Delete pages/gpt_v2.py

pages/gpt_v2.py

@@ -1,41 +0,0 @@
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-import torch
-import streamlit as st
-
-model = GPT2LMHeadModel.from_pretrained(
-    'sberbank-ai/rugpt3small_based_on_gpt2',
-    output_attentions = False,
-    output_hidden_states = False,
-)
-
-tokenizer = GPT2Tokenizer.from_pretrained('sberbank-ai/rugpt3small_based_on_gpt2')
-
-# Вешаем сохраненные веса на нашу модель
-model.load_state_dict(torch.load('model.pt', map_location=torch.device('cpu')))
-
-prompt = st.text_input('Введите текст prompt:')
-length = st.slider('Длина генерируемой последовательности:', 1, 256, 16)
-num_samples = st.slider('Число генераций:', 1, 6, 1)
-temperature = st.slider('Температура:', 1.0, 6.0, 1.0)
-selected_text = st.empty()
-
-def generate_text(model, tokenizer, prompt, length, num_samples, temperature, selected_text):
-    input_ids = tokenizer.encode(prompt, return_tensors='pt')
-    output_sequences = model.generate(
-        input_ids=input_ids,
-        max_length=length,
-        num_return_sequences=num_samples,
-        temperature=temperature
-    )
-
-    generated_texts = []
-    for output_sequence in output_sequences:
-        generated_text = tokenizer.decode(output_sequence, clean_up_tokenization_spaces=True)
-        generated_texts.append(generated_text)
-
-    selected_text.slider('Выберите текст:', 1, num_samples, 1)
-    return generated_texts[selected_text.value-1]
-
-if st.button('Сгенерировать текст'):
-    text = generate_text(model, tokenizer, prompt, length, num_samples, temperature, selected_text)
-    st.write(text)