103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
"""
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菜品名清洗工具
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用于将VLM输出的菜品名进行标准化处理，提升向量匹配准确率
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"""
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import re
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from typing import Dict
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# 同义词映射表（可根据实际情况扩展）
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SYNONYM_MAP: Dict[str, str] = {
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"西红柿": "番茄",
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"土豆": "马铃薯",
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"洋芋": "马铃薯",
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"青椒": "柿子椒",
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# 可继续添加...
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}
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def clean_dish_name(name: str) -> str:
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"""
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清洗菜品名：去除括号注释、前缀、英文等干扰信息
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示例：
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"宫保鸡丁（川菜）" -> "宫保鸡丁"
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"川菜-麻婆豆腐" -> "麻婆豆腐"
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"红烧肉 Braised Pork" -> "红烧肉"
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"""
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if not isinstance(name, str):
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return ""
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# 1. 去除括号及内容（中英文括号）
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name = re.sub(r"[（(].*?[)）]", "", name)
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name = re.sub(r"\[.*?\]", "", name)
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# 2. 去除常见前缀（菜系、地域等）
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prefixes = ["川菜", "粤菜", "鲁菜", "苏菜", "浙菜", "闽菜", "湘菜", "徽菜",
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"东北", "西北", "西南", "华南", "华北"]
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for prefix in prefixes:
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if name.startswith(prefix):
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name = name[len(prefix):]
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break
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# 3. 去除分隔符后的前缀（如 "川菜-宫保鸡丁"）
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name = re.sub(r"^[^-—]*[-—]", "", name)
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# 4. 去除英文部分（保留中文）
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name = re.sub(r"[a-zA-Z\s]+", "", name)
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# 5. 去除多余空格和标点
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name = re.sub(r"[,，、·\s]+", "", name)
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# 6. 去除可能的烹饪方式后缀（如果VLM违规输出）
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cooking_suffixes = ["炒制", "烹饪", "料理", "做法"]
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for suffix in cooking_suffixes:
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if name.endswith(suffix):
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name = name[:-len(suffix)]
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return name.strip()
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def normalize_dish_name(name: str) -> str:
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"""
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标准化菜品名：应用同义词映射
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示例：
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"西红柿炒鸡蛋" -> "番茄炒鸡蛋"
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"""
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cleaned = clean_dish_name(name)
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# 应用同义词替换
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for synonym, standard in SYNONYM_MAP.items():
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cleaned = cleaned.replace(synonym, standard)
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return cleaned
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def extract_main_dish_name(text: str) -> str:
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"""
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从VLM返回文本中提取主要菜品名（兼容多种输出格式）
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示例：
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"这是宫保鸡丁" -> "宫保鸡丁"
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"菜品：红烧肉" -> "红烧肉"
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"""
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if not text:
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return ""
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# 尝试匹配常见模式
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patterns = [
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r"菜品[:：]\s*([^，,。\n]+)",
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r"识别为[:：]\s*([^，,。\n]+)",
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r"这是\s*([^，,。\n]+)",
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r"应该是\s*([^，,。\n]+)",
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]
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for pattern in patterns:
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match = re.search(pattern, text)
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if match:
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return match.group(1).strip()
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# 如果没有匹配到，返回清洗后的整个文本
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return clean_dish_name(text)
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