616 lines
26 KiB
Python
616 lines
26 KiB
Python
# spacy_training/scripts/predict.py
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import re
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import cn2an
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import os.path
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from tqdm import tqdm
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from pathlib import Path
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# 单位与连接符定义
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MAX_ITEM_OFFSET = 10
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UNIT_PATTERN = "个|张|片|块|条|幅|根"
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SIZE_UNITS_PATTERN = r'mm|cm|dm|m'
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SIZE_JOIN_PATTERN = "xX×"
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NUMS_PATTERN = r'\d+(?:\.\d+)' # 修复：原缺少 ? 导致整数不匹配
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CNT_PATTERN = rf'{NUMS_PATTERN}?[^克+\s\d-]*'
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EXCEPT_TEXT = "解析失败"
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INDEX_COL = '序号'
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base_colume = ['解析备注', '尺寸备注', '描述', '异常信息']
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mapping_size = {
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"width_mm": "长",
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"height_mm": "宽",
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# "original_size": "原始尺寸",
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# "total_quantity": "总数量",
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"style_count": "款数",
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"quantity_per_style": "数量",
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"unit": "单位",
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"描述": "描述",
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"解析备注": "解析备注",
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"size_unit": "尺寸单位",
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"exception_msg": '异常信息'
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}
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import spacy
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import pandas as pd
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# 将 doc 转换为字典
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def doc_to_dict(doc):
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return {
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"text": doc.text,
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"entities": [
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{
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"text": ent.text,
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"label": ent.label_,
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"start": ent.start_char,
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"end": ent.end_char
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}
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for ent in doc.ents
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],
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}
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def predict_file(df, model_path="./spacy_training/model", key='备注'):
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print(model_path)
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nlp = spacy.load(model_path)
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if "sentencizer" not in nlp.pipe_names:
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nlp.add_pipe("sentencizer")
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# 统一表头空格
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df.columns = df.columns.str.strip()
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key = key.strip()
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for i, row in tqdm(df.iterrows(), total=len(df)):
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order = str(row[key]).strip()
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base_row = {key: val for key, val in row.items()} # 复制原行数据
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yield order, base_row, doc_to_dict(nlp(order))
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# doc = nlp(text)
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# print(f"\n🔤 文本: {text}")
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# import json; print(json.dumps(doc_to_dict(doc), indent=4, ensure_ascii=False))
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# break
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from dataclasses import dataclass
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@dataclass
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class StructuredOrder:
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key: str
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width_mm: float
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height_mm: float
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original_size: str
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style_count: int
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quantity_per_style: float
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unit: str
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total_quantity: float
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original_qty: str
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size_position: list
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qty_position: list
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exception_msg: str
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calc_type: str
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size_unit: str
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meta: dict
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def expand_to_structured(raw_pairs, size_unit=''):
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unit_map = {
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'百': 100, '千': 1000, '万': 10000,
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'百万': 1000000, '千万': 10000000, '亿': 100000000,
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}
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UNIT_REGEX = re.compile(rf'({UNIT_PATTERN})$')
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exception_msg = []
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def extract_multiplier(text):
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"""
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从字符串中提取数值乘数，支持：
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- 阿拉伯数字 + 中文单位：3.5万 → 35000
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- 纯中文数字 + 单位：三万五千 → 35000
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- 纯中文：两千万 → 20000000
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"""
