import re
import uuid
import math
from pathlib import Path
from typing import List, Dict, Any, Optional, Tuple
import pandas as pd
import chardet
from openpyxl import load_workbook
from app.config import MAX_HEADER_SCAN_ROWS, UPLOAD_DIR


def clean_value(val):
    """清理單一值，將 NaN/Inf 轉換為 None 以便 JSON 序列化"""
    if val is None:
        return None
    if isinstance(val, float):
        if math.isnan(val) or math.isinf(val):
            return None
    return val


def clean_dict(d: Dict) -> Dict:
    """清理字典中的所有 NaN/Inf 值"""
    return {k: clean_value(v) for k, v in d.items()}


def clean_records(records: List[Dict]) -> List[Dict]:
    """清理記錄列表中的所有 NaN/Inf 值"""
    return [clean_dict(r) for r in records]

# 欄位名稱對應表
COLUMN_MAPPING = {
    'dit': {
        'op_id': ['opportunity name', 'opportunity no', 'opportunity', 'op編號', 'op 編號', 'op_id', 'opid', '案件編號', '案號', 'opportunity id'],
        'erp_account': ['erp account', 'account no', 'erp account no', '客戶代碼', '客戶編號', 'erp_account'],
        'customer': ['account name', 'branding customer', '客戶', '客戶名稱', 'customer', 'customer name', '公司名稱'],
        'pn': ['product name', '料號', 'part number', 'pn', 'part no', 'part_number', '產品料號', 'stage/part'],
        'eau': ['eau quantity', 'eau quantity (pcs)', 'eau', '年預估量', 'annual usage', '預估用量'],
        'stage': ['stage', 'oppty product stage', '階段', 'status', '狀態', '專案階段'],
        'date': ['created date', '日期', 'date', '建立日期', 'create date']
    },
    'sample': {
        'sample_id': ['樣品訂單號碼', 'item', '樣品編號', 'sample_id', 'sample id', '編號'],
        'order_no': ['樣品訂單號碼', '單號', 'order_no', 'order no', '樣品單號', '申請單號'],
        'oppy_no': ['oppy no', 'oppy_no', '案號', '案件編號', 'opportunity no'],
        'cust_id': ['cust id', 'cust_id', '客戶編號', '客戶代碼', '客戶代號'],
        'customer': ['客戶名稱', '客戶簡稱', '客戶', 'customer', 'customer name'],
        'pn': ['item', 'type', '料號', 'part number', 'pn', 'part no', '產品料號', '索樣數量'],
        'qty': ['索樣數量pcs', '索樣數量 k', '數量', 'qty', 'quantity', '申請數量'],
        'date': ['需求日', '日期', 'date', '申請日期']
    },
    'order': {
        'order_id': ['項次', '訂單編號', 'order_id', 'order id'],
        'order_no': ['訂單單號', '訂單號', 'order_no', 'order no', '銷貨單號'],
        'cust_id': ['客戶編號', '客戶代碼', '客戶代號', 'cust_id', 'cust id'],
        'customer': ['客戶', '客戶名稱', 'customer', 'customer name'],
        'pn': ['type', '內部料號', '料號', 'part number', 'pn', 'part no', '產品料號'],
        'qty': ['訂單量', '數量', 'qty', 'quantity', '訂購數量', '出貨數量'],
        'status': ['狀態', 'status', '訂單狀態'],
        'amount': ['原幣金額(含稅)', '台幣金額(未稅)', '金額', 'amount', 'total', '訂單金額']
    }
}

class ExcelParser:
    def __init__(self):
        self.parsed_files: Dict[str, Dict] = {}

    def detect_encoding(self, file_path: Path) -> str:
        """偵測檔案編碼"""
        with open(file_path, 'rb') as f:
            result = chardet.detect(f.read(10000))
            return result.get('encoding', 'utf-8')

    def find_header_row(self, df: pd.DataFrame, file_type: str) -> int:
        """自動偵測表頭位置"""
        expected_columns = set()
        for variants in COLUMN_MAPPING[file_type].values():
            expected_columns.update([v.lower() for v in variants])

        for idx in range(min(MAX_HEADER_SCAN_ROWS, len(df))):
            row = df.iloc[idx]
            row_values = [str(v).lower().strip() for v in row.values if pd.notna(v)]

            # 檢查是否有匹配的欄位名稱
            matches = sum(1 for v in row_values if any(exp in v for exp in expected_columns))
            if matches >= 2:  # 至少匹配 2 個欄位
                return idx

        return 0  # 預設第一行為表頭

    def map_columns(self, df: pd.DataFrame, file_type: str) -> Dict[str, str]:
        """將 DataFrame 欄位對應到標準欄位名稱"""
        mapping = {}
        column_map = COLUMN_MAPPING[file_type]

        df_columns = [str(c).lower().strip() for c in df.columns]

        for standard_name, variants in column_map.items():
            for variant in variants:
                variant_lower = variant.lower()
                for idx, col in enumerate(df_columns):
                    if variant_lower in col or col in variant_lower:
                        mapping[df.columns[idx]] = standard_name
                        break
                if standard_name in mapping.values():
                    break

        return mapping

    def parse_file(self, file_path: Path, file_type: str) -> Tuple[str, Dict[str, Any]]:
        """解析 Excel/CSV 檔案"""
        file_id = str(uuid.uuid4())

        # 讀取檔案
        if file_path.suffix.lower() == '.csv':
            encoding = self.detect_encoding(file_path)
            df = pd.read_csv(file_path, encoding=encoding, header=None)
        else:
            df = pd.read_excel(file_path, header=None)

        # 找到表頭
        header_row = self.find_header_row(df, file_type)

        # 重新讀取，以正確的表頭
        if file_path.suffix.lower() == '.csv':
            df = pd.read_csv(file_path, encoding=encoding, header=header_row)
        else:
            df = pd.read_excel(file_path, header=header_row)

        # 欄位對應
        column_mapping = self.map_columns(df, file_type)
        df = df.rename(columns=column_mapping)

        # 只保留需要的欄位
        required_columns = list(COLUMN_MAPPING[file_type].keys())
        available_columns = [c for c in required_columns if c in df.columns]
        df = df[available_columns]

        # 清理資料
        df = df.dropna(how='all')

        # 產生預覽資料（清理 NaN 值以便 JSON 序列化）
        preview = clean_records(df.head(10).to_dict(orient='records'))

        # 儲存解析結果
        parsed_data = {
            'file_id': file_id,
            'file_type': file_type,
            'filename': file_path.name,
            'header_row': header_row,
            'row_count': len(df),
            'columns': list(df.columns),
            'preview': preview,
            'dataframe': df
        }

        self.parsed_files[file_id] = parsed_data

        return file_id, {k: v for k, v in parsed_data.items() if k != 'dataframe'}

    def get_parsed_data(self, file_id: str) -> Optional[pd.DataFrame]:
        """取得解析後的 DataFrame"""
        if file_id in self.parsed_files:
            return self.parsed_files[file_id].get('dataframe')
        return None

    def get_file_info(self, file_id: str) -> Optional[Dict]:
        """取得檔案資訊"""
        if file_id in self.parsed_files:
            data = self.parsed_files[file_id]
            return {k: v for k, v in data.items() if k != 'dataframe'}
        return None

# 全域實例
excel_parser = ExcelParser()