import re
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

nltk.download('punkt')
nltk.download('punkt_tab')
nltk.download('stopwords')
nltk.download('wordnet')

stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()

def preprocess_text(text: str) -> str:
    """Clean and preprocess input text."""
    text = text.lower()
    text = re.sub(r'[^a-zA-Z0-9\s]', ' ', text)
    tokens = nltk.word_tokenize(text)
    tokens = [word for word in tokens if word not in stop_words and word not in string.punctuation]
    tokens = [lemmatizer.lemmatize(word) for word in tokens]
    return " ".join(tokens)