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# 去除末尾单位（保留前面的部分）
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clean = UNIT_REGEX.sub('', text.strip())
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# 如果去单位后为空，原字符串可能是纯单位（如“万”），默认系数为1
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if not clean:
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matched_unit = UNIT_REGEX.search(text)
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if matched_unit:
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unit = matched_unit.group(1)
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return unit_map.get(unit, 1.0)
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return 1.0
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# 尝试直接转阿拉伯数字
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try:
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return float(clean)
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except ValueError:
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pass
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# 尝试转中文数字（如“三万五千”虽然不合理，但“三万”可以）
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try:
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# 注意：cn2an 可以直接处理“三万五千”这种
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num = cn2an.cn2an(clean, "smart") # smart 模式支持混合写法
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if isinstance(num, (int, float)):
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# 检查原字符串是否有单位后缀（比如“三万”中的“万”已被去除，需补回）
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matched_unit = UNIT_REGEX.search(text)
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if matched_unit:
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unit = matched_unit.group(1)
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num *= unit_map[unit]
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return float(num)
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except Exception:
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exception_msg.append(f"尺寸解析异常：{text}")
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pass
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return 1.0
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def parse_size(size_str):
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join_str = ''.join(re.findall(f"[{SIZE_JOIN_PATTERN}]+", size_str))
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if len(join_str) == 0 or re.search('比例|等高|等比|等宽', size_str):
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result = f'异常值：{size_str}'
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return result, result, size_unit
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size_str = size_str.replace(join_str, "x")
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match = re.match(rf'({NUMS_PATTERN}?)([a-z]+)?[{SIZE_JOIN_PATTERN}]({NUMS_PATTERN}?)([a-z]+)?', size_str, re.IGNORECASE)
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if not match:
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return None, None, size_unit
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w_val, w_unit, h_val, h_unit = match.groups()
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unit = (w_unit or h_unit or size_unit).lower()
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conv = {'m': 1000, 'dm': 100, 'cm': 10, 'mm': 1}
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if unit not in conv:
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print(f'未知单位: {unit} size_str:{size_str} size_unit:{size_unit}')
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exception_msg.append(f"尺寸解析异常：{size_str}, 未知单位: {unit}")
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return None, None, unit
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w_mm = conv[unit] * float(w_val)
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h_mm = conv[unit] * float(h_val)
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return round(w_mm, 3), round(h_mm, 3), unit
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def parse_quantity(qty_str):
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qty_str = "".join(qty_str.split())
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multi_match = re.match((
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rf'[共计]?(.+?)款\w*?([共计]?[\d.一二两三四五六七八九十百千万亿]+)({UNIT_PATTERN})'
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), qty_str, re.IGNORECASE)
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if multi_match:
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style_part, qty_part = multi_match.group(1), multi_match.group(2)
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if qty_part[0] in '共计':
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qty_part = qty_part[1:]
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calc_type = 'total'
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else:
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calc_type = 'single'
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style_count = int(extract_multiplier(style_part))
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quantity_per_style = int(extract_multiplier(qty_part))
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unit = multi_match.group(3)
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return {"style_count": style_count, "quantity_per_style": quantity_per_style, "unit": unit, "calc_type": calc_type}
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single_match = re.match(rf'(.+?)({UNIT_PATTERN})', qty_str, re.IGNORECASE)
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if single_match:
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qty_part = single_match.group(1)
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if qty_part[0] in '共计':
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qty_part = qty_part[1:]
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calc_type = 'total'
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else:
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calc_type = 'single'
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quantity_per_style = int(extract_multiplier(qty_part))
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unit = single_match.group(2)
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return {"style_count": 1, "quantity_per_style": quantity_per_style, "unit": unit, "calc_type": calc_type}
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return {"style_count": 0, "quantity_per_style": 0, "unit": "个"}
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structured = []
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for size_info, qty_info in raw_pairs:
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s_start, s_end, s_text, *_ = size_info
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q_start, q_end, q_text, *_ = qty_info
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if s_text == EXCEPT_TEXT:
