| import streamlit as st
|
| from transformers import AutoModelForCausalLM, AutoTokenizer
|
| import torch
|
|
|
| def run():
|
| @st.cache_resource
|
| def load_model():
|
| model = AutoModelForCausalLM.from_pretrained("gpt")
|
| tokenizer = AutoTokenizer.from_pretrained("gpt")
|
| return model, tokenizer
|
|
|
| model, tokenizer = load_model()
|
| model.eval()
|
|
|
|
|
| st.title("📝 Генерация текста GPT-моделью")
|
| prompt = st.text_area("Ввод пользователя (prompt)", "Не думай, друг, что время лечит —")
|
|
|
| max_length = st.slider("Максимальная длина генерации", 20, 300, 100)
|
| num_return_sequences = st.slider("Количество генераций", 1, 5, 1)
|
| temperature = st.slider("Temperature", 0.1, 2.0, 1.0)
|
| top_k = st.slider("Top-k", 0, 100, 50)
|
| top_p = st.slider("Top-p", 0.0, 1.0, 0.95)
|
|
|
| generate_btn = st.button("Сгенерировать")
|
|
|
| if generate_btn:
|
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
|
|
|
| with torch.no_grad():
|
| outputs = model.generate(
|
| **inputs,
|
| max_length=max_length,
|
| num_return_sequences=num_return_sequences,
|
| temperature=temperature,
|
| top_k=top_k,
|
| top_p=top_p,
|
| do_sample=True,
|
| pad_token_id=tokenizer.eos_token_id,
|
| eos_token_id=tokenizer.eos_token_id,
|
| )
|
|
|
| st.subheader("📄 Сгенерированный текст:")
|
| for i, output in enumerate(outputs):
|
| text = tokenizer.decode(output, skip_special_tokens=True)
|
| st.markdown(f"**Вариант {i+1}:**\n\n{text}") |
