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import streamlit as st | |
from transformers import GPT2LMHeadModel, GPT2Tokenizer, TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments | |
import torch | |
st.header('Генератор плохих подкатов') | |
tokenizer = GPT2Tokenizer.from_pretrained('SeducerWeight/repo_name') | |
model = GPT2LMHeadModel.from_pretrained('SeducerWeight/repo_name', from_tf=False) | |
text = st.text_input("Кринжовое начало") | |
max_length = st.slider('Выберите длину', 30, 150, 40, 5) | |
temp = st.slider('Выберите температуру', 1.5, 4.0, 2.0, 0.25) | |
top_p = st.slider('Выберите top_p', 1.5, 4.0, 2.0, 0.25) | |
if text and max_length and temp and top_p: | |
input_ids = tokenizer.encode(text, return_tensors="pt") | |
model.eval() | |
with torch.no_grad(): | |
out = model.generate(input_ids, | |
do_sample=True, | |
num_beams=3, | |
temperature=temp, | |
top_p=top_p, | |
max_length=max_length, | |
) | |
generated_text = list(map(tokenizer.decode, out)) | |
count = st.number_input('Введите количество подкатов', 1, 5, 1) | |
st.write(generated_text[0].split('\n')[count-1]) | |