#!/usr/bin/python3
# -*- coding: utf-8 -*-

import json
import requests
import sys

BUFFER_SIZE = 500

def lemmatize_sentences(language_code, sentences):
    data = {
        'lemmatize': True,
        'language':language_code,
        'sentences':sentences
    }
    response = requests.post(url = 'http://127.0.0.1:10002/preprocess', json = data) 
    response_json = json.loads(response.text)

    result = {'normalized':[], 'lemmatized':[]}
    for processed_sentence in response_json['processed_sentences']:
        result['normalized'].append(processed_sentence['normalized'])
        result['lemmatized'].append(processed_sentence['tokens'])
    return result

def write_result(result, norm_file, lem_file):
    for s in result['normalized']:
        norm_file.write(s+'\n')
    for s in result['lemmatized']:
        lem_file.write(s+'\n')


file_name = sys.argv[1]
language_code = sys.argv[2]
norm_output_name = sys.argv[3]
lem_output_name = sys.argv[4]

sentences_buffer = []
with open(file_name) as in_file, open(norm_output_name, 'w') as out_norm, open(lem_output_name, 'w') as out_lem:    
    for line in in_file:
        sentences_buffer.append(line.rstrip())
        if len(sentences_buffer) == BUFFER_SIZE:
            write_result(lemmatize_sentences(language_code,sentences_buffer), out_norm, out_lem)
            sentences_buffer = []

    if len(sentences_buffer) > 0:
        write_result(lemmatize_sentences(language_code,sentences_buffer), out_norm, out_lem)