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import pandas as pd |
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import numpy as np |
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import tensorflow as tf |
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from transformers.models.bert import BertTokenizer |
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from transformers import TFBertModel |
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import streamlit as st |
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import pandas as pd |
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from transformers import TFAutoModel |
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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] |
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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] |
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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] |
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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] |
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Epochs = [i for i in range(1,13)] |
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hist_loss[:] = [x * 100 for x in hist_loss] |
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hist_acc[:] = [x * 100 for x in hist_acc] |
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hist_val_acc[:] = [x * 100 for x in hist_val_acc] |
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hist_val_loss[:] = [x * 100 for x in hist_val_loss] |
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d = {'val_acc':hist_val_acc, 'acc':hist_acc,'loss':hist_loss, 'val_loss':hist_val_loss, 'Epochs': Epochs} |
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chart_data = pd.DataFrame(d) |
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chart_data.index = range(1,13) |
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@st.cache(suppress_st_warning=True, allow_output_mutation=True) |
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def load_model(show_spinner=True): |
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yorum_model = tf.keras.models.load_model('TC32_SavedModel') |
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tokenizer = BertTokenizer.from_pretrained('NimaKL/tc32_test') |
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return yorum_model, tokenizer |
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st.set_page_config(layout='wide', initial_sidebar_state='expanded') |
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st.title("TC32 Multi-Class Text Classification") |
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st.subheader('Model Loss and Accuracy') |
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st.markdown("<br>", unsafe_allow_html=True) |
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st.area_chart(chart_data, height=320) |
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yorum_model, tokenizer = load_model() |
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st.title("Sınıfı bulmak için bir şikayet girin. (Ctrl+Enter)") |
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st.subheader("Enter complaint (in Turkish) to find the class.") |
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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) |
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def prepare_data(input_text, tokenizer): |
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token = tokenizer.encode_plus( |
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input_text, |
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max_length=256, |
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truncation=True, |
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padding='max_length', |
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add_special_tokens=True, |
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return_tensors='tf' |
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) |
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return { |
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'input_ids': tf.cast(token.input_ids, tf.float64), |
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'attention_mask': tf.cast(token.attention_mask, tf.float64) |
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} |
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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']): |
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probs = model.predict(processed_data)[0] |
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return classes[np.argmax(probs)] |
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if text: |
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with st.spinner('Wait for it...'): |
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processed_data = prepare_data(text, tokenizer) |
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result = make_prediction(yorum_model, processed_data=processed_data) |
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st.markdown("<br>", unsafe_allow_html=True) |
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st.success("Tahmin başarıyla tamamlandı!") |
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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>' |
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st.markdown(description, unsafe_allow_html=True) |
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