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Update app.py
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app.py
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from transformers import AutoTokenizer, T5ForConditionalGeneration
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import torch
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import gradio as gr
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import json
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import re
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import
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#
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tokenizer = AutoTokenizer.from_pretrained(
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"cointegrated/rut5-base-multitask",
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legacy=False
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model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-base-multitask")
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try:
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prompt = f"""
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Сгенерируй SEO-метатеги
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Описание товара:
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{description.strip()}
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- description: до 160 символов, краткое описание преимуществ
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Формат вывода (строго JSON):
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{{"title": "...", "description": "..."}}
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"""
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inputs = tokenizer(prompt, return_tensors="pt",
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=200,
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do_sample=True,
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temperature=0.5,
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num_beams=3,
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eos_token_id=tokenizer.eos_token_id
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)
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result["title"] = result["title"][:60]
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result["description"] = result["description"][:160]
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return result
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except:
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pass
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# Фоллбэк
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title = re.sub(r'[^\w\s]', '', description)[:60]
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desc = re.sub(r'[^\w\s]', '', description)[:160]
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return {"title": title, "description": desc}
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except Exception as e:
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print(f"
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)
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if __name__ == "__main__":
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from transformers import AutoTokenizer, T5ForConditionalGeneration
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import torch
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import gradio as gr
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import re
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import json
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# Инициализация модели и токенизатора
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def load_model():
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tokenizer = AutoTokenizer.from_pretrained(
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"cointegrated/rut5-base-multitask",
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legacy=False # Отключаем предупреждения
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)
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model = T5ForConditionalGeneration.from_pretrained(
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"cointegrated/rut5-base-multitask",
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torch_dtype=torch.float16 # Экономия памяти
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)
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return tokenizer, model
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tokenizer, model = load_model()
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# Основная функция генерации
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def generate_seo_tags(description):
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try:
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# Подготовка промта
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prompt = f"""
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Сгенерируй SEO-метатеги title и description для товара.
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Требования:
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- Title: до 60 символов, содержит ключевые слова
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- Description: до 160 символов, описание преимуществ
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Описание товара:
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{description.strip()}
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Выведи только JSON:
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{{"title": "...", "description": "..."}}
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"""
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# Токенизация и генерация
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inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=200,
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num_beams=3,
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do_sample=True,
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temperature=0.5,
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early_stopping=True
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)
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# Декодирование и очистка результата
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result = tokenizer.decode(outputs[0], skip_special_tokens=True)
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json_match = re.search(r'\{.*\}', result, re.DOTALL)
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if json_match:
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seo_tags = json.loads(json_match.group())
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# Проверка и обрезка по длине
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seo_tags["title"] = seo_tags.get("title", "")[:60]
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seo_tags["description"] = seo_tags.get("description", "")[:160]
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return seo_tags
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except Exception as e:
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print(f"Ошибка: {e}")
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# Фоллбэк если что-то пошло не так
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clean_text = re.sub(r'[^\w\s]', '', description)
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return {
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"title": clean_text[:60],
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"description": clean_text[:160]
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}
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# Интерфейс Gradio
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with gr.Blocks(title="SEO Генератор") as app:
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gr.Markdown("## Генератор SEO-метатегов для товаров")
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with gr.Row():
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with gr.Column():
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input_text = gr.Textbox(
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label="Описание товара",
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placeholder="Введите описание товара...",
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lines=5
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)
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submit_btn = gr.Button("Сгенерировать")
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with gr.Column():
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output_json = gr.JSON(
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label="SEO-метатеги",
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interactive=False
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)
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submit_btn.click(
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fn=generate_seo_tags,
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inputs=input_text,
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outputs=output_json
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)
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if __name__ == "__main__":
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app.launch(
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server_name="0.0.0.0",
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server_port=7860,
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show_error=True
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)
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