import pickle
from sklearn.tree import DecisionTreeClassifier


def train_model(pkl_path, labels, samples):
    # Train a decision tree classifier on the dataset
    clf = DecisionTreeClassifier()
    clf.fit(samples, labels)
    # Save the trained model to a file
    with open(pkl_path, "wb") as f:
        pickle.dump(clf, f)


def load(pkl_path):
    # Load the trained model from the pickle file
    with open(pkl_path, "rb") as f:
        clf = pickle.load(f)
    return clf


def predict(pkl_path, features):
    # Load the model
    model = load(pkl_path)
    # Convert the features into a feature vector
    feature_vector = [[features['comment_quality'],
                      features['code_identation'],
                      features['style_adherence'],
                      features['repetitive_patterns'],
                      features['code_complexity']]]
    # Use the trained model to make a prediction
    prediction = model.predict(feature_vector)
    # Return the prediction
    return bool(prediction[0])
