import pandas as pd
import sparql
import warnings
from wikidata.client import Client  # https://github.com/dahlia/wikidata
import tqdm
import json
import time


def testing_dbpedia(author):
    endpoint = 'http://dbpedia.org/sparql'
    s = sparql.Service(endpoint, "utf-8", "SELECT")
    query_author = """SELECT ?names WHERE {{
                <{}> owl:sameAs ?names .
                }}""".format(author)

    query_works = """SELECT ?works WHERE {{
                ?works dbo:author <{}>
                }}""".format(author)

    result = s.query(query_author)
    results_works = s.query(query_works)
    return [result, results_works]


def testing_wikidata(entity_q):
    """
    Notable work = P800
    Date of birth = P569
    Present in work = P1441
    """
    # DEBUGGING_ENTITY = 'Q1398'
    # entity_q = DEBUGGING_ENTITY

    dict_works = {}
    dict_present_in_works = {}

    client = Client()
    entity = client.get(entity_q, load=True)
    notable_work = client.get('P800')
    present_in_work = client.get('P1441')
    # date_of_birth = client.get('P569')
    # birth = entity.get(date_of_birth)   # TODO: debug this
    aut_names = entity.label.texts
    _works = entity.get(notable_work)
    _present_in_work = entity.get(present_in_work)
    if _works is not None:
        for work in _works:
            dict_works[work.id] = work.label.texts
    if _present_in_work is not None:
        for p_work in _present_in_work:
            dict_present_in_works[p_work.id] = p_work.label.texts
    return entity, aut_names, dict_works, dict_present_in_works


def print_results(results):
    result, results_works = results[0], results[1]
    print('# NAMES:')
    for row in result:
        values = sparql.unpack_row(row)
        print(values[0])

    print('# AUTHOR OF:')
    for row in results_works:
        values = sparql.unpack_row(row)
        print(values[0])


def extract_wikidata_endpoint(author_names, show_warnings=True):
    r = [sparql.unpack_row(name)[0] for name in author_names if 'wikidata' in sparql.unpack_row(name)[0]]
    try:
        endpoint = r[0].split('/')[-1]
        return endpoint
    except IndexError:
        if show_warnings:
            warnings.warn('Entity has not a wikimdata endpoint ')
        return None


# PSEUDO-MAIN ------------------------------------------------------
stime = time.time()
convivio_df = pd.read_csv('../commentaries/data_parsed/convivio_DF.csv')
monarchia_df = pd.read_csv('../commentaries/data_parsed/monarchia_DF.csv')
rime_df = pd.read_csv('../commentaries/data_parsed/rime_DF.csv')
author_uri_list_convivio = list(set(convivio_df['author_uri'].dropna()))
author_uri_list_monarchia = list(set(monarchia_df['author_uri'].values))
author_uri_list_rime = list(set(rime_df['author_uri'].values))

full_auth_list = author_uri_list_convivio + author_uri_list_monarchia + author_uri_list_rime
dict_res = {}
print(f'# Number of authors: {len(full_auth_list)}')
for auth in tqdm.tqdm(full_auth_list):
    entity_q = testing_dbpedia(auth)[0]
    wikidata_endp = extract_wikidata_endpoint(entity_q, show_warnings=False)
    dict_res[wikidata_endp] = None
    if wikidata_endp is not None:
        _, names, works, other_works = testing_wikidata(wikidata_endp)
        dict_res[wikidata_endp] = {'aut_name': names,
                                   'aut_works': works,
                                   'aut_present_work': other_works}

with open('knowledge_base/KB_wikimedia.json', 'w+') as f:
    json.dump(dict_res, f)

print(f'# Process finished in: {round((time.time()-stime), 5)}')