app.py

from transformers import GPTJForCausalLM, GPT2Tokenizer
import torch


# Загрузка токенизатора и модели
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-j-6B")
model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
from transformers import MarianMTModel, MarianTokenizer
# Функции для перевода
def translate_to_english(text):
    tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
    model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
    translated = model.generate(**tokenizer.prepare_seq2seq_batch([text], return_tensors="pt"))
    return tokenizer.decode(translated[0])

def translate_to_russian(text):
    tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ru")
    model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-ru")
    translated = model.generate(**tokenizer.prepare_seq2seq_batch([text], return_tensors="pt"))
    return tokenizer.decode(translated[0])

def generate_response(input_text):
    # Кодирование входного текста и генерация ответа
    input_ids = tokenizer.encode(input_text, return_tensors='pt')
    output = model.generate(input_ids, max_length=1500, num_return_sequences=1, pad_token_id=50256, 
                            num_beams=5, early_stopping=True)

    # Декодирование и возвращение ответа
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response

# Тестирование функции
input_text = "What is the capital of France?"
response = generate_response(input_text)
print(response)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
user_input = ""
while user_input.lower() != 'exit':
    user_input = input("You: ")
    if user_input.lower() != 'exit':
        user_input_english = translate_to_english(user_input)
        response_english = generate_response(user_input_english)
        response_russian = translate_to_russian(response_english)
        print(f"Bot: {response_russian}")



import gradio as gr

def predict(message, history):
    user_input_english = translate_to_english(message)
    response_english = generate_response(user_input_english)
    response_russian = translate_to_russian(response_english)
    return response_russian

demo = gr.ChatInterface(
    predict,
    title='LLM for MyChatENRU'
)

demo.launch()