import re
import json

gender_lexicons = json.load(open("config/gender_lexicons.json", "r"))


def count_gender_terms(text, gender_terms):
    pattern = r"\b({})\b".format("|".join(gender_terms))
    matches = re.findall(pattern, str(text))
    return len(matches)


def get_gender_tag(count_m_term, count_f_term):
    total_terms = count_m_term + count_f_term
    if total_terms == 0:
        return "No Gender"

    m_proportion = (count_m_term / total_terms) * 100
    if m_proportion >= 75:
        return "Male Strongly Positive Gender"
    elif m_proportion >= 50:
        return "Male Positive Gender"

    f_proportion = (count_f_term / total_terms) * 100
    if f_proportion >= 75:
        return "Female Strongly Positive Gender"
    elif f_proportion >= 50:
        return "Female Positive Gender"

    return "Equal Gender"


def get_pg_spg(sample_df):
    gender_labels = [
        "Gender",
        "No Gender",
        "Equal Gender",
        "Female Positive Gender",
        "Male Positive Gender",
        "Female Strongly Positive Gender",
        "Male Strongly Positive Gender",
    ]

    gender_counts = sample_df["gender_cat"].value_counts()
    result = {label: str(gender_counts.get(label, 0)) for label in gender_labels}

    return result


def eval_gender_divide(data):
    male_terms = gender_lexicons.get("male_lexicons")
    female_terms = gender_lexicons.get("female_lexicons")

    data[data.columns[0]] = data[data.columns[0]].str.lower().str.strip()

    data["count_male_term"] = data.apply(
        lambda x: count_gender_terms(x[data.columns[0]], male_terms), axis=1
    )
    data["count_female_term"] = data.apply(
        lambda x: count_gender_terms(x[:], female_terms), axis=1
    )

    data["gender_cat"] = data.apply(
        lambda row: get_gender_tag(row["count_male_term"], row["count_female_term"]),
        axis=1,
    )

    collection = get_pg_spg(data)
    return collection