# modules/preprocessing.py

import re
import nltk

# Download stopwords if not already present
nltk.download("stopwords", quiet=True)
from nltk.corpus import stopwords

STOPWORDS = set(stopwords.words("english"))

def clean_text(text: str) -> str:
    """
    Basic text cleaning:
    - Lowercase
    - Remove URLs, mentions, hashtags
    - Remove numbers, punctuation, and stopwords
    """
    text = text.lower()

    # Remove URLs
    text = re.sub(r"http\S+|www\S+|https\S+", "", text)

    # Remove mentions and hashtags
    text = re.sub(r"@\w+|#\w+", "", text)

    # Remove numbers and special characters
    text = re.sub(r"[^a-z\s]", "", text)

    # Remove stopwords
    tokens = [word for word in text.split() if word not in STOPWORDS]

    return " ".join(tokens)

def preprocess_texts(texts: list) -> list:
    """
    Clean and preprocess a list of texts.
    """
    return [clean_text(t) for t in texts if t.strip()]