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| 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) | |
| def load_model(show_spinner=True): | |
| yorum_model = tf.keras.models.load_model('TC32_SavedModel') | |
| tokenizer = BertTokenizer.from_pretrained('NimaKL/tc32_test') | |
| 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.markdown("<br>", unsafe_allow_html=True) | |
| st.area_chart(chart_data, height=320) | |
| yorum_model, tokenizer = load_model() | |
| with col2: | |
| st.title("Sınıfı bulmak için bir şikayet girin. (Ctrl+Enter)") | |
| st.subheader("Enter complaint (in Turkish) to find the class.") | |
| #st.subheader("Şikayet") | |
| text = st.text_area("", "Bebeğim haftada bir kutu mama bitiriyor. Geçen hafta 135 tl'ye aldığım mama bugün 180 tl olmuş. Ben de artık aptamil almayacağım. Tüketici haklarına şikayet etmemiz gerekiyor. Yazıklar olsun.", height=285) | |
| 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: | |
| with col1: | |
| with st.spinner('Wait for it...'): | |
| processed_data = prepare_data(text, tokenizer) | |
| result = make_prediction(yorum_model, processed_data=processed_data) | |
| st.markdown("<br>", unsafe_allow_html=True) | |
| st.success("Tahmin başarıyla tamamlandı!") | |
| with col2: | |
| description = '<table style="border: collapse; padding-top: 1px;"><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) | |