import streamlit as st
from transformers import pipeline

# Turkish 
sentiment_pipeline_tr = pipeline(task = "text-classification", model = "SoDehghan/BERTurk-hate-speech")  # "gritli/bert-sentiment-analyses-imdb"
strength_pipeline_tr = pipeline(task = "text-classification", model = "SoDehghan/BERTurk-hate-speech")  # "gritli/bert-sentiment-analyses-imdb"

def write():
    st.markdown(
    """
    # Hate Speech Detection in Turkish
    
    """
    )

    tr_input = st.text_area("Enter your text here:", height=50, key="tr_input")  #height=30
    if st.button("Model prediction", key="tr_predict"):
       st.write(" ")
       with st.spinner('Generating predictions...'):
         result_sentiment_tr = sentiment_pipeline_tr(tr_input)
         sentiment_tr = result_sentiment_tr[0]["label"]
         label_dict_sentiment = {'LABEL_1': 'Detection: Hate ❌', 'LABEL_0': 'Detection: Non-hate ✅'}  #🚫
         sentiment_tr = label_dict_sentiment[sentiment_tr]

         result_strength_tr = strength_pipeline_tr(tr_input)
         strength_tr = result_strength_tr[0]["label"]
         label_dict_strength = {'LABEL_0': 'Strength: 0', 'LABEL_1': 'Strength: 1', 'LABEL_2': 'Strength: 2','LABEL_3': 'Strength: 3', 'LABEL_4': 'Strength: 4'}  #🚫
         strength_tr = label_dict_strength[strength_tr]
         st.write(sentiment_tr)
         st.write(strength_tr)
          
         #st.success(sentiment_tr)
         #st.success(strength_tr)