app.py

import streamlit as st
import tensorflow as tf
import numpy as np
import pandas as pd
from transformers import *
import json
import numpy as np
import pandas as pd
from tqdm import tqdm
import os
from tensorflow.python.client import device_lib

model = TFBertModel.from_pretrained('./huggingface_bert.h5')

def sentence_convert_data(data):
    global tokenizer
    tokens, masks, segments = [], [], []
    token = tokenizer.encode(data, max_length=SEQ_LEN, truncation=True, padding='max_length')
    
    num_zeros = token.count(0) 
    mask = [1]*(SEQ_LEN-num_zeros) + [0]*num_zeros 
    segment = [0]*SEQ_LEN

    tokens.append(token)
    segments.append(segment)
    masks.append(mask)

    tokens = np.array(tokens)
    masks = np.array(masks)
    segments = np.array(segments)
    return [tokens, masks, segments]

def movie_evaluation_predict(sentence):
    data_x = sentence_convert_data(sentence)
    predict = sentiment_model.predict(data_x)
    predict_value = np.ravel(predict)
    predict_answer = np.round(predict_value,0).item()

    print(predict_value)

    if predict_answer == 0:
      st.write("(부정 확률 : %.2f) 부정적인 영화 평가입니다." % (1.0-predict_value))
    elif predict_answer == 1:
      st.write("(긍정 확률 : %.2f) 긍정적인 영화 평가입니다." % predict_value)