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// Create instance of the IAU manager.
val appUpdateManager = AppUpdateManagerFactory.create(context)

// Add state listener to app update info task.
appUpdateManager.appUpdateInfo.addOnSuccessListener { appUpdateInfo ->
    // If there is an update available, prepare to promote it.
    if (appUpdateInfo.updateAvailability() == UpdateAvailability.UPDATE_AVAILABLE
        && appUpdateInfo.isUpdateTypeAllowed(AppUpdateType.FLEXIBLE)) {
        // ...
    }

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appUpdateManager.startUpdateFlowForResult(
        appUpdateInfo,
        AppUpdateType.FLEXIBLE,
        activity,
        0
)

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// Create a listener to track downloading state updates.
val listener = InstallStateUpdatedListener { state -> 
    // Update progress indicator, request user to approve app reload, etc.
}

// At some point before starting an update, register a listener for updates.
appUpdateManager.registerListener(listener)

// ...

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Snackbar.make(
    rootView,
    "An update has just been downloaded from Google Play",
    Snackbar.LENGTH_INDEFINITE
).apply {
    setAction("RELOAD") { appUpdateManager.completeUpdate() }
    show()
}

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def preprocessData(dataFilePath, mode):
    conversations = []
    labels = []
    with io.open(dataFilePath, encoding="utf8") as finput:
        finput.readline()
        for line in finput:
            line = line.strip().split('\t')
            for i in range(1, 4):
                line[i] = tokenize(line[i])
            if mode == "train":

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from ekphrasis.classes.preprocessor import TextPreProcessor
from ekphrasis.classes.tokenizer import SocialTokenizer
from ekphrasis.dicts.emoticons import emoticons
import numpy as np

import re
import io

label2emotion = {0: "others", 1: "happy", 2: "sad", 3: "angry"}
emotion2label = {"others": 0, "happy": 1, "sad": 2, "angry": 3}

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def getEmbeddings(file):
    embeddingsIndex = {}
    dim = 0
    with io.open(file, encoding="utf8") as f:
        for line in f:
            values = line.split()
            word = values[0]
            embeddingVector = np.asarray(values[1:], dtype='float32')
            embeddingsIndex[word] = embeddingVector 
            dim = len(embeddingVector)

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from keras.layers import Input, Dense, Embedding, Concatenate, Activation, \
    Dropout, LSTM, Bidirectional, GlobalMaxPooling1D, GaussianNoise
from keras.models import Model


def buildModel(embeddings_matrix, sequence_length, lstm_dim, hidden_layer_dim, num_classes, 
               noise=0.1, dropout_lstm=0.2, dropout=0.2):
    turn1_input = Input(shape=(sequence_length,), dtype='int32')
    turn2_input = Input(shape=(sequence_length,), dtype='int32')
    turn3_input = Input(shape=(sequence_length,), dtype='int32')

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@inproceedings{
    smetanin-2019-emosense, 
    title = "{E}mo{S}ense at {S}em{E}val-2019 Task 3: Bidirectional {LSTM} Network for Contextual Emotion Detection in Textual Conversations", 
    author = "Smetanin, Sergey", booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation", 
    year = "2019", 
    address = "Minneapolis, Minnesota, USA", 
    publisher = "Association for Computational Linguistics", 
    url = "https://www.aclweb.org/anthology/S19-2034", pages = "210--214"
}

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