# keyword_extractor.py (using KeyBERT, no OpenAI required)

from keybert import KeyBERT

# Initialize KeyBERT with a small, efficient model
kw_model = KeyBERT('all-MiniLM-L6-v2')

def extract_keywords(articles, num_keywords=10):
    """
    articles: list of dicts, each with 'title' and 'content'
    returns: list of unique keywords/phrases (strings)
    """
    all_text = " ".join([
        art.get("title", "") + " " + art.get("content", "")
        for art in articles if art
    ])
    # Extract top keywords and phrases
    keywords = kw_model.extract_keywords(
        all_text,
        keyphrase_ngram_range=(1, 2),
        stop_words='english',
        top_n=num_keywords
    )
    # keywords is a list of tuples: [(keyword, score), ...]
    keywords = [kw for kw, score in keywords]
    return keywords