app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelWithLMHead, GPT2Tokenizer, GPT2Model, FlaxGPT2LMHeadModel, GPT2LMHeadModel, pipeline, set_seed
import torch

#===========================================#
#        Loads Model and Pipeline        #
#===========================================#

tokenizer = AutoTokenizer.from_pretrained("flax-community/swe-gpt-wiki")
model = AutoModelWithLMHead.from_pretrained("flax-community/swe-gpt-wiki")

generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
# set_seed(42)


#===========================================#
#              Streamlit Code               #
#===========================================#

# result = st.sidebar.selectbox(
#     "What language model would you like to try?",
#     ("Swedish", "Norwegian", "Danish", "Nordic"))


st.title('Svensk GPT text generering')
desc = "En svensk GPT-modell tränad på wikipedia. Fyll i text i fältet nedanför för generering."

st.write(desc)

num_sentences = st.number_input('Number of Characters', min_value=1, max_value=150, value=75)
user_input = st.text_input('Fyll i text att generera ifrån')


if st.button('Generate Text'):
    generated_text = generator(user_input, max_length=num_sentences, num_return_sequences=1)
    st.write(generated_text[0]["generated_text"])