Update app.py

app.py

@@ -7,4 +7,80 @@ if not os.path.exists('variables'):
 if not os.path.exists('tokenizer'):
     os.makedirs('tokenizer')
     shutil.move('tokenizer_config.json', 'tokeizer')
-    shutil.move('vocab.txt', 'tokenizer')
+    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('')
+    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!")