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FROM node:13-alpine

ENV MONGO_DB_USERNAME=admin \
    MONGO_DB_PWD=password

RUN mkdir -p /home/app

COPY ./app /home/app

# set default dir so that next commands executes in /home/app dir

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version: '3'
services:
  # my-app:
  # image: ${docker-registry}/my-app:1.0
  # ports:
  # - 3000:3000
  mongodb:
    image: mongo
    ports:
      - 27017:27017

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loss, accuracy = model.evaluate(X_test,y_test)
print('Testing Accuracy is {} '.format(accuracy*100))

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loss, accuracy = model.evaluate(X_train, y_train, verbose=1)
print('Training Accuracy is {}'.format(accuracy*100))

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model = Sequential()
model.add(Embedding(vocab_size, 8, input_length=max_length))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])

print(model.summary())

model.fit(X_train, y_train, epochs=20, verbose=0)

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max_length = 1719
X_train = pad_sequences(X_train, maxlen=max_length, padding='pre')
X_test = pad_sequences(X_test, maxlen=max_length, padding='pre')

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maxlen=-1
for doc in X_train:
    if(maxlen<len(doc)):
        maxlen=len(doc)
        print(maxlen)
print("The maximum number of words in any document is : ",maxlen)

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print(X_train.iloc[1])

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t = Tokenizer()
t.fit_on_texts(docs)
vocab_size = len(t.word_index) + 1
# integer encode the documents
print(vocab_size)

X_train = [one_hot(d, vocab_size,filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~',lower=True, split=' ') for d in X_train]
X_test = [one_hot(d, vocab_size,filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~',lower=True, split=' ') for d in X_test]

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docs = df['review']
labels = array(df['sentiment'])
from sklearn.model_selection import train_test_split 
X_train, X_test , y_train, y_test = train_test_split(docs, labels , test_size = 0.40)