#!/usr/bin/env python3

# flake8: noqa E501 (line length)

import pandas as pd
from fuzzywuzzy import fuzz
import sys
import json
import unicodedata
import re
import config
pd.options.mode.chained_assignment = None


# Check if an element of the list is in affiliation
def is_hospital_affiliation(affiliation):
    affiliation_lower = affiliation.lower()
    for aff in config.acronyms:
        if aff in affiliation_lower:
            return True
    return False


# correction of the cities : remove the department numbers
def remove_department_numbers(city_name):
    # Grenoble - 38 => Grenoble
    sep = " -"
    stripped = city_name.split(sep, 1)[0]
    stripped = stripped.replace("-", " ")

    return stripped


# correction of the cities: convert acronyms
def convert_acronyms(city_name):
    stripped = city_name.replace("Saint", "St")
    stripped = stripped.replace("Mont", "Mt")

    return stripped


# correction of the cities : remove accents
def remove_accents(city_name):
    normalized_text = unicodedata.normalize("NFD", city_name)
    text_with_no_accent = re.sub("[\u0300-\u036f]", "", normalized_text)
    return text_with_no_accent


def is_city_in_affiliation(city, affiliation):
    return city.lower() in affiliation.lower()


def affiliations_match_ratio(first_affiliation, second_affiliation):
    return fuzz.ratio(first_affiliation, second_affiliation)


def get_corresponding_hospital_from_affiliation(affiliation):
    affiliations_dataframe = pd.read_csv("hospital_affiliations.csv", sep=";")
    acronyms = config.acronyms

    hospital = "N.C"
    affiliation = affiliation.lower()
    for acronym in acronyms:
        if acronym not in affiliation:
            continue

        # standarize original dataframe
        affiliations_dataframe["contains_acronyms"] = affiliations_dataframe["Affiliation"].apply(is_hospital_affiliation)
        affiliations_dataframe["standardized_city"] = affiliations_dataframe["Ville_canonique_Dpt"].apply(remove_department_numbers).apply(convert_acronyms).apply(remove_accents)

        acronyms_dataframe = affiliations_dataframe[affiliations_dataframe["contains_acronyms"] == True]  # noqa: E712
        if len(acronyms_dataframe) == 0:  # noqa: E712
            continue

        # create new dataframe only with affiliations that contain acronyms
        acronyms_dataframe["city_in_affiliations"] = acronyms_dataframe["standardized_city"].apply(is_city_in_affiliation, affiliation=affiliation)

        ancronyms_cities_dataframe = acronyms_dataframe[acronyms_dataframe["city_in_affiliations"] == True]  # noqa: E712
        if len(ancronyms_cities_dataframe) == 0:
            continue

        # create new dataframe only with affiliations that contain acronyms and cities
        ancronyms_cities_dataframe["ratio"] = ancronyms_cities_dataframe["Affiliation"].apply(affiliations_match_ratio, second_affiliation=affiliation)
        hospital = ancronyms_cities_dataframe["Orga NonCnrs Acorriger"][ancronyms_cities_dataframe["ratio"].idxmax()]
        break

    return hospital


def main():
    for line in sys.stdin:
        data = json.loads(line)
        texte = data["value"]
        data["value"] = get_corresponding_hospital_from_affiliation(texte)
        sys.stdout.write(json.dumps(data))
        sys.stdout.write("\n")


if __name__ == "__main__":
    main()