Update app.py

app.py

@@ -1,4 +1,44 @@
 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
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+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)