import requests
import streamlit as st
import time 
from transformers import pipeline
import os
from .utils import query

def write():
	st.markdown(
			"""
			<h1 style="text-align:left;">TURNA</h1>
			""",
			unsafe_allow_html=True,
		)

	st.write("#")

	col = st.columns(2)

	col[0].image("images/turna-logo.png", width=100)

	st.markdown(
			"""
			<h3 style="text-align:left;">... is a Turkish encoder-decoder language model.</h3>
			<p style="text-align:right;"><p>
			<p style="text-align:left;">In this Huggingface space, you can test the TURNA language model. </p>
			<p style="text-align:left;">The model contains 1.1B parameters, and was pre-trained with an encoder-decoder architecture following the UL2 framework on 43B tokens from various domains.  </p>
            <p style="text-align:left;">TURNA was fine-tuned to carry out Turkish summarization, paraphrasing, news title generation, sentiment classification, text categorization, named entity recognition, part-of-speech tagging, semantic textual similarity and natural language inference tasks.  </p>
            <p style="text-align:left;">Go to the <strong>Navigation</strong> bar to access our applications. </p>
            <p style="text-align:left;">Refer to our <a href="https://arxiv.org/abs/2401.14373">paper</a>  for more details...  </p>
            <p style="text-align:left;"><p>
            <p style="text-align:right;"><em>TURNA can generate toxic content or provide erroneous information. Double-check before usage. </em><p>
			""",
			unsafe_allow_html=True,
		)