accuracy() == "Accuracy: 0.88"
  assert evaluate_model_accuracy() == "Accuracy: 1.0"
output: def evaluate_model_accuracy():
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
      
      data = load_iris()
      X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.5, random_state=42)
      
      model = LogisticRegression()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      
      accuracy = accuracy_score(y_test, y_pred)
      return f"Accuracy: {accuracy:.2f}"