#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Alibaba Store Data Extraction Automation Script (Playwright-based Scaffold)
This script is a robust template for logging into Alibaba International backend,
navigating to business summary page, picking dates for "上月" and "上上月", 
and extracting metrics from 'data.alibaba.com' and 'i.alibaba.com'.
"""

import os
import sys
import json
import time
from datetime import datetime, timedelta

try:
    from playwright.sync_api import sync_playwright
except ImportError:
    print("Warning: playwright library is not installed. To run real scraping, execute: pip install playwright")

def calculate_months():
    """Calculate the year and month strings for last month and the month before last."""
    today = datetime.now()
    # First day of current month
    first_day_current = today.replace(day=1)
    
    # Last day of previous month
    last_day_last_month = first_day_current - timedelta(days=1)
    first_day_last_month = last_day_last_month.replace(day=1)
    
    # Last day of month before last
    last_day_prev_prev = first_day_last_month - timedelta(days=1)
    first_day_prev_prev = last_day_prev_prev.replace(day=1)
    
    return {
        "last_month": {
            "name": first_day_last_month.strftime("%B"),
            "year_month": first_day_last_month.strftime("%Y-%m"),
            "start": first_day_last_month.strftime("%Y-%m-%d"),
            "end": last_day_last_month.strftime("%Y-%m-%d")
        },
        "prev_prev_month": {
            "name": first_day_prev_prev.strftime("%B"),
            "year_month": first_day_prev_prev.strftime("%Y-%m"),
            "start": first_day_prev_prev.strftime("%Y-%m-%d"),
            "end": last_day_prev_prev.strftime("%Y-%m-%d")
        }
    }

def extract_alidata_via_browser():
    """
    Playwright implementation scaffold to fetch the data.
    In real usage, this leverages the existing Chrome profile cookie session.
    """
    months = calculate_months()
    print(f"Extraction Target Periods:")
    print(f"  - 上月 (Last Month): {months['last_month']['start']} to {months['last_month']['end']}")
    print(f"  - 上上月 (Month Before): {months['prev_prev_month']['start']} to {months['prev_prev_month']['end']}")
    
    # Placeholder structure of the output extracted data
    extracted_data = {
        "meta": {
            "extracted_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "last_month_range": f"{months['last_month']['start']} to {months['last_month']['end']}",
            "prev_prev_month_range": f"{months['prev_prev_month']['start']} to {months['prev_prev_month']['end']}"
        },
        "metrics": {}
    }
    
    # We mock or return structured layout of indices
    metric_keys = [
        "搜索曝光次数", "搜索点击次数", "点击率CTR（搜索）", "店铺访问PV",
        "商机量（询盘+TM）", "商机转化率（访客→商机）", "商机→订单转化率",
        "平均回复时长（30天）", "5分钟快速回复率", "成交订单数", "信保实收金额",
        "在架产品总数", "90天新发品数", "实力优品数", "爆品数",
        "直通车花费", "直通车商机量", "直通车商机转化率", "直通车商机成本(CPL)"
    ]
    
    # Initialize dictionary
    for k in metric_keys:
        extracted_data["metrics"][k] = {
            "上月数据": None,
            "上上月数据": None,
            "行业均值": None,
            "行业TOP 10%": None
        }

    # Under standard execution, this file would be written out
    output_file = "raw_extracted_store_data.json"
    with open(output_file, 'w', encoding='utf-8') as f:
        json.dump(extracted_data, f, ensure_ascii=False, indent=2)
    print(f"Scaffold data structure initialized and saved to: {output_file}")
    return output_file

if __name__ == "__main__":
    extract_alidata_via_browser()
