app.py

import transformers
import numpy as np
import torch
import streamlit as st

from transformers import GPT2Tokenizer, GPT2LMHeadModel

@st.cache(allow_output_mutation=True)
def load_model():
  model_ckpt = "bankholdup/rugpt3_song_writer"
  tokenizer = GPT2Tokenizer.from_pretrained(model_ckpt)
  model = GPT2LMHeadModel.from_pretrained(model_ckpt)
  return tokenizer, model

def set_seed(rng=100000):
    rd = np.random.randint(rng)
    np.random.seed(rd)
    torch.manual_seed(rd)

title = st.title("Загрузка модели")
tokenizer, model = load_model()
title.title("ruGPT3 Song Writer")
context = st.text_input("Введите начало песни", "Нету милфы сексапильней, чем Екатерина Шульман")
temperature= st.slider("temperature (чем выше, тем текст безумнее, чем ниже, тем ближе к исходным данным)", 0.0, 2.5, 0.95)

if st.button("Поехали", help="Может занять какое-то время"):
    with st.spinner("Генерируем..."):
        generated_sequences = []
        set_seed()
        #st.write("Генерируем...")
        #st.write("temperature = {}".format(temperature))
        #st.write("_____________")
        prompt_text = f"{context}"
        encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")
        output_sequences = model.generate(
                input_ids=encoded_prompt,
                max_length=200 + len(encoded_prompt[0]),
                temperature=temperature,
                top_k=50,
                top_p=0.95,
                repetition_penalty=1.0,
                do_sample=True,
                num_return_sequences=1,
            )
        if len(output_sequences.shape) > 2:
            output_sequences.squeeze_()   
            
        for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
            generated_sequence = generated_sequence.tolist()
            text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
    
            total_sequence = (
                prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
            )
    
            splits = total_sequence.splitlines()
            for line in range(len(splits)-5):
                if "[" in splits[line]:
                    st.write("\n")
                    continue
                    
                st.write(splits[line])