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width_mm, height_mm = EXCEPT_TEXT, EXCEPT_TEXT
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else:
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size_parsed = parse_size(s_text)
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if size_parsed:
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width_mm, height_mm, size_unit = size_parsed
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else:
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width_mm, height_mm, size_unit = None, None, None
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if q_text == EXCEPT_TEXT:
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style_count, quantity_per_style, unit = 0, EXCEPT_TEXT, EXCEPT_TEXT
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else:
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qty_parsed = parse_quantity(q_text)
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style_count, quantity_per_style, unit = qty_parsed["style_count"], qty_parsed["quantity_per_style"], qty_parsed["unit"]
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structured.append(StructuredOrder(
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key=f"{s_start}{s_end}{s_text}",
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width_mm=width_mm,
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height_mm=height_mm,
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original_size=s_text,
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style_count=style_count,
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quantity_per_style=quantity_per_style,
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unit=unit,
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total_quantity=style_count * quantity_per_style if style_count and style_count else None,
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original_qty=q_text,
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size_position=[s_start, s_end],
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qty_position=[q_start, q_end],
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exception_msg="; ".join(exception_msg),
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calc_type=qty_parsed.get('calc_type', None),
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size_unit=size_unit,
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meta={
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"size_text_except": s_text if s_text == EXCEPT_TEXT or (not width_mm and not height_mm) else None,
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"qty_text_except": q_text if q_text == EXCEPT_TEXT else None,
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}
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))
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return structured
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def decode(doc_dict_list, is_horizontal=False):
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ent_group = {
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# "单号": ["单号"],
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"刮刮膜尺寸": ["刮刮膜尺寸"],
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# "产品": ["产品"],
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# "材质": ["材质"],
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# "工艺": ["工艺"],
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# "用户": ["用户信息"],
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# "加急": ["加急"],
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"数量": ["尺寸", "数量"],
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}
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columns = list(ent_group.keys())
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columns.remove('数量')
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columns.extend(base_colume)
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group_reflect = {v: k for k, vs in ent_group.items() for v in vs}
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result_items = []
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exception_list = []
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for doc_dict in doc_dict_list:
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sentence = doc_dict["text"]
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entities = doc_dict["entities"]
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is_except = False
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# 强关联实体需要进行组合处理
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# ======================================== 实体分组 ========================================
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ent_group_dict = {k: [] for k in ent_group.keys()}
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base_ent_group_dict = {}
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for ent in entities:
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start, end, label, text = ent["start"], ent["end"], ent["label"], ent["text"]
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group_name = group_reflect[label]
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ent_group_dict[group_name].append((start, end, text, label))
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base_ent_group_dict[group_name] = text
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result = []
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# 解析出尺寸和数量的组合
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# ======================================== 解析尺寸和数量 ========================================
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group_list = []
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size_and_qty_list = [[], []]
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# 数量需要和尺寸进行组合处理
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base_ent_group_dict.pop("数量", None)
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for cnt_item in ent_group_dict["数量"]:
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size_info, qty_info = size_and_qty_list
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label = cnt_item[-1]
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if label == '数量':
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qty_info.append(cnt_item)
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elif label == '尺寸':
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start, end, text, _label = cnt_item
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if f'-({text[:2]}' in sentence:
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continue
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if qty_info:
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group_list.append(size_and_qty_list)
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size_and_qty_list = [[], []]
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size_info, qty_info = size_and_qty_list
