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import gradio as gr
from pythainlp import word_tokenize
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Embedding, Conv1D, MaxPooling1D, Dense, Flatten, Concatenate, Dropout, Dot, Activation, Reshape, Permute, Multiply
from keras import backend as K
import pandas as pd
from transformers import TFAutoModel, AutoTokenizer
from sklearn.model_selection import train_test_split
# load the tokenizer and transformer model
tokenizer = AutoTokenizer.from_pretrained("nlptown/flaubert_small_cased_sentiment",max_length=60) #xlm-roberta-base bert-base-multilingual-cased
transformer_model = TFAutoModel.from_pretrained("nlptown/flaubert_small_cased_sentiment") #philschmid/tiny-bert-sst2-distilled
max_seq_length = 32
def create_model():
# input_layer = Input(shape=(input_ids.shape[1],))
# embedding_layer = Embedding(max_seq_length+ 1, 200, input_length=max_seq_length)(input_layer)
inputs = tf.keras.layers.Input(shape=(input_ids.shape[1],), dtype=tf.int32)
embedding_layer = transformer_model(inputs)[0]
flatten_layer = Flatten()(embedding_layer)
x1 = Dense(64, activation='relu')(flatten_layer)
x1 = Dense(32, activation='relu')(x1)
x1 = Dense(16, activation='relu')(x1)
x2 = Dense(64, activation='relu')(flatten_layer)
x2 = Dense(32, activation='relu')(x2)
x2 = Dense(16, activation='relu')(x2)
x3 = Dense(64, activation='relu')(flatten_layer)
x3 = Dense(32, activation='relu')(x3)
x3 = Dense(16, activation='relu')(x3)
x4 = Dense(64, activation='relu')(flatten_layer)
x4 = Dense(32, activation='relu')(x4)
x4 = Dense(16, activation='relu')(x4)
x5 = Dense(64, activation='relu')(flatten_layer)
x5 = Dense(32, activation='relu')(x5)
x5 = Dense(16, activation='relu')(x5)
x6 = Dense(512, activation='relu')(flatten_layer)
x6 = Dense(256, activation='relu')(x6)
x6 = Dense(128, activation='relu')(x6)
x7 = Dense(128, activation='relu')(flatten_layer)
x7 = Dense(64, activation='relu')(x7)
x7 = Dense(32, activation='relu')(x7)
x8 = Dense(256, activation='relu')(flatten_layer)
x8 = Dense(128, activation='relu')(x8)
x8 = Dense(64, activation='relu')(x8)
output_layer1 = Dense(1, activation='sigmoid', name='output1')(x1)
output_layer2 = Dense(1, activation='sigmoid', name='output2')(x2)
output_layer3 = Dense(1, activation='sigmoid', name='output3')(x3)
output_layer4 = Dense(1, activation='sigmoid', name='output4')(x4)
output_layer5 = Dense(1, activation='sigmoid', name='output5')(x5)
output_layer6 = Dense(num_class_label_6, activation='softmax', name='output6')(x6)
output_layer7 = Dense(num_class_label_7, activation='softmax', name='output7')(x7)
output_layer8 = Dense(num_class_label_8, activation='softmax', name='output8')(x8)
model = Model(inputs=inputs , outputs=[output_layer1, output_layer2, output_layer3,output_layer4,output_layer5,output_layer6,output_layer7,output_layer8])
model.load_weights("t1_m1.h5")
return model
model =create_model()
def predict(text):
test_texts = [text]
spilt_thai_text = [word_tokenize(x) for x in test_texts]
new_input_ids = tokenizer(spilt_thai_text, padding=True, truncation=True, return_tensors="tf",is_split_into_words=True)["input_ids"]
test_padded_sequences = pad_sequences(new_input_ids, maxlen=max_seq_length,padding='post',truncating='post',value=1) #post pre
print(test_padded_sequences.shape)
predicted_labels = model.predict(test_padded_sequences)
for i in range(len(test_texts)):
print(test_texts[i])
valid = 1 if predicted_labels[0][i] > 0.5 else 0
is_scene = 1 if predicted_labels[1][i] > 0.5 else 0
has_num = 1 if predicted_labels[2][i] > 0.5 else 0
print(f'is_valid : {valid}')
print(f'is_scene : {is_scene}')
print(f'has_num : {has_num}')
turn = 1 if predicted_labels[3][i] > 0.5 else 0
print(f'turn_on_off : {turn}')
print(f'device : ΰΉΰΈ')
env_id = np.argmax(predicted_labels[5][i])
env_label = env_decode[env_id]
hour_id = np.argmax(predicted_labels[6][i])
hour_label = hour_decode[hour_id]
minute_id = np.argmax(predicted_labels[7][i])
minute_label = minute_decode[minute_id]
print(f'env : {env_label}')
print(f'hour : {hour_label}')
print(f'minute : {minute_label}')
print('----')
return 'hello'
iface = gr.Interface(
fn=predict,
inputs='text',
outputs='text',
examples=[["Hello! My name is Omar"]]
)
iface.launch()
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