Delete app.py

app.py

@@ -1,76 +0,0 @@
-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 = TFAutoModel.from_pretrained("NimaKL/tc32_test")
-    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.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!")
-