import streamlit as st from transformers import GPT2LMHeadModel, GPT2Tokenizer import time def generate_text(model, tokenizer, prompt, max_length, num_generations, temperature): generated_texts = [] for _ in range(num_generations): input_ids = tokenizer.encode(prompt, return_tensors="pt") output = model.generate( input_ids, max_length=max_length, temperature=temperature, num_return_sequences=1 ) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) generated_texts.append(generated_text) return generated_texts button_style = """ """ DEVICE = 'cpu' tokenizer_path = "sberbank-ai/rugpt3small_based_on_gpt2" model = torch.load('srcs/gpt_weights.pth').to(DEVICE) tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path) st.markdown(""" """, unsafe_allow_html=True) st.write("## Text generator") st.page_link("app.py", label="Home", icon='🏠') st.markdown( """ This streamlit-app can generate text using your prompt """ ) # Ввод пользовательского prompt prompt = st.text_area("Enter your prompt:") # Параметры генерации max_length = st.slider("Max length of generated text:", min_value=10, max_value=500, value=100, step=10) num_generations = st.slider("Number of generations:", min_value=1, max_value=10, value=3, step=1) temperature = st.slider("Temperature:", min_value=0.1, max_value=2.0, value=1.0, step=0.1) try: if st.button("Generate text"): start_time = time.time() generated_texts = generate_text(model, tokenizer, prompt, max_length, num_generations, temperature) end_time = time.time() st.subheader("Сгенерированный текст:") for i, text in enumerate(generated_texts, start=1): st.write(f"Генерация {i}:\n{text}") generation_time = end_time - start_time st.write(f"\nВремя генерации: {generation_time:.2f} секунд") st.markdown(button_style, unsafe_allow_html=True) # Применяем стиль к кнопке st.markdown( """ """, unsafe_allow_html=True, ) except: st.write('Модель в разработке ( ノ ゚ー゚)ノ( ノ ゚ー゚)ノ( ノ ゚ー゚)ノ( ノ ゚ー゚)ノ')