wsb1224
67b2dac23f
迁移系统加固 — 加 advisory lock 防并发，失败回滚并中止启动（原来是 catch-and-continue） 安全漏洞 — Settings/History API 权限收紧，API key 返回掩码值，海报 case 所有权校验 依赖缺失 — requirements.txt 补齐 python-pptx/openai/Pillow，Dockerfile 改为统一安装 海报鉴权下载 — 前端全部改用 authenticated blob，不再 window.open 无 token URL LLM 配置分离 — 海报文案读取 poster_llm_*（不再复用 ppt_llm_*），支持 config namespace 图片生成器 — 兼容 b64_json 和 URL 两种响应格式，追踪 generation_mode/provider/model 种子数据 — 新环境自动获得 2 个海报模板 + 2 个文案模板
92 lines
3.6 KiB
Python
92 lines
3.6 KiB
Python
"""文案生成器 — 模板模式 + AI 模式双模式。"""
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import re
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import json
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import logging
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logger = logging.getLogger(__name__)
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class CopyGenerator:
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"""文案生成器。"""
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def generate_template_copy(self, template_content: str, product_rules: dict, customer_data: dict) -> dict:
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"""模板模式：本地变量替换。
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参数:
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template_content: 含 {{变量}} 占位符的文案模板
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product_rules: 产品规则（manual_parsed_rules）
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customer_data: 客户数据（confirmed_data）
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返回:
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{"headline": "...", "body": "...", "call_to_action": "..."}
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"""
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# 构建变量映射
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variables = {}
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# 产品信息
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if product_rules:
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variables["product_name"] = product_rules.get("product_name", "")
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features = product_rules.get("features", [])
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for i, feat in enumerate(features[:3], 1):
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variables[f"feature_{i}_title"] = feat.get("title", "")
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variables[f"feature_{i}_summary"] = feat.get("summary", "")
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variables["currency_options"] = ", ".join(product_rules.get("currency_options", []))
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# 客户数据
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if customer_data:
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variables["age"] = str(customer_data.get("age", ""))
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variables["currency"] = customer_data.get("currency", "")
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variables["sum_assured"] = str(customer_data.get("sum_assured", ""))
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variables["annual_premium"] = str(customer_data.get("annual_premium", ""))
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variables["coverage_period"] = customer_data.get("coverage_period", "")
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# 替换占位符
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result = template_content
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for key, value in variables.items():
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result = result.replace("{{" + key + "}}", value)
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# 尝试按段落拆分为 headline/body/call_to_action
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lines = [l.strip() for l in result.split("\n") if l.strip()]
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return {
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"headline": lines[0] if lines else "",
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"body": "\n".join(lines[1:-1]) if len(lines) > 2 else lines[1] if len(lines) > 1 else "",
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"call_to_action": lines[-1] if len(lines) > 1 else "",
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}
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async def generate_ai_copy(self, product_rules: dict, customer_data: dict, style: str = "专业") -> dict:
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"""AI 模式：调用 LLM 生成营销文案。
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返回:
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{"headline": "...", "body": "...", "call_to_action": "..."}
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"""
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from insurance.ppt.llm_client import poster_llm_client
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system_prompt = f"""你是一位专业的保险营销文案撰写人。
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根据以下产品信息和客户数据，生成一张保险营销海报的文案。
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要求：
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1. 标题（headline）：简短有力，8字以内
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2. 正文（body）：突出产品亮点与客户需求的匹配，50-100字
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3. 行动号召（call_to_action）：引导客户咨询，15字以内
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4. 风格：{style}
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5. 必须基于真实数据，不得虚构收益数字
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以 JSON 格式返回（不要包含 markdown 代码块标记）。"""
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user_prompt = f"""
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产品信息：{json.dumps(product_rules, ensure_ascii=False)}
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客户数据：{json.dumps(customer_data, ensure_ascii=False)}"""
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result, _response = await poster_llm_client.structured_output(
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user_prompt, system_prompt,
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schema={
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"type": "object",
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"properties": {
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"headline": {"type": "string"},
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"body": {"type": "string"},
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"call_to_action": {"type": "string"},
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},
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"required": ["headline", "body", "call_to_action"],
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},
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)
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return result
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