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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"))
gpt_models = ["birgermoell/swedish-gpt", "flax-community/swe-gpt-wiki", "flax-community/nordic-gpt-wiki", "flax-community/norsk-gpt-wiki", "flax-community/dansk-gpt-wiki"]
selected_model = st.selectbox(
"What language model would you like to try?",
(gpt_models))
tokenizer = AutoTokenizer.from_pretrained(selected_model)
model = AutoModelWithLMHead.from_pretrained(selected_model)
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
st.title('GPT text generering')
desc = "Pröva GPT-modeller på flera språk. Skriv text nedanför för att utvärdera. Använder " + selected_model + " för att generera text. " + "Här kan du läsa mer om modellen https://huggingface.co/" + selected_model
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"])
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