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from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
import streamlit as st
model = GPT2LMHeadModel.from_pretrained(
'sberbank-ai/rugpt3small_based_on_gpt2',
output_attentions = False,
output_hidden_states = False,
)
tokenizer = GPT2Tokenizer.from_pretrained('sberbank-ai/rugpt3small_based_on_gpt2')
# Вешаем сохраненные веса на нашу модель
model.load_state_dict(torch.load('model.pt', map_location=torch.device('cpu')))
prompt = st.text_input('Введите текст prompt:')
length = st.slider('Длина генерируемой последовательности:', 1, 256, 16)
num_samples = st.slider('Число генераций:', 1, 6, 1)
temperature = st.slider('Температура:', 1.0, 6.0, 1.0)
selected_text = st.empty()
def generate_text(model, tokenizer, prompt, length, num_samples, temperature, selected_text):
input_ids = tokenizer.encode(prompt, return_tensors='pt')
output_sequences = model.generate(
input_ids=input_ids,
max_length=length,
num_return_sequences=num_samples,
temperature=temperature
)
generated_texts = []
for output_sequence in output_sequences:
generated_text = tokenizer.decode(output_sequence, clean_up_tokenization_spaces=True)
generated_texts.append(generated_text)
selected_text.slider('Выберите текст:', 1, num_samples, 1)
return generated_texts[selected_text.value-1]
if st.button('Сгенерировать текст'):
text = generate_text(model, tokenizer, prompt, length, num_samples, temperature, selected_text)
st.write(text)
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