translation.py

# -*- coding: utf-8 -*-
from flask import Flask, render_template, jsonify, redirect, url_for, request


# Import TensorFlow >= 1.10 and enable eager execution
import tensorflow as tf
tf.enable_eager_execution()
from keras.models import load_model
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

import unicodedata
import re
import numpy as np
import os
import time
from fpdf import FPDF
import pysubs2
from flask import send_file

app = Flask(__name__)
app.config.from_object(__name__)


#define our global variable
enterdText=None
model = None
graph = None

"""
def load_model_():
    #load our pretrianed model 
	global model
	model =load_model('elmira.h5')
	global graph
	graph = tf.get_default_graph()
"""


@app.route('/')
def index():
    return render_template('home.html')

path_to_file='fra.txt'
# Converts the unicode file to ascii
def unicode_to_ascii(s):
    return ''.join(c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn')


def preprocess_sentence(w):
    w = unicode_to_ascii(w.lower().strip())
    
    # creating a space between a word and the punctuation following it
    # eg: "he is a boy." => "he is a boy ." 
    # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation
    w = re.sub(r"([?.!,¿])", r" \1 ", w)
    w = re.sub(r'[" "]+', " ", w)
    
    # replacing everything with space except (a-z, A-Z, ".", "?", "!", ",")
    w = re.sub(r"[^a-zA-Z?.!,¿]+", " ", w)
    
    w = w.rstrip().strip()
    
    # adding a start and an end token to the sentence
    # so that the model know when to start and stop predicting.
    w = '<start> ' + w + ' <end>'
    return w

# 1. Remove the accents
# 2. Clean the sentences
# 3. Return word pairs in the format: [ENGLISH, SPANISH]
def create_dataset(path, num_examples):
    lines = open(path, encoding='utf-8' ).read().strip().split('\n')
    
    word_pairs = [[preprocess_sentence(w) for w in l.split('\t')]  for l in lines[:num_examples]]
    
    return word_pairs

# This class creates a word -> index mapping (e.g,. "dad" -> 5) and vice-versa 
# (e.g., 5 -> "dad") for each language,
class LanguageIndex():
  def __init__(self, lang):
    self.lang = lang
    self.word2idx = {}
    self.idx2word = {}
    self.vocab = set()
    
    self.create_index()
    
  def create_index(self):
    for phrase in self.lang:
      self.vocab.update(phrase.split(' '))
    
    self.vocab = sorted(self.vocab)
    
    self.word2idx['<pad>'] = 0
    for index, word in enumerate(self.vocab):
      self.word2idx[word] = index + 1
    
    for word, index in self.word2idx.items():
      self.idx2word[index] = word

def max_length(tensor):
    return max(len(t) for t in tensor)


def load_dataset(path, num_examples):
    # creating cleaned input, output pairs
    pairs = create_dataset(path, num_examples)

    # index language using the class defined above    
    inp_lang = LanguageIndex(sp for en, sp in pairs)
    targ_lang = LanguageIndex(en for en, sp in pairs)
    
    # Vectorize the input and target languages
    
    # Spanish sentences
    input_tensor = [[inp_lang.word2idx[s] for s in sp.split(' ')] for en, sp in pairs]
    
    # English sentences
    target_tensor = [[targ_lang.word2idx[s] for s in en.split(' ')] for en, sp in pairs]
    
    # Calculate max_length of input and output tensor
    # Here, we'll set those to the longest sentence in the dataset
    max_length_inp, max_length_tar = max_length(input_tensor), max_length(target_tensor)
    
    # Padding the input and output tensor to the maximum length
    input_tensor = tf.keras.preprocessing.sequence.pad_sequences(input_tensor, 
                                                                 maxlen=max_length_inp,
                                                                 padding='post')
    
    target_tensor = tf.keras.preprocessing.sequence.pad_sequences(target_tensor, 
                                                                  maxlen=max_length_tar, 
                                                                  padding='post')
    
    return input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_tar

# Try experimenting with the size of that dataset
num_examples =31000
input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_targ = load_dataset(path_to_file, num_examples)

# Creating training and validation sets using an 80-20 split
input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)

# Show length
len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val)

BUFFER_SIZE = len(input_tensor_train)
BATCH_SIZE = 64
N_BATCH = BUFFER_SIZE//BATCH_SIZE
embedding_dim = 256
units = 1024
vocab_inp_size = len(inp_lang.word2idx)
vocab_tar_size = len(targ_lang.word2idx)

dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)
dataset = dataset.batch(BATCH_SIZE)

def gru(units):
  # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)
  # the code automatically does that.
  if tf.test.is_gpu_available():
    return tf.keras.layers.CuDNNGRU(units, 
                                    return_sequences=True, 
                                    return_state=True, 
                                    recurrent_initializer='glorot_uniform')
  else:
    return tf.keras.layers.GRU(units, 
                               return_sequences=True, 
                               return_state=True, 
                               recurrent_activation='sigmoid', 
                               recurrent_initializer='glorot_uniform')

class Encoder(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):
        super(Encoder, self).__init__()
        self.batch_sz = batch_sz
        self.enc_units = enc_units
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        self.gru = gru(self.enc_units)
        
    def call(self, x, hidden):
        x = self.embedding(x)
        output, state = self.gru(x, initial_state = hidden)        
        return output, state
    
    def initialize_hidden_state(self):
        return tf.zeros((self.batch_sz, self.enc_units))

class Decoder(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):
        super(Decoder, self).__init__()
        self.batch_sz = batch_sz
        self.dec_units = dec_units
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        self.gru = gru(self.dec_units)
        self.fc = tf.keras.layers.Dense(vocab_size)
        
