#!/usr/bin/env python3
from nlptools import *
from nlptools.tools import *
from nlptools.resources import *

import os
import time
import logging
import sys
import json
import plac

# exemple :
# sed -e '1d; $d' ../data/data2.json | sed  's/\,$/ /g' |  python3 analyze.py stemmer -o doc -ini conf_EZ.ini  -log
# analyze.log

@plac.annotations(
    analyzer=(
            "Name oh the analyzer [default stemmer porter]",
            "positional",
            None,
            str,
            ["stemmer", "termMatcher", "ner", "NPchunker", "POStagger", "gazetteer","NPchunkerDP"],
    ),
    output=("Format result ", "option", "o", str, ["doc", "json"]),
    init_file=("initialisation file [default config.ini]", "option", "ini", str),
    log=("log file", "option", "log", str)
)


def main( init_file, log, analyzer="stemmer", output="doc"):

    field = 1  # indique le nombre de champs tsv/csv/txt des fichiers du corpus
    compteur = 0
    pipe = full_run(analyzer, init_file, output )   # doc = corpus traité sur la sortie standard


    logging.basicConfig(filename=log, level=logging.DEBUG)
    t1 = time.time()

    # boucle de traitement sur le champ "text" de chaque document
    # label et keywords sont extraits, puis replacés
    for json_line in sys.stdin:
        compteur += 1
        try:
            data = json.loads(json_line)
        except json.decoder.JSONDecodeError:
            logging.error("Input format problem line :{} : String could not be converted to JSON".format(compteur))
            exit(1)

        #print("in".format(compteur))
        data["value"] = pipe.pipe_analyse(data["value"])
        #print("ou".format(compteur))
        sys.stdout.write(json.dumps(data))
        sys.stdout.write("\n")

    # calcul du temps execution
    t2 = time.time()
    logging.info("TRACE::Executing times %.3f " % (t2 - t1))

# class runner
class full_run:

    def __init__(self,  analyzer, init_file, output):

        self.location = os.path.realpath(
            os.path.join(os.getcwd(), os.path.dirname("doc"))
        )
        self.parsers = [
            exec_spacy_pipe(analyzer, init_file, output),
        ]

    def pipe_analyse(self, text):

        # execution du pipe
        for parser in self.parsers:
            text = parser(text)
        return text


if __name__ == "__main__":

    if False:
        import cProfile
        import pstats

        cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
        s = pstats.Stats("Profile.prof")
        s.strip_dirs().sort_stats("time").print_stats()
    else:
        plac.call(main)