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import streamlit as st
from keras.layers import LSTM, Dropout, Bidirectional, Dense,Embedding,Flatten,Maximum,Activation,Conv2D,LayerNormalization,add\
, BatchNormalization, SpatialDropout1D ,Input,Layer,Multiply,Reshape ,Add, GRU,Concatenate,Conv1D,TimeDistributed,ZeroPadding1D,concatenate,MaxPool1D,GlobalMaxPooling1D
import keras.backend as K
from keras import initializers, regularizers, constraints, activations
from keras.initializers import Constant
class TimestepDropout(Dropout):
def __init__(self, rate, **kwargs):
super(TimestepDropout, self).__init__(rate, **kwargs)
def _get_noise_shape(self, inputs):
input_shape = K.shape(inputs)
noise_shape = (input_shape[0], input_shape[1], 1)
return noise_shape
def model_(n_gram = 21):
input1 = Input(shape=(21,),dtype='float32',name = 'char_input')
input2 = Input(shape=(21,),dtype='float32',name = 'type_input')
a = Embedding(180, 32,input_length=21)(input1)
a = SpatialDropout1D(0.1)(a)
a = TimestepDropout(0.05)(a)
char_input = BatchNormalization()(a)
a_concat = []
filters = [[1,200],[2,200],[3,200],[4,200],[5,200],[6,200],[7,200],[8,200],[9,150],[10,150],[11,150],[12,100]]
for (window_size, filters_size) in filters:
convs = Conv1D(filters=filters_size, kernel_size=window_size, strides=1)(char_input)
convs = Activation('elu')(convs)
convs = TimeDistributed(Dense(5, input_shape=(21, filters_size)))(convs)
convs = ZeroPadding1D(padding=(0, window_size-1))(convs)
a_concat.append(convs)
token_max = Maximum()(a_concat)
lstm_char = Bidirectional(LSTM(100 ,return_sequences=True))(char_input)
b = Embedding(12, 12, input_length=21)(input2)
b = SpatialDropout1D(0.1)(b)
type_inputs = TimestepDropout(0.05)(b)
x = Concatenate()([lstm_char, type_inputs, char_input, token_max])
x = BatchNormalization()(x)
x = Flatten()(x)
x = Dense(200, activation='elu')(x)
x = Dropout(0.2)(x)
out = Dense(1, activation='sigmoid',dtype = 'float32')(x)
model = Model(inputs=[input1, input2], outputs=out)
return model
def create_feature_array(text, n_pad=21):
n = len(text)
n_pad_2 = int((n_pad - 1)/2)
text_pad = [' '] * n_pad_2 + [t for t in text] + [' '] * n_pad_2
x_char, x_type = [], []
for i in range(n_pad_2, n_pad_2 + n):
char_list = text_pad[i + 1: i + n_pad_2 + 1] + \
list(reversed(text_pad[i - n_pad_2: i])) + \
[text_pad[i]]
char_map = [CHARS_MAP.get(c, 179) for c in char_list]
char_type = [CHAR_TYPES_MAP.get(CHAR_TYPE_FLATTEN.get(c, 'o'), 4)
for c in char_list]
x_char.append(char_map)
x_type.append(char_type)
x_char = np.array(x_char).astype(float)
x_type = np.array(x_type).astype(float)
return x_char, x_type
def tokenize(text):
n_pad = 21
if not text:
return ['']
if isinstance(text, str) and sys.version_info.major == 2:
text = text.decode('utf-8')
x_char, x_type = create_feature_array(text, n_pad=n_pad)
word_end = []
y_predict = model.predict([x_char, x_type], batch_size = 512)
y_predict = (y_predict.ravel() > 0.4).astype(int)
word_end = y_predict[1:].tolist() + [1]
tokens = []
word = ''
for char, w_e in zip(text, word_end):
word += char
if w_e:
tokens.append(word)
word = ''
return tokens
model = model_()
model.load_weights("cutto_tf2.h5")
text = st.text_area("Enter original text!")
words = tokenize(text)
st.write('|'.join(words)) |