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size_info.append(cnt_item)
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# 存在数量和尺寸
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if size_and_qty_list[0] or size_and_qty_list[1]:
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group_list.append(size_and_qty_list)
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# 如果只有一对尺寸，有可能反过来描述
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if len(group_list) == 2:
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_1, qty_info = group_list[0]
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size_info, _2 = group_list[1]
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if not _1 and not _2:
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group_list[:] = [[qty_info, size_info]]
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# print(f'订单中尺寸和数量组合：{group_list}')
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# 解析出尺寸和数量的组合
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# ======================================== 解析长、宽、款数、数量 ========================================
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size_unit = ''
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for size_info, qty_info in group_list:
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units = set(''.join(re.findall(SIZE_UNITS_PATTERN, ent[2])) for ent in size_info)
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units = [unit for unit in units if unit]
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if len(units) == 1:
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size_unit = units[0]
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size_and_qty_parsed_result = []
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for size_info, qty_info in group_list:
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size_len, qty_len = len(size_info), len(qty_info)
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if size_len == 0:
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print(f'解析异常，尺寸为空，数量为{qty_info}，原文：{sentence}', entities)
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is_except = True
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# 起始位置、结束位置、尺寸文本、尺寸标签
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qty_info = [(0, 0, EXCEPT_TEXT, '数量')]
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if qty_len == 0:
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print(f'解析异常，数量为空，尺寸为{size_info}，原文：{sentence}', entities)
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is_except = True
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# 起始位置、结束位置、尺寸文本、尺寸标签
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size_info = [(0, 0, EXCEPT_TEXT, '尺寸')]
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# continue
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# print('size_info, qty_info', size_info, qty_info)
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if size_len == qty_len:
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decode_desc = "单尺寸、单款式描述的订单" if size_len == 1 else "多尺寸、多款式描述一一匹配的订单"
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for size, qty in zip(size_info, qty_info):
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expand = expand_to_structured([(size, qty)], size_unit) or []
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exceptions = [item.meta for item in expand if item.meta.get('size_text_except') or item.meta.get('qty_text_except')]
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if exceptions: print("解析异常", exceptions, '原文', sentence, entities)
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for item in expand:
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parsed_item = item.__dict__.copy()
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parsed_item['描述'] = f"{size[2]}|{qty[2]}"
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parsed_item['解析备注'] = decode_desc
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parsed_item['size_len'] = size_len
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parsed_item['qty_len'] = qty_len
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size_and_qty_parsed_result.append(parsed_item)
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elif size_len>1 and qty_len>1:
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if size_len > qty_len:
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total_qty = []
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for size, qty in zip(size_info, qty_info):
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expand = expand_to_structured([(size, qty)], size_unit) or []
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for item in expand:
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total_qty.append(item.style_count)
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if size_len == sum(total_qty):
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decode_desc = "多尺寸、多款式描述:多款式描述和尺寸相等的订单"
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item = []
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for idx, qty in enumerate(total_qty):
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item.extend(qty_info[idx] for _ in range(qty))
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print('解析：', size_info, qty_info, item)
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# breakpoint()
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qty_info = item
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qty_len = len(qty_info)
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else:
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# qty_info = []
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decode_desc = f"匹配异常：多尺寸、多款式描述的订单: {size_info} {qty_info}"
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else:
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# qty_info = []
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decode_desc = f"匹配异常：多尺寸、多款式描述的订单: {size_info} {qty_info}"
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for size, qty in zip(size_info, qty_info):
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expand = expand_to_structured([(size, qty)], size_unit) or []
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exceptions = [item.meta for item in expand if item.meta.get('size_text_except') or item.meta.get('qty_text_except')]
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if exceptions: print("解析异常", exceptions, '原文', sentence, entities)
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for item in expand:
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parsed_item = item.__dict__.copy()
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parsed_item['style_count'] = 1
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parsed_item['描述'] = f"{size[2]}|{qty[2]}"
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parsed_item['解析备注'] = decode_desc
