"""

    AUTHOR:

        Khushal P Soonderji

    DATE:

        Saturday, 18th May, 2024

    OBJECTIVE:

        To provide a quick set of functions to work with fuzzy logic.

    REFERENCES:

        1) https://www.w3schools.com/python/python_json.asp

    DOWNLOADS:

        N/A

"""

#   *****************************************************************************************************************
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# ***                                                   IMPORT                                                      ***
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# To make sibling directories accessible for imports:
import sys
sys.path.append(".")
sys.path.append("..")

# To apply fuzzy logic:
from thefuzz import fuzz, process

# To work with tabulated data:
import pandas as pd


#   *****************************************************************************************************************
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# ***                                           MACROS / ONE-TIME INIT                                              ***
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# --- Nothing Yet


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# ***                                                VARIABLES                                                      ***
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# --- Nothing Yet


#   *****************************************************************************************************************
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# ***                                                  FUNCTIONS                                                    ***
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def get_best_match(
        target,
        choices,
        threshold = 0.70,
        partial = False
):

    if partial: scorer = fuzz.partial_token_sort_ratio
    else: scorer = fuzz.ratio

    result = process.extractOne(
        target,
        choices,
        score_cutoff = threshold * 100,
        scorer = scorer
    )

    try: return result[0]
    except: return None


# ---------------------------------------------------------------------------------------------------------------------


def rank(target, choices, partial = True):

    if partial: scorer = fuzz.partial_token_sort_ratio
    else: scorer = fuzz.ratio

    result = process.extract(
        target,
        choices,
        limit = len(choices),
        scorer = scorer
    )

    result = pd.DataFrame(result, columns = ["choice", "closeness"])
    result["closeness"] = result["closeness"] / 100.0

    return result


# ---------------------------------------------------------------------------------------------------------------------


def match(targets, choices, threshold = 0.7, partial = False, allow_null = False):

    all_matches_df = None
    all_matches = {target: None for target in targets}
    something_is_null = False

    for target in targets:
        match_df = rank(target, choices, partial = partial)
        match_df["target"] = target
        if all_matches_df is None: all_matches_df = match_df
        else: all_matches_df = pd.concat([all_matches_df, match_df])

    all_matches_df = all_matches_df.sort_values(by = ["closeness"], ascending = False).reset_index(drop = True)

    for target in targets:
        target_df = all_matches_df[all_matches_df["target"] == target].reset_index(drop = True)
        if target_df.empty: continue
        if target_df.at[0, "closeness"] >= threshold:
            choice = target_df.at[0, "choice"]
            all_matches[target] = choice
            all_matches_df = all_matches_df[all_matches_df["choice"] != choice]
        else:
            all_matches[target] = None
            something_is_null = True

    # print(all_matches)
    if something_is_null and not allow_null: return None
    else: return all_matches


#   *****************************************************************************************************************
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# ***                                               MAIN PROGRAM                                                    ***
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#   *****************************************************************************************************************


if __name__ == "__main__":

    import async_json_utils

    awb_numbers = [
        "SF1111BIC",
        "SF2222BIC",
        "SF3333BIC",
        "SF4444BIC",
    ]

    chat_text = "SF1112BIC"

    # print(chat_text == names[0])
    best_match = get_best_match(chat_text, awb_numbers, threshold = 0.60, partial = False)
    print(f"Best match for '{chat_text}' is '{best_match}'")