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import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
def app(): # Инкапсулирующая функция
st.title("GPT-2 Demo")
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_path = "zhvanetsky_model"
tokenizer = GPT2Tokenizer.from_pretrained(model_path)
model = GPT2LMHeadModel.from_pretrained(model_path).to(DEVICE)
def generate_text(input_text, num_beams, temperature, max_length, top_p):
model.eval()
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(DEVICE)
with torch.no_grad():
out = model.generate(input_ids,
do_sample=True,
num_beams=num_beams,
temperature=temperature,
top_p=top_p,
top_k=500,
max_length=max_length,
no_repeat_ngram_size=3,
num_return_sequences=3,
)
return tokenizer.decode(out[0], skip_special_tokens=True)
user_input = st.text_area("Input Text", "Введите ваш текст")
# Add sliders or input boxes for model parameters
num_beams = st.slider("Number of Beams", min_value=1, max_value=20, value=10)
temperature = st.slider("Temperature", min_value=0.1, max_value=3.0, value=1.0, step=0.1)
max_length = st.number_input("Max Length", min_value=10, max_value=300, value=100)
top_p = st.slider("Top P", min_value=0.1, max_value=1.0, value=0.85, step=0.05)
if st.button("Generate"):
generated_output = generate_text(user_input, num_beams, temperature, max_length, top_p)
st.text_area("Generated Text", generated_output)
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