import pickle
from sklearn.feature_extraction.text import TfidfVectorizer

def load_model(model_path):
    with open(model_path, 'rb') as f:
        model_data = pickle.load(f)
    return model_data

def predict_answer(question, model_data):
    vectorizer = model_data['vectorizer']
    df = model_data['data']
    question_vector = vectorizer.transform([question])
    similarity_scores = np.dot(df, question_vector.T).toarray().flatten()
    best_match_index = np.argmax(similarity_scores)
    return df.iloc[best_match_index]['คำตอบ']

model_data = load_model('tfidf_model.pkl')
response = predict_answer("คำถามของผู้ใช้", model_data)
print(response)