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

"""

Created on Fri Sep 20 13:15:58 2019

@author: KS5046082

"""

# https://machinelearningmastery.com/encoder-decoder-models-text-summarization-keras/

# https://machinelearningmastery.com/timedistributed-layer-for-long-short-term-memory-networks-in-python/

from sklearn.preprocessing import MinMaxScaler

from keras.models import Sequential,Input,Model

from keras.layers import Dense,Dropout,LSTM,concatenate,Embedding

from keras.preprocessing.text import Tokenizer

from keras.preprocessing.text import text_to_word_sequence

from keras.preprocessing.text import one_hot

import re

import math

# define the document

path_of_file = "D:/extracted_data_file.txt"

txt_file = open(path_of_file,"r")

text = txt_file.readlines()

txt_file.close()

print(type(text))

text_str = "".join(text)

# estimate the size of the vocabulary

words = set(text_to_word_sequence(text_str))

vocab_size = len(words)

print("vocab_size = ",vocab_size)

# using regex (findall())

# to count words in string

res = len(re.findall(r'\w+', text_str))

# printing result

print ("The number of words in string are : " + str(res))

# integer encode the document

result = one_hot(text_str, round(vocab_size*1.3))

#print(result)

tokenizer = Tokenizer(num_words = 3000 )

src_txt_length = len(text_str)

sum_txt_length = math.ceil(src_txt_length*0.3)

print("sum_txt_length = ",sum_txt_length)

print("src_txt_length = " ,src_txt_length)

# source text input model

inputs1 = Input(shape=(src_txt_length,))

am1 = Embedding(vocab_size, 128)(inputs1)

am2 = LSTM(128)(am1)

# summary input model

inputs2 = Input(shape=(sum_txt_length,))

sm1 = Embedding(vocab_size, 128)(inputs2)

sm2 = LSTM(128)(sm1)

# decoder output model

decoder1 = concatenate([am2, sm2])

outputs = Dense(vocab_size, activation='softmax')(decoder1)

# tie it together [article, summary] [word]

model = Model(inputs=[inputs1, inputs2], outputs=outputs)

model.compile(loss='categorical_crossentropy', optimizer='adam')

#model.compile(loss='mean_squared_error', optimizer='adam')

print(model.summary())