from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer

def train_classifier(dummy_data):
    vectorizer = TfidfVectorizer()
    train_texts, train_labels = zip(*dummy_data)
    train_vectors = vectorizer.fit_transform(train_texts)
    classifier = LogisticRegression()
    classifier.fit(train_vectors, train_labels)
    return classifier, vectorizer

def classify_text(text: str, classifier, vectorizer) -> str:
    try:
        transformed_data = vectorizer.transform([text])
        category = classifier.predict(transformed_data)[0]
        return category
    except Exception as e:
        return str(e)