app.py

import streamlit as st
import time
from transformers import pipeline
import torch
#from transformers import AutoModelForCausalLM, AutoTokenizer

#@st.cache(allow_output_mutation=True)
#def define_model():
#    model = AutoModelForCausalLM.from_pretrained("facebook/opt-1.3b", torch_dtype=torch.float16).cuda()
#    tokenizer = AutoTokenizer.from_pretrained("facebook/opt-1.3b", use_fast=False)
#    return model, tokenizer
generator = pipeline('text-generation', model="facebook/opt-1.3b", skip_special_tokens=True)    

@st.cache(allow_output_mutation=True)
def define_model(prompt):  
    answer = generator(prompt)
    return answer


#@st.cache(allow_output_mutation=True)
#def opt_model(prompt, model, tokenizer, num_sequences = 1, max_length = 50):  
#    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
#    generated_ids = model.generate(input_ids, num_return_sequences=num_sequences, max_length=max_length)
#    answer = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
#    return answer


#model, tokenizer = define_model()

prompt= st.text_area('Your prompt here',
 '''Hello, I'm am conscious and''')
 
#answer = opt_model(prompt, model, tokenizer,)
#lst = ['ciao come stai sjfsbd dfhsdf  fuahfuf  feuhfu wefwu ']
answer = define_model(prompt)
lst = answer[0]['generated_text']

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