# coding=utf-8
import re

class OcrParser:
    @staticmethod
    def parse(ocr_lines):
        """
        解析 OCR 文本行，提取场站结构化数据。
        不依赖 LLM，使用规则和正则匹配。
        """
        result = {
            "station_name": "",
            "distance": "",
            "price": None,
            "piles": [],
            "parking": "",
            "tags": []
        }
        
        # 预处理：清洗无关字符
        clean_lines = []
        for line in ocr_lines:
            # 去除 "行 X: " 前缀
            text = re.sub(r"^行\s*\d+:\s*", "", line)
            # 去除 "(置信度: ...)" 后缀
            text = re.sub(r"\s*\(置信度:.*?\)\s*$", "", text)
            text = text.strip()
            if text:
                clean_lines.append(text)

        # 1. 场站名称 (Station Name)
        # 策略：通常是第一行，且不包含特定关键词（如km, /度, ￥等）
        for line in clean_lines:
            if len(line) < 2: continue
            if line.startswith("（") or line.startswith("(") or line == "…": continue
            
            # 排除明显属性行
            if any(k in line.lower() for k in ["km", "/度", "￥", "停车", "积分", "组团"]):
                continue
                
            # 排除纯数字
            if re.match(r"^\d+$", line): continue
            
            # 排除可能是充电枪信息的行 (例如 "闲2/4", "快", "慢")
            if re.search(r"\d+/\d+", line): continue
            if line in ["快", "慢", "超", "快充", "慢充", "超充"]: continue
            
            result["station_name"] = line
            break
            
        # 2. 距离 (Distance)
        for line in clean_lines:
            # 匹配 "7.4km", "17.4km"
            m = re.search(r"(\d+(\.\d+)?)\s*km", line, re.IGNORECASE)
            if m:
                result["distance"] = m.group(0)
                break
                
        # 3. 价格 (Price)
        for line in clean_lines:
            # 匹配 "0.7111/度", "1.2/度"
            m = re.search(r"(\d+\.\d+)(?=/度)", line)
            if m:
                try:
                    result["price"] = float(m.group(1))
                    break
                except: pass
        
        # 4. 充电枪信息 (Piles)
        # 规则：查找 "快", "慢", "超" 以及 "x/y" 格式
        # 示例： "快", "闲3/4" (跨行) 或 "快充 3/4" (同行)
        current_type = "未知"
        
        # 辅助：先尝试在单行内同时匹配类型和数量
        # 如果单行内有 "快" 和 "3/4"，则直接提取
        # 如果单行只有类型，更新 current_type
        # 如果单行只有 "3/4"，使用 current_type
        
        for line in clean_lines:
            # 更新当前类型上下文
            if "快" in line: current_type = "快"
            elif "慢" in line: current_type = "慢"
            elif "超" in line: current_type = "超"
            
            # 匹配 "3/4" 或 "闲3/4"
            # 排除价格行 (包含小数点，如 0.7111/度 这里的 / 前后也可能是数字，需小心)
            if "/度" in line: continue
            
            # 匹配分数结构：整数/整数
            # (?:闲)? 可选的前缀 "闲"
            m = re.search(r"(?:闲)?(\d+)/(\d+)", line)
            if m:
                # 再次确认不是价格（有时候 OCR 会把 . 漏掉，变成 07111/度，但通常会有 /度）
                # 简单校验：总数通常不会特别大，比如不会是 2024/12 (日期)
                try:
                    free = int(m.group(1))
                    total = int(m.group(2))
                    
                    # 过滤可能的日期或异常值
                    if total > 500: continue 
                    
                    result["piles"].append({
                        "type": current_type,
                        "free": free,
                        "total": total
                    })
                except: pass
                
        # 5. 停车信息 (Parking)
        for line in clean_lines:
            if "停车" in line:
                # 清理前缀符号，如 "·收费停车" -> "收费停车"
                clean_text = re.sub(r"^[·\.\sP]+", "", line)
                result["parking"] = clean_text
                break
                
        # 6. 标签 (Tags)
        # 收集除上述已解析字段外的简短关键词
        known_keywords = ["快", "慢", "超", "P", "组团", "积分"]
        for line in clean_lines:
            # 简单匹配一些常见 Tag
            if "积分" in line: result["tags"].append(line)
            elif "组团" in line: result["tags"].append("组团")
            elif line.strip() == "P": result["tags"].append("P")
            
        return result