app.py

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import xformers
import streamlit as st
import torch
import time
import random
from transformers.trainer_utils import set_seed
import time

SEED = int(time.time())
set_seed(SEED)

#make columns
col1, col2 = st.columns([2,1])

#import model
tokenizer = AutoTokenizer.from_pretrained("gpt2-medium")
tokenizer.padding_side = 'left'
model = AutoModelForCausalLM.from_pretrained("breadlicker45/gpt2-music")
def get_model():
    return pipeline('text-generation', model=model,
 tokenizer=tokenizer, do_sample=True)


#ui
with col1:
    prompt= st.text_input('input',
        '''2623 2619 3970 3976 2607 3973 2735 3973 2598 3985 2726 3973 2607 4009 2735 3973 2598 3973 2726 3973 2607 3973 2735 4009''') 

#gen text
text = prompt

generator = get_model()    
gen = st.info('Generating text...')
answer = generator(text, pad_token_id=tokenizer.eos_token_id, do_sample=True, max_length=350, min_length=80, temperature=0.7, top_k=2, num_beams=1, no_repeat_ngram_size=1, early_stopping=True)                      
gen.empty()
                       
lst = answer[0]['generated_text']
out = lst

t = st.empty()
for i in range(len(out)):
    t.markdown("#### %s" % out[0:i])
    time.sleep(0.04)