#!/usr/bin/env python3
"""
Simple 1688 Daily Goods Scraper Demo
This scraper demonstrates collecting both raw HTML and structured data from 1688.com
Modified from alibaba-scraper project for 1688 platform
"""

import requests
import json
import csv
from pathlib import Path
from datetime import datetime
import re

class Simple1688Scraper:
    def __init__(self, output_dir="demo_output"):
        self.output_dir = Path(output_dir)
        self.html_dir = self.output_dir / "html_raw"
        self.json_dir = self.output_dir / "json_structured"

        # Create directories
        self.html_dir.mkdir(parents=True, exist_ok=True)
        self.json_dir.mkdir(parents=True, exist_ok=True)

        self.headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
        }

    def scrape_search_page(self, keyword, page_num=1):
        """Scrape a single search page from 1688.com"""
        print(f"\n[INFO] Scraping keyword: {keyword}, page: {page_num}")

        # Construct 1688 search URL
        url = f"https://s.1688.com/selloffer/offer_search.htm?keywords={keyword}&beginPage={page_num}"

        try:
            response = requests.get(url, headers=self.headers, timeout=30)
            response.encoding = 'utf-8'

            # Clean keyword for filename
            clean_keyword = re.sub(r'[^\w\s-]', '', keyword).strip().replace(' ', '_')
            timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')

            # Save raw HTML
            html_filename = self.html_dir / f"{clean_keyword}_page{page_num}_{timestamp}.html"
            with open(html_filename, 'w', encoding='utf-8') as f:
                f.write(response.text)

            print(f"[SUCCESS] Saved HTML: {html_filename}")

            # Extract structured data (basic extraction)
            products = self.extract_products(response.text, keyword, page_num)

            # Save structured data as JSON
            json_filename = self.json_dir / f"{clean_keyword}_page{page_num}_{timestamp}.json"
            with open(json_filename, 'w', encoding='utf-8') as f:
                json.dump(products, f, ensure_ascii=False, indent=2)

            print(f"[SUCCESS] Saved JSON: {json_filename}")
            print(f"[INFO] Extracted {len(products)} product entries")

            return {
                'html_file': str(html_filename),
                'json_file': str(json_filename),
                'product_count': len(products),
                'keyword': keyword,
                'page': page_num
            }

        except Exception as e:
            print(f"[ERROR] Failed to scrape {keyword} page {page_num}: {str(e)}")
            return None

    def extract_products(self, html_content, keyword, page_num):
        """Extract product data from HTML (basic extraction)"""
        products = []

        # Basic extraction - this is a simplified version
        # In production, you would use proper HTML parsing with BeautifulSoup or lxml

        # For demo purposes, create sample structured data showing what would be extracted
        sample_product = {
            'source': '1688.com',
            'keyword': keyword,
            'page_number': page_num,
            'timestamp': datetime.now().isoformat(),
            'note': 'This is demo data. Full extraction requires proper HTML parsing and may need authentication.',
            'extraction_method': 'basic_pattern_matching',
            'html_size_bytes': len(html_content),
            'data_fields': [
                'product_name',
                'price_range',
                'seller_name',
                'minimum_order',
                'product_url',
                'image_url',
                'seller_rating',
                'transaction_count'
            ]
        }

        products.append(sample_product)

        return products

    def scrape_multiple_keywords(self, keywords, max_pages=2):
        """Scrape multiple keywords"""
        all_results = []

        print(f"\n{'='*60}")
        print(f"Starting 1688 Daily Goods Scraper")
        print(f"{'='*60}")
        print(f"Keywords: {', '.join(keywords)}")
        print(f"Max pages per keyword: {max_pages}")
        print(f"Output directory: {self.output_dir}")

        for keyword in keywords:
            for page in range(1, max_pages + 1):
                result = self.scrape_search_page(keyword, page)
                if result:
                    all_results.append(result)

        # Save summary
        summary_file = self.output_dir / "scraping_summary.json"
        with open(summary_file, 'w', encoding='utf-8') as f:
            json.dump({
                'timestamp': datetime.now().isoformat(),
                'total_pages_scraped': len(all_results),
                'keywords': keywords,
                'max_pages_per_keyword': max_pages,
                'results': all_results
            }, f, ensure_ascii=False, indent=2)

        print(f"\n{'='*60}")
        print(f"Scraping completed!")
        print(f"Total pages scraped: {len(all_results)}")
        print(f"Summary saved to: {summary_file}")
        print(f"{'='*60}\n")

        return all_results

if __name__ == "__main__":
    # Daily goods keywords in Chinese
    keywords = [
        "日用品",      # Daily necessities
        "厨房用品",    # Kitchen supplies
        "家居用品",    # Home supplies
    ]

    scraper = Simple1688Scraper(output_dir="demo_1688_output")
    results = scraper.scrape_multiple_keywords(keywords, max_pages=2)

    print("\n[COMPLETE] Raw HTML files and structured JSON data have been saved.")
    print(f"[LOCATION] Check the '{scraper.output_dir}' directory for all files.")