appStore/tfidf_extraction.py

import re
import langdetect
from stopwordsiso import stopwords, has_lang
from sklearn.feature_extraction.text import TfidfVectorizer

def detect_language(text: str) -> str:
    try:
        return langdetect.detect(text)
    except:
        return "en"  # fallback if detection fails

def get_stopwords_for_language(lang_code: str):
    lang_code = lang_code.lower()
    if has_lang(lang_code):
        return stopwords(lang_code)  # returns a *set* of stopwords
    else:
        return set()

def extract_top_keywords(text: str, top_n: int = 5) -> list[str]:
    cleaned_text = re.sub(r"[^\w\s]", " ", text.lower())

    lang_code = detect_language(cleaned_text)
    language_stopwords = get_stopwords_for_language(lang_code)

    # Convert the set to a list here!
    vectorizer = TfidfVectorizer(stop_words=list(language_stopwords))

    tfidf_matrix = vectorizer.fit_transform([cleaned_text])
    feature_names = vectorizer.get_feature_names_out()
    scores = tfidf_matrix.toarray()[0]

    # Pair up (word, score), then sort descending
    word_score_pairs = list(zip(feature_names, scores))
    word_score_pairs.sort(key=lambda x: x[1], reverse=True)

    return [w for (w, _) in word_score_pairs[:top_n]]