Remove input length limit

# -*- coding: utf-8 -*-

from keras.models import load_model

from pykospacing.embedding_maker import load_vocab, encoding_and_padding

import numpy as np

import os

import pkg_resources

import re

import warnings

import numpy as np

import pkg_resources

from keras.models import load_model

from pykospacing.embedding_maker import encoding_and_padding, load_vocab

__all__ = ['spacing', ]

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

model_path = pkg_resources.resource_filename('pykospacing', os.path.join('resources', 'models', 'kospacing'))

dic_path = pkg_resources.resource_filename('pykospacing', os.path.join('resources', 'dicts', 'c2v.dic'))

model = load_model(model_path)

model.make_predict_function()

w2idx, _ = load_vocab(dic_path)

model_path = pkg_resources.resource_filename(

    'pykospacing', os.path.join('resources', 'models', 'kospacing'))

dic_path = pkg_resources.resource_filename(

    'pykospacing', os.path.join('resources', 'dicts', 'c2v.dic'))

MODEL = load_model(model_path)

MODEL.make_predict_function()

W2IDX, _ = load_vocab(dic_path)

class pred_spacing:

class PredSpacing:

    """predict spacing for input string

    """

    def __init__(self, model, w2idx):

        self.model = model

        self.w2idx = w2idx

        self._model = model

        self._w2idx = w2idx

        self.pattern = re.compile(r'\s+')

    def get_spaced_sent(self, raw_sent):

        raw_sent_ = "«" + raw_sent + "»"

        raw_sent_ = raw_sent_.replace(' ', '^')

        sents_in = [raw_sent_, ]

        mat_in = encoding_and_padding(word2idx_dic=self.w2idx, sequences=sents_in, maxlen=200, padding='post', truncating='post')

        results = self.model.predict(mat_in)

        mat_in = encoding_and_padding(

            word2idx_dic=self._w2idx, sequences=sents_in, maxlen=200,

            padding='post', truncating='post')

        results = self._model.predict(mat_in)

        mat_set = results[0, ]

        preds = np.array(['1' if i > 0.5 else '0' for i in mat_set[:len(raw_sent_)]])

        preds = np.array(

            ['1' if i > 0.5 else '0' for i in mat_set[:len(raw_sent_)]])

        return self.make_pred_sents(raw_sent_, preds)

    def make_pred_sents(self, x_sents, y_pred):

        return subs

pred_spacing = pred_spacing(model, w2idx)

PredSpacing = PredSpacing(MODEL, W2IDX)

MAX_LEN = 198

def spacing(sent):

    if len(sent) > 198:

        warnings.warn('One sentence can not contain more than 198 characters. : {}'.format(sent))

    spaced_sent = pred_spacing.get_spaced_sent(sent)

    return(spaced_sent.strip())

    if len(sent) > MAX_LEN:

        splitted_sent = [sent[y-MAX_LEN:y] for y in range(MAX_LEN, len(sent)+MAX_LEN, MAX_LEN)]

        spaced_sent = ''.join([PredSpacing.get_spaced_sent(ss)

                               for ss in splitted_sent])

    else:

        spaced_sent = PredSpacing.get_spaced_sent(sent)

    return spaced_sent.strip()