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('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)