gpt2.py

import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch


def app():  # Инкапсулирующая функция
    st.title("GPT-2 Generator")

    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", "Ладно Павел, спасибо за поддержку!")
    
    # 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)