import streamlit as st
import transformers
from transformers import GPT2LMHeadModel, GPT2Tokenizer

#from transformers import T5Tokenizer, T5ForConditionalGeneration
#model_name = "t5-base"
#tokenizer = T5Tokenizer.from_pretrained(model_name)
#model = T5ForConditionalGeneration.from_pretrained(model_name)
model_name = "indonesia/gpt-2-small-indonesia"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

def generate_response(input_text):
    input_ids = tokenizer.encode(input_text, return_tensors='pt')
    outputs = model.generate(input_ids,
                             min_length=5,
                             max_length=300,
                             do_sample=True, num_beams=5, no_repeat_ngram_size=2)
    generated_text = tokenizer.decode(
        outputs[0], skip_special_tokens=True)
    return generated_text


prompt = st.chat_input(placeholder="Say Something!",key=None, max_chars=None, disabled=False, on_submit=None, args=None, kwargs=None)
if prompt:
   with st.chat_message(name="AI",avatar=None):
    st.write(generate_response(prompt))