import streamlit as st
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer

from model.funcs import execution_time


@st.cache_data
def load_model():
    model_path = "17/"
    model_name = "sberbank-ai/rugpt3small_based_on_gpt2"
    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
    model = GPT2LMHeadModel.from_pretrained(model_path)
    return tokenizer, model


tokenizer, model = load_model()


@execution_time
def generate_text(promt):
    promt = tokenizer.encode(promt, return_tensors="pt")
    model.eval()
    with torch.no_grad():
        out = model.generate(
            promt,
            do_sample=True,
            num_beams=2,
            temperature=1.5,
            top_p=0.9,
            max_length=150,
        )
    out = list(map(tokenizer.decode, out))[0]
    return out


promt = st.text_input("Ask a question")
generate = st.button("Generate")
if generate:
    if not promt:
        st.write("42")
    else:
        st.write(generate_text(promt))