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 and convert acronyms
def remove_department_numbers_and_convert_acronyms(city_name) :
    # department numbers: Grenoble - 38 => Grenoble
    sep = " -"
    stripped = city_name.split(sep, 1)[0]
    stripped = stripped.replace("-"," ")

    # acronyms
    stripped = stripped.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_and_convert_acronyms).apply(remove_accents)
        
        if len(affiliations_dataframe[affiliations_dataframe["contains_acronyms"] == True]) == 0:
            continue

        # create new dataframe only with affiliations that contain acronyms
        dataframe_with_acronyms = affiliations_dataframe[affiliations_dataframe["contains_acronyms"] == True]
        dataframe_with_acronyms["city_in_affiliations"] = dataframe_with_acronyms["standardized_city"].apply(is_city_in_affiliation,affiliation=affiliation)
        
        if len(dataframe_with_acronyms[dataframe_with_acronyms["city_in_affiliations"] == True]) == 0:
            continue

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