apps/turna.py

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:right;">TURNA is a Turkish encoder-decoder language model.</h3>
			
			<p style="text-align:right;"><p>
			<p style="text-align:right;">Use the generation parameters on the sidebar to adjust generation quality.</p>
			<p style="text-align:right;"><p>
			""",
			unsafe_allow_html=True,
		)

	#st.title('Turkish Language Generation')
	#st.write('...with Turna')

	# Sidebar

	# Taken from https://huggingface.co/spaces/flax-community/spanish-gpt2/blob/main/app.py
	st.sidebar.subheader("Configurable parameters")

	max_new_tokens = st.sidebar.number_input(
		"Maximum length",
		min_value=0,
		max_value=512,
		value=128,
		help="The maximum length of the sequence to be generated.",
	)
	length_penalty = st.sidebar.number_input(
		"Length penalty",
		value=1.0,
		help=" length_penalty > 0.0 promotes longer sequences, while length_penalty < 0.0 encourages shorter sequences. ",
	)
	do_sample = st.sidebar.selectbox(
		"Sampling?",
		(True, False),
		help="Whether or not to use sampling; use greedy decoding otherwise.",
	)
	num_beams = st.sidebar.number_input(
		"Number of beams",
		min_value=1,
		max_value=10,
		value=3,
		help="The number of beams to use for beam search.",
	)
	repetition_penalty = st.sidebar.number_input(
		"Repetition Penalty",
		min_value=0.0,
		value=3.0,
		step=0.1,
		help="The parameter for repetition penalty. 1.0 means no penalty",
	)
	no_repeat_ngram_size = st.sidebar.number_input(
		"No Repeat N-Gram Size",
		min_value=0,
		value=3,
		help="If set to int > 0, all ngrams of that size can only occur once.",
	)
	temp = st.sidebar.slider(
		"Temperature",
		value=1.0,
		min_value=0.1,
		max_value=100.0,
		help="The value used to module the next token probabilities.",
	)
	top_k = st.sidebar.number_input(
		"Top k",
		value=10,
		help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
	)
	top_p = st.sidebar.number_input(
		"Top p",
		value=0.95,
		help=" If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.",
	)

	input_text = st.text_area(label='Enter a text: ', height=100, 
		value="Bir varmış, bir yokmuş, evvel zaman içinde, kalbur saman içinde, uzak diyarların birinde bir turna")
	url = "https://api-inference.huggingface.co/models/boun-tabi-LMG/TURNA"
	params = {"length_penalty": length_penalty, "no_repeat_ngram_size": no_repeat_ngram_size, "max_new_tokens": max_new_tokens,
			"do_sample":do_sample, "num_beams":num_beams, "repetition_penalty":repetition_penalty, 
			 "top_p":top_p, "top_k":top_k, "temperature":temp, "early_stopping": True, "max_length": 256}
	if st.button("Generate"):
		with st.spinner('Generating...'):
			output = query(f'[S2S] {input_text}<EOS>', url, params)
			st.success(output)