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import gradio as gr |
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from transformers import pipeline |
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import torch |
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import logging |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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model_name = "microsoft/Phi-3-mini-4k-instruct" |
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try: |
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logger.info(f"Попытка загрузки модели {model_name}...") |
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generator = pipeline( |
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"text-generation", |
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model=model_name, |
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device=-1, |
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framework="pt", |
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max_length=150, |
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truncation=True, |
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model_kwargs={"torch_dtype": torch.float32} |
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) |
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logger.info("Модель успешно загружена.") |
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except Exception as e: |
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logger.error(f"Ошибка загрузки модели: {e}") |
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exit(1) |
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def respond(message, max_tokens=150, temperature=0.5, top_p=0.8): |
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prompt = f"You are a medical chatbot. User says: '{message}'. Provide diagnosis and treatment in English." |
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try: |
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logger.info(f"Генерация ответа для: {message}") |
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outputs = generator( |
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prompt, |
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max_length=max_tokens, |
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temperature=temperature, |
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top_p=top_p, |
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do_sample=True, |
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num_return_sequences=1 |
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) |
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response = outputs[0]["generated_text"].replace(prompt, "").strip() |
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if "Diagnosis" in response: |
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diagnosis = response.split("Diagnosis:")[1].split("Treatment:")[0].strip() |
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treatment = response.split("Treatment:")[1].strip() |
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response = f"Диагноз: {diagnosis}\nЛечение: {treatment}" |
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except Exception as e: |
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logger.error(f"Ошибка генерации ответа: {e}") |
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return f"Ошибка генерации: {e}" |
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return response |
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demo = gr.Interface( |
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fn=respond, |
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inputs=[ |
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gr.Textbox(label="Ваше сообщение", placeholder="Опишите симптомы (например, 'Болит горло')..."), |
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gr.Slider(minimum=50, maximum=300, value=150, step=10, label="Макс. токенов"), |
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gr.Slider(minimum=0.1, maximum=1.0, value=0.5, label="Температура"), |
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gr.Slider(minimum=0.1, maximum=1.0, value=0.8, label="Top-p") |
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], |
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outputs="text", |
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title="Медицинский чат-бот на базе Phi-3-mini-4k-instruct", |
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theme=gr.themes.Soft() |
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) |
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if __name__ == "__main__": |
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demo.launch() |
