|
import os |
|
import shutil |
|
if not os.path.exists('variables'): |
|
os.makedirs('variables') |
|
shutil.move('variables.data-00000-of-00001', 'variables') |
|
shutil.move('variables.index', 'variables') |
|
if not os.path.exists('tokenizer'): |
|
os.makedirs('tokenizer') |
|
shutil.move('tokenizer_config.json', 'tokeizer') |
|
shutil.move('vocab.txt', 'tokenizer') |
|
|
|
import pandas as pd |
|
import numpy as np |
|
import tensorflow as tf |
|
from transformers.models.bert import BertTokenizer |
|
from transformers import TFBertModel |
|
import streamlit as st |
|
import pandas as pd |
|
from transformers import TFAutoModel |
|
|
|
|
|
|
|
hist_loss= [0.1971,0.0732,0.0465,0.0319,0.0232,0.0167,0.0127,0.0094,0.0073,0.0058,0.0049,0.0042] |
|
hist_acc = [0.9508,0.9811,0.9878,0.9914,0.9936,0.9954,0.9965,0.9973,0.9978,0.9983,0.9986,0.9988] |
|
hist_val_acc = [0.9804,0.9891,0.9927,0.9956,0.9981,0.998,0.9991,0.9997,0.9991,0.9998,0.9998,0.9998] |
|
hist_val_loss = [0.0759,0.0454,0.028,0.015,0.0063,0.0064,0.004,0.0011,0.0021,0.00064548,0.0010,0.00042896] |
|
Epochs = [i for i in range(1,13)] |
|
|
|
hist_loss[:] = [x * 100 for x in hist_loss] |
|
hist_acc[:] = [x * 100 for x in hist_acc] |
|
hist_val_acc[:] = [x * 100 for x in hist_val_acc] |
|
hist_val_loss[:] = [x * 100 for x in hist_val_loss] |
|
d = {'val_acc':hist_val_acc, 'acc':hist_acc,'loss':hist_loss, 'val_loss':hist_val_loss, 'Epochs': Epochs} |
|
chart_data = pd.DataFrame(d) |
|
chart_data.index = range(1,13) |
|
|
|
@st.cache(suppress_st_warning=True, allow_output_mutation=True) |
|
def load_model(show_spinner=True): |
|
yorum_model = tf.keras.models.load_model(os.currdir) |
|
tokenizer = BertTokenizer.from_pretrained('NimaKL/TC32') |
|
return yorum_model, tokenizer |
|
|
|
st.set_page_config(layout='wide', initial_sidebar_state='expanded') |
|
col1, col2= st.columns(2) |
|
with col1: |
|
st.title("TC32 Multi-Class Text Classification") |
|
st.subheader('Model Loss and Accuracy') |
|
st.area_chart(chart_data) |
|
yorum_model, tokenizer = load_model() |
|
|
|
|
|
|
|
with col2: |
|
st.title("Sınıfı bulmak için bir şikayet girin.") |
|
st.subheader("Şikayet") |
|
text = st.text_area('', height=240) |
|
aButton = st.button('Ara') |
|
|
|
def prepare_data(input_text, tokenizer): |
|
token = tokenizer.encode_plus( |
|
input_text, |
|
max_length=256, |
|
truncation=True, |
|
padding='max_length', |
|
add_special_tokens=True, |
|
return_tensors='tf' |
|
) |
|
return { |
|
'input_ids': tf.cast(token.input_ids, tf.float64), |
|
'attention_mask': tf.cast(token.attention_mask, tf.float64) |
|
} |
|
|
|
def make_prediction(model, processed_data, classes=['Alışveriş','Anne-Bebek','Beyaz Eşya','Bilgisayar','Cep Telefonu','Eğitim','Elektronik','Emlak ve İnşaat','Enerji','Etkinlik ve Organizasyon','Finans','Gıda','Giyim','Hizmet','İçecek','İnternet','Kamu','Kargo-Nakliyat','Kozmetik','Küçük Ev Aletleri','Medya','Mekan ve Eğlence','Mobilya - Ev Tekstili','Mücevher Saat Gözlük','Mutfak Araç Gereç','Otomotiv','Sağlık','Sigorta','Spor','Temizlik','Turizm','Ulaşım']): |
|
probs = model.predict(processed_data)[0] |
|
return classes[np.argmax(probs)] |
|
|
|
|
|
if text or aButton: |
|
with col2: |
|
with st.spinner('Wait for it...'): |
|
processed_data = prepare_data(text, tokenizer) |
|
result = make_prediction(yorum_model, processed_data=processed_data) |
|
description = '<table style="border: collapse;"><tr><div style="height: 62px;"></div></tr><tr><p style="border-width: medium; border-color: #aa5e70; border-radius: 10px;padding-top: 1px;padding-left: 20px;background:#20212a;font-family:Courier New; color: white;font-size: 36px; font-weight: boldest;">'+result+'</p></tr><table>' |
|
st.markdown(description, unsafe_allow_html=True) |
|
with col1: |
|
st.success("Tahmin başarıyla tamamlandı!") |
|
|