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parsed_item['size_len'] = size_len
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parsed_item['qty_len'] = qty_len
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size_and_qty_parsed_result.append(parsed_item)
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else:
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# 多尺寸-单数量、单尺寸-多数量 告警多尺寸-多数量
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iter_item = ((size, qty) for size in size_info for qty in qty_info)
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if size_len > 1 and qty_len == 1:
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decode_desc = "多尺寸、单款式描述的订单"
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elif size_len == 1 and qty_len > 1:
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decode_desc = "单尺寸、多款式描述的订单"
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else:
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decode_desc = f"异常：多尺寸、多款式描述的订单: {size_info} {qty_info}"
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for item in iter_item:
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size, qty = item
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expand = expand_to_structured([(size, qty)], size_unit) or []
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exceptions = [item.meta for item in expand if item.meta.get('size_text_except') or item.meta.get('qty_text_except')]
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if exceptions: print("解析异常", exceptions, '原文', sentence, entities)
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for item in expand:
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parsed_item = item.__dict__.copy()
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parsed_item['描述'] = f"{size[2]}|{qty[2]}"
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parsed_item['解析备注'] = decode_desc
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parsed_item['size_len'] = size_len
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parsed_item['qty_len'] = qty_len
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size_and_qty_parsed_result.append(parsed_item)
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result.extend(size_and_qty_parsed_result)
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for item in result:
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# print('item', item)
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new_item = base_ent_group_dict.copy()
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for key in mapping_size:
|
||
col_name = mapping_size[key]
|
||
new_item[col_name] = item[key]
|
||
columns.append(col_name)
|
||
size_len = item.pop('size_len', None)
|
||
qty_len = item.pop('qty_len', None)
|
||
calc_type = item.pop('calc_type', None)
|
||
|
||
if size_len > 1 and calc_type == 'total':
|
||
new_item[f'数量'] = f"警告：异常值(总计值:{new_item[f'数量']})"
|
||
|
||
if size_len == 1 or size_len == qty_len:
|
||
pass
|
||
# new_item[f'解析备注'] = '单尺寸的订单'
|
||
else:
|
||
if size_len == new_item['款数']:
|
||
new_item[f'款数'] = 1
|
||
# new_item[f'解析备注'] = '尺寸数量和款数相同的订单'
|
||
elif new_item['款数'] == 1:
|
||
new_item[f'款数'] = 1
|
||
# new_item[f'解析备注'] = '单款的订单'
|
||
else:
|
||
new_item[f'款数'] = "异常值：款数和尺寸数不一致"
|
||
new_item[f'数量'] = "异常值：款数和尺寸数不一致"
|
||
# new_item[f'解析备注'] = '多尺寸并且和款数不同的订单'
|
||
|
||
result_items.append(new_item)
|
||
# print(json.dumps(new_item, indent=4, ensure_ascii=False))
|
||
|
||
if not result:
|
||
if base_ent_group_dict:
|
||
base_ent_group_dict['是否异常'] = is_except
|
||
base_ent_group_dict[f'解析备注'] = '未解析到实际尺寸的订单1'
|
||
result_items.append(base_ent_group_dict.copy())
|
||
else:
|
||
result_items.append({
|
||
"是否异常": is_except,
|
||
"解析备注": "未解析到任何实体"
|
||
})
|
||
|
||
return exception_list, columns, result_items
|
||
|
||
|
||
def parse_finance_data(file_path, target_index, is_horizontal, sheet_name="Sheet1"):
|
||
|
||
df = pd.read_excel(file_path, dtype=str, sheet_name=sheet_name)
|
||
total_amount = len(df)
|
||
|
||
items = predict_file(df, './app/resources/models/订单尺寸识别/best_model', target_index)
|
||
# items = predict_file(file_path, './resources/models/lintao/best_model', target_index)
|
||
exception_list, results, outputs_columns = [], [], []
|
||
origin_item = None
|
||
|
||
parsed_dict = {}
|
||
mapping_size_values = mapping_size.values()
|
||
|
||
for text, origin_item, item in items:
|
||
|
||
decode_result = []
|
||
|
||
base_info = {}
|
||
result_index = parsed_dict.get(f"{text}_index")
|
||
if result_index:
|
||
exceptions, columns = [], []
|
||
try:
|
||
decode_item = parsed_dict[text][result_index]
|
||
for key in origin_item:
|
||
if key in mapping_size_values:
|
||
continue
|
||
decode_item[key] = origin_item[key]
|
||
decode_item['尺寸备注'] = '多组尺寸解析'
|
||
parsed_dict[f"{text}_index"] += 1
|
||
continue
|
||
except IndexError:
|
||
print(f'解析到的尺寸不足：{text}，{result_index}， {parsed_dict[text]}')
|
||
base_item = parsed_dict[text][0]
|
||
for col in base_colume:
|
||
base_info[col] = base_item.get(col, '')
|
||
base_info["长"] = '异常值：没有解析到那么多尺寸'
|
||
|
||
decode_items = []
|
||
parsed_dict[f"{text}_index"] += 1
|
||
else:
|
||
exceptions, columns, decode_items = decode([item], is_horizontal) or []
|
||
|
||
# if '(250731202428388557)' in text:
|
||
# print(result_index, text, exceptions, columns, decode_items)
|
||
# breakpoint()
|
||
|
||
if len(columns) > len(outputs_columns):
|
||
outputs_columns = columns
|
||
|
||
if len(decode_items) > 1:
|
||
for item in decode_items:
|
||
item['尺寸备注'] = '多组尺寸解析'
|
||
|
||
for idx, decode_item in enumerate(decode_items):
|
||
base_row = origin_item.copy()
|
||
if idx and INDEX_COL in base_row: base_row[INDEX_COL] = ''
|
||
# base_row = origin_item.copy() if idx == 0 else {}
|
||
base_row.update(decode_item)
|
||
is_except = base_row.pop('是否异常', False)
|
||
if is_except:
|
||
for col in base_colume:
|
||
origin_item[col] = base_row.get(col, '')
|
||
decode_result.append(origin_item)
|
||
exception_list.append(base_row)
|
||
else:
|
||
decode_result.append(base_row)
|
||
if not decode_items:
|
||
if base_info:
|
||
origin_item.update(base_info)
|
||
|
||
decode_result.append(origin_item)
|
||
else:
|
||
if is_horizontal:
|
||
new_decode_result = decode_result[0].copy()
|
||
for idx, item in enumerate(decode_result[1:], 1):
|
||
for key in mapping_size_values:
|
||
new_key = f"{key}{idx}"
|
||
new_decode_result[new_key] = item[key]
|
||
if new_key not in outputs_columns:
|
||
outputs_columns.append(new_key)
|
||
decode_result = [new_decode_result]
|
||
|
||
if text not in parsed_dict:
|
||
parsed_dict[text] = decode_result
|
||
parsed_dict[f"{text}_index"] = 0
|
||
parsed_dict[f"{text}_index"] += 1
|
||
|
||
# if decode_result:
|
||
# results.append(decode_result[0])
|
||
if len(decode_items) > 1:
|
||
for item in decode_result[1:]:
|
||
item['尺寸备注'] = '复制新增：多组尺寸解析'
|
||
results.extend(decode_result)
|
||
|
||
# break
|
||
|
||
print('横向解析', outputs_columns)
|
||
if origin_item:
