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Update app.py
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app.py
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import gradio as gr
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from transformers import pipeline
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#
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model="t-bank-ai/ruDialoGPT-small",
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tokenizer="t-bank-ai/ruDialoGPT-small",
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device=-1 # CPU
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)
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pipe_rugpt3 = pipeline(
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task="text-generation",
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model="ai-forever/rugpt3small_based_on_gpt2",
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tokenizer="ai-forever/rugpt3small_based_on_gpt2",
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device=-1 # CPU
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)
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# Функция обработки пользовательского запроса
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# Возвращает генерацию от обеих моделей
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def generate_responses(prompt: str):
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# Настройки генерации можно подкорректировать по потребностям
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kwargs = {
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"max_length": 200,
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"do_sample": True,
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"top_p": 0.9,
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"temperature": 0.7
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}
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out1 = pipe_dialo(prompt, **kwargs)[0]["generated_text"]
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out2 = pipe_rugpt3(prompt, **kwargs)[0]["generated_text"]
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return out1, out2
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# Gradio-интерфейс
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with gr.Blocks() as demo:
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gr.Markdown(
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txt = gr.Textbox(label=
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demo.launch()
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import gradio as gr
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import time
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from transformers import pipeline
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from datasets import load_dataset
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# Загрузка бесплатных русскоязычных моделей
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models = {
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'ruDialoGPT-small': pipeline('text-generation', model='t-bank-ai/ruDialoGPT-small', tokenizer='t-bank-ai/ruDialoGPT-small', device=-1),
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'ruDialoGPT-medium': pipeline('text-generation', model='t-bank-ai/ruDialoGPT-medium', tokenizer='t-bank-ai/ruDialoGPT-medium', device=-1),
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'ruGPT3-small': pipeline('text-generation', model='ai-forever/rugpt3small_based_on_gpt2', tokenizer='ai-forever/rugpt3small_based_on_gpt2', device=-1)
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}
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# Загрузка банковского датасета с диалогами
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bank_data = load_dataset('ai-lab/MBD', split='train')
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# Определяем колонку с диалогами
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col = next((c for c in bank_data.column_names if 'dialog' in c), None)
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if col is None:
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raise ValueError('В датасете не найдена колонка с диалогами')
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# Берём первые два примера для few-shot
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examples = [item[col] for item in bank_data.select(range(2))]
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# Функция построения запроса с CoT и few-shot примерами
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def build_prompt(question):
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few_shot = '\n\n'.join(f'Диалог:\n{ex}' for ex in examples)
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prompt = (
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f"{few_shot}\n\n"
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f"Вопрос: {question}\n"
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"Сначала подробно опишите рассуждения шаг за шагом, а затем дайте краткий связный ответ."
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)
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return prompt
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# Генерация ответов и снятие тайминга
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def generate(question):
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prompt = build_prompt(question)
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results = {}
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for name, pipe in models.items():
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start = time.time()
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out = pipe(prompt, max_length=300, do_sample=True, top_p=0.9, temperature=0.7)[0]['generated_text']
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elapsed = round(time.time() - start, 2)
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# Извлечение финального ответа после рассуждений
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if 'Ответ:' in out:
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answer = out.split('Ответ:')[-1].strip()
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else:
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answer = out.strip().split('\n')[-1]
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results[name] = {'answer': answer, 'time': elapsed}
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return results
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# Форматируем вывод для Gradio
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def format_outputs(question):
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res = generate(question)
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return (
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res['ruDialoGPT-small']['answer'], f"{res['ruDialoGPT-small']['time']}s",
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res['ruDialoGPT-medium']['answer'], f"{res['ruDialoGPT-medium']['time']}s",
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res['ruGPT3-small']['answer'], f"{res['ruGPT3-small']['time']}s"
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)
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# Интерфейс Gradio
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with gr.Blocks() as demo:
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gr.Markdown('## CoT на трёх моделях и банковский датасет')
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txt = gr.Textbox(label='Ваш вопрос', placeholder='Введите вопрос, связанный с банковскими услугами', lines=2)
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btn = gr.Button('Сгенерировать')
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out1 = gr.Textbox(label='ruDialoGPT-small Ответ')
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t1 = gr.Textbox(label='ruDialoGPT-small Время')
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out2 = gr.Textbox(label='ruDialoGPT-medium Ответ')
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t2 = gr.Textbox(label='ruDialoGPT-medium Время')
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out3 = gr.Textbox(label='ruGPT3-small Ответ')
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t3 = gr.Textbox(label='ruGPT3-small Время')
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btn.click(format_outputs, inputs=[txt], outputs=[out1, t1, out2, t2, out3, t3])
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demo.launch()
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