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 = '

'+result+'

' st.markdown(description, unsafe_allow_html=True) with col1: st.success("Tahmin başarıyla tamamlandı!")