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def insertion_sort(array):
	for i in range(1, len(array)):
		data = array[i]
		j = i
		while j > 0 and array[j-1] > data:
			array[j] = array[j - 1]
			j -= 1
		array[j] = data
	return array

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/*
 * A Java Script containing methods to manipulate a MongoDB
 * Biney Kingsley - 07-04-2019
 * 
 * */

package com.mongo;

import org.bson.Document;

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def score_review(source=None, file_type=None):
    '''
    source: the text, as either a string or a list of strings
    file_type: (str): indicating whether we expecting a file containing the
    text data or a directory containing a list files holding the text
    options: 'file' or 'dir'
    '''
    text_list = list()
    if isinstance(source, str) and file_type is None:
        text_list.append(source)

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def decode_review(text_list):
    word_index = tokenizer.word_index
    sequences = tokenizer.texts_to_sequences(text_list)
    data = pad_sequences(sequences, maxlen=500)

    # decode the words
    reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
    decoded_review = ' '.join([reverse_word_index.get(i, '?') for i in sequences[0]])
    return decoded_review, data

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def review_rating(score, decoded_review):
    if float(score) >= 0.9:
        print('Review: {}\nSentiment: Strongly Positive\nScore: {}'.format(decoded_review, score))
    elif float(score) >= 0.7 and float(score) < 0.9:
        print('Review: {}\nSentiment: Positive\nScore: {}'.format(decoded_review, score))
    elif float(score) >= 0.5 and float(score) < 0.7:
        print('Review: {}\nSentiment: Okay\nScore: {}'.format(decoded_review, score))
    else:
        print('Review: {}\nSentiment: Negative\nScore: {}'.format(decoded_review, score))
    print('\n\n')

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tokenizer_path = 'tokenizer'
model_path = 'model'
model_file = os.path.join(model_path, 'movie_sentiment_m1.h5')
tokenizer_file = os.path.join(tokenizer_path, 'tokenizer_m1.pickle')
model = load_model(model_file)

# load tokenizer
with open(tokenizer_file, 'rb') as handle:
    tokenizer = pickle.load(handle)

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# save the tokenizer
with open(os.path.join(tokenizer_path, 'tokenizer_m1.pickle'), 'wb') as handle:
    pk.dump(tokenizer, handle, protocol=pk.HIGHEST_PROTOCOL)

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callback_list = [
    keras.callbacks.EarlyStopping(
        patience=1,
        monitor='acc',
    ),
    
    keras.callbacks.TensorBoard(
        log_dir='log_dir_m1',
        histogram_freq=1,
        embeddings_freq=1,

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history = model.fit(x_train, y_train, epochs=50, batch_size=128, callbacks=callback_list,
                    validation_data=(x_val, y_val))

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# model developing
text_input_layer = Input(shape=(500,))
embedding_layer = Embedding(max_words, 50)(text_input_layer)
text_layer = Conv1D(256, 3, activation='relu')(embedding_layer)
text_layer = MaxPooling1D(3)(text_layer)
text_layer = Conv1D(256, 3, activation='relu')(text_layer)
text_layer = MaxPooling1D(3)(text_layer)
text_layer = Conv1D(256, 3, activation='relu')(text_layer)
text_layer = MaxPooling1D(3)(text_layer)
text_layer = Conv1D(256, 3, activation='relu')(text_layer)