AIyaodianzhushou/data-pipeline/crawlers/pharmacopoeia.py

"""
药典 PDF 解析器
将药典 PDF 文件解析为结构化的药品条目 JSON
"""
import json
import logging
from pathlib import Path
from typing import Optional

logger = logging.getLogger(__name__)


class PharmacopoeiaParser:
    def __init__(self, pdf_path: Path):
        self.pdf_path = pdf_path
        self.drug_entries = []

    def parse(self) -> list[dict]:
        """
        解析流程：
        1. PyMuPDF 提取文本 + 坐标
        2. 基于字体大小/位置识别标题层级
        3. 正则匹配药品条目边界
        4. 按条目切分 → 结构化输出

        Phase 2 实现。
        """
        logger.info(f"Parsing pharmacopoeia PDF: {self.pdf_path}")
        return []

    def _extract_text_with_position(self, page_num: int) -> list[dict]:
        """提取页面文本块及其位置信息"""
        return []

    def _detect_entry_boundaries(self, blocks: list[dict]) -> list[tuple[int, int]]:
        """检测药品条目边界"""
        return []

    def _parse_entry_sections(self, text: str) -> dict:
        """
        解析条目内各栏目：
        性状、鉴别、检查、含量测定、类别、贮藏、制剂
        """
        sections = {
            "性状": "", "鉴别": "", "检查": "",
            "含量测定": "", "类别": "", "贮藏": "", "制剂": "",
        }
        return sections

    def export_json(self, output_path: Path):
        """导出为 JSON 文件"""
        entries = self.parse()
        with open(output_path, "w", encoding="utf-8") as f:
            json.dump(entries, f, ensure_ascii=False, indent=2)
        logger.info(f"Exported {len(entries)} drug entries to {output_path}")