        # used for attention
        self.W1 = tf.keras.layers.Dense(self.dec_units)
        self.W2 = tf.keras.layers.Dense(self.dec_units)
        self.V = tf.keras.layers.Dense(1)
        
    def call(self, x, hidden, enc_output):
        # enc_output shape == (batch_size, max_length, hidden_size)
        
        # hidden shape == (batch_size, hidden size)
        # hidden_with_time_axis shape == (batch_size, 1, hidden size)
        # we are doing this to perform addition to calculate the score
        hidden_with_time_axis = tf.expand_dims(hidden, 1)
        
        # score shape == (batch_size, max_length, 1)
        # we get 1 at the last axis because we are applying tanh(FC(EO) + FC(H)) to self.V
        score = self.V(tf.nn.tanh(self.W1(enc_output) + self.W2(hidden_with_time_axis)))
        
        # attention_weights shape == (batch_size, max_length, 1)
        attention_weights = tf.nn.softmax(score, axis=1)
        
        # context_vector shape after sum == (batch_size, hidden_size)
        context_vector = attention_weights * enc_output
        context_vector = tf.reduce_sum(context_vector, axis=1)
        
        # x shape after passing through embedding == (batch_size, 1, embedding_dim)
        x = self.embedding(x)
        
        # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)
        x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)
        
        # passing the concatenated vector to the GRU
        output, state = self.gru(x)
        
        # output shape == (batch_size * 1, hidden_size)
        output = tf.reshape(output, (-1, output.shape[2]))
        
        # output shape == (batch_size * 1, vocab)
        x = self.fc(output)
        
        return x, state, attention_weights
        
    def initialize_hidden_state(self):
        return tf.zeros((self.batch_sz, self.dec_units))

encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)
decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)



optimizer = tf.train.AdamOptimizer()


def loss_function(real, pred):
  mask = 1 - np.equal(real, 0)
  loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask
  return tf.reduce_mean(loss_)

from keras.models import Model

checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
                                 encoder=encoder,
                                 decoder=decoder)


def evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):
    attention_plot = np.zeros((max_length_targ, max_length_inp))
    
    sentence = preprocess_sentence(sentence)

    inputs = [inp_lang.word2idx[i] for i in sentence.split(' ')]
    inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=max_length_inp, padding='post')
    inputs = tf.convert_to_tensor(inputs)
    
    result = ''

    hidden = [tf.zeros((1, units))]
    enc_out, enc_hidden = encoder(inputs, hidden)

    dec_hidden = enc_hidden
    dec_input = tf.expand_dims([targ_lang.word2idx['<start>']], 0)

    for t in range(max_length_targ):
        predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out)
        
        # storing the attention weigths to plot later on
        attention_weights = tf.reshape(attention_weights, (-1, ))
        attention_plot[t] = attention_weights.numpy()

        predicted_id = tf.argmax(predictions[0]).numpy()

        result += targ_lang.idx2word[predicted_id] + ' '

        if targ_lang.idx2word[predicted_id] == '<end>':
            return result, sentence, attention_plot
        
        # the predicted ID is fed back into the model
        dec_input = tf.expand_dims([predicted_id], 0)

    return result, sentence, attention_plot

# function for plotting the attention weights
def plot_attention(attention, sentence, predicted_sentence):
    fig = plt.figure(figsize=(10,10))
    ax = fig.add_subplot(1, 1, 1)
    ax.matshow(attention, cmap='viridis')
    
    fontdict = {'fontsize': 14}
    
    ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)
    ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)

    plt.show()

def translate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):
    result, sentence, attention_plot = evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)
        
   # print('Input: {}'.format(sentence))
    #print('Predicted translation: {}'.format(result))
    tra = ' {}'.format(result)
    return tra
    

@app.route('/submit', methods=['POST'])
def submit():

    enterdText=request.form['text']

    #return 'You entered: {}'.format(request.form['text'])
    if request.method == 'POST':
        result = request.form['text']
        trans=translate(u''.join(result), encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ) 
        #manage output files
        #pdf
        pdf = FPDF()
        pdf.add_page()
        pdf.set_xy(0, 0)
        pdf.set_font('arial', 'B', 13.0)
        pdf.cell(ln=0, h=5.0, align='L', w=0, txt=trans, border=0)
        pdf.output('outputFiles/pdf/output.pdf', 'F')
        #text
        text_file = open("outputFiles/text/Output.txt", "w")
        text_file.write(trans)
        text_file.close()
        #srt
        with open("outputFiles/srt/subtitles.srt", "w") as fp:
            fp.write(trans)

        return render_template("submit.html",value = trans ,secvalue=result)

@app.route('/return-files/output.pdf')
def return_files_tut():
	try:
		return send_file('outputFiles/pdf/output.pdf', attachment_filename='output.pdf')
	except Exception as e:
		return str(e)

@app.route('/return-text/Output.txt')
def return_files_tut_():
	try:
		return send_file('outputFiles/text/Output.txt', attachment_filename='Output.txt')
	except Exception as e:
		return str(e)        

@app.route('/return-srt/subtitles.srt')
def return_files_tut__():
	try:
		return send_file('outputFiles/srt/subtitles.srt', attachment_filename='subtitles.srt')
	except Exception as e:
		return str(e) 
if __name__ == "__main__":

   # load_model_()
    app.run(debug=True)