|
||
# outputs_columns.
|
||
_outputs_columns = list(origin_item.keys()) + outputs_columns
|
||
col_set = set()
|
||
outputs_columns = []
|
||
|
||
for col in _outputs_columns:
|
||
if col not in col_set:
|
||
col_set.add(col)
|
||
outputs_columns.append(col)
|
||
|
||
df = pd.DataFrame(results, columns=outputs_columns)
|
||
|
||
file_name = os.path.basename(file_path).replace(".xlsx", '')
|
||
filename = file_name + '_解析' + ('_横向排列' if is_horizontal else '_纵向排列')
|
||
|
||
upload_dir = Path("outputs")
|
||
upload_dir.mkdir(parents=True, exist_ok=True)
|
||
save_file_path = upload_dir / f'{filename}_1.xlsx'
|
||
|
||
# 使用 ExcelWriter 同时写入多个 sheet
|
||
with pd.ExcelWriter(save_file_path, engine='openpyxl') as writer:
|
||
df.to_excel(writer, sheet_name='正常解析', index=False)
|
||
if exception_list:
|
||
except_df = pd.DataFrame(exception_list, columns=outputs_columns)
|
||
except_df.to_excel(writer, sheet_name='异常解析', index=False)
|
||
|
||
print(f'解析结果保存在：{save_file_path}')
|
||
return save_file_path, total_amount
|
||
|
||
|
||
if __name__ == "__main__":
|
||
|
||
# print(expand_to_structured([[(0,1,'600x50cm','r'), (0,1,'3款各1张','e')]]))
|
||
# exit()
|
||
# predict("Apple is opening a new office in Tokyo.")
|
||
# predict("Google hired Sarah Connor from Berlin last year.")
|
||
file_info = ['d:/会计组/新领图8月.xlsx', '备注']
|
||
# file_info = ['d:/会计组/即客2025年8月尺寸整理.xlsx', '系统文件名']
|
||
file_info = ['d:/会计组/8月订单明细9.6.xlsx', '文件名']
|
||
file_info = ['d:/会计组/ZHX-8月订单明细.xlsx', '文件名']
|
||
file_info = ['d:/会计组/国税数据源/智韬2025年8月尺寸整理(1).xlsx', '系统文件名']
|
||
file_info = ['d:/会计组/彩印通8月数码9.23.xlsx', 'ERP系统文件名']
|
||
file_info = ['d:/会计组/CYT8月明细9.24.xlsx', 'ERP系统文件名']
|
||
file_info = ['d:/会计组/9.1-9.26.xlsx', '备注']
|
||
file_info = ['d:/会计组/JD.xlsx', '文件名']
|
||
file_info = ['d:/会计组/泰州即客2025年9月尺寸整理.xlsx', '系统文件名']
|
||
file_info = ['d:/会计组/ZHX需拆明细9月.xlsx', '文件名', False]
|
||
file_info = ['d:/会计组/七彩2024年9月账单尺寸整理.xlsx', '系统文件名', True]
|
||
file_info = ['d:/会计组/艾印图文2024年9月账单尺寸整理.xlsx', '系统文件名', True]
|
||
file_info = ['d:/会计组/9月转印.xlsx', 'erp', True]
|
||
file_info = ['d:/会计组/9月CYT.xlsx', '文件名', False]
|
||
file_info = ['d:/会计组/智韬2025年9月尺寸整理.xlsx', '系统文件名', True]
|
||
file_info = ['d:/会计组/9月UV转印贴.xlsx', '文件名', True]
|
||
file_info = ['d:/会计组/彩印通2025年9月（数码）.xlsx', '文件名', True]
|
||
file_info = ['d:/会计组/9月名片.xlsx', '文件名', True]
|
||
file_info = ['d:/会计组/9月不干胶.xlsx', '文件名', True]
|
||
file_path, target_index, is_horizontal = file_info
|
||
parse_finance_data(file_path, target_index, is_horizontal)
|
||
|
||
|