app.py

import transformers
import streamlit as st

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VietAI/gpt-neo-1.3B-vietnamese-news")
@st.cache
def load_model(model_name):
    model = AutoModelForCausalLM.from_pretrained(model_name)
    return model

model = load_model("VietAI/gpt-neo-1.3B-vietnamese-news")
def infer(input_ids, max_length):

    output_sequences = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        do_sample=True,
        temperature=0.9,
        top_k=20,
        #top_p=top_p,
        #num_return_sequences=1
    )

    return output_sequences

default_value = "Have fun!"

st.title("Write with Transformers 🦄")
st.write("Generate Vietnamese text from a given prompt")

sent = st.text_area("Text", default_value, height = 275)
max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=30)
#temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
#top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0)
#top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.9)

encoded_prompt = tokenizer.encode(sent, add_special_tokens=False, return_tensors="pt")
if encoded_prompt.size()[-1] == 0:
    input_ids = None
else:
    input_ids = encoded_prompt

gen_tokens = infer(encoded_prompt, max_length)
gen_text = tokenizer.batch_decode(gen_tokens)[0]
st.write(gen_text)