app.py

import streamlit as st
import transformers


# @st.cache(hash_funcs={tokenizers.Tokenizer: id, tokenizers.Tokenizer: id})
def load_stuff():
    model = transformers.AutoModelForCausalLM.from_pretrained("distilgpt2")
    tokenizer = transformers.AutoTokenizer.from_pretrained("distilgpt2")
    return model, tokenizer


st.image("./img.jpg")

model, tokenizer = load_stuff()

user_inputed_text = st.text_input("Insert text")

if len(user_inputed_text) == 0:
    outputs_text = "no text provided. write some text, meatbag"
else:
    outputs = model.generate(
        **tokenizer([user_inputed_text], return_tensors='pt'),
        max_new_tokens=50, do_sample=True,
    )
    outputs_text = tokenizer.decode(outputs[0])

st.text_area(label='output', value=outputs_text)