0cf3297290
- Краулеры: smart_crawler.py, regional_crawler.py - Аудит: audit_orel_to_excel.py, audit_chukotka_to_excel.py - РКН проверка: check_rkn_registry.py, recheck_unclear_rkn.py - Отчёты: create_orel_horizontal_report.py - Обработка: process_all_hotels_embeddings.py - Документация: README.md, DB_SCHEMA_REFERENCE.md
444 lines
17 KiB
Python
444 lines
17 KiB
Python
#!/usr/bin/env python3
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"""
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Crawler для парсинга сайтов отелей с сохранением в PostgreSQL
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- Сохраняет сырой HTML (для будущей переобработки)
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- Сохраняет очищенный текст
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- Извлекает структурированные данные
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"""
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import asyncio
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import json
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import logging
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import re
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import psycopg2
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from psycopg2.extras import Json
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from datetime import datetime
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from typing import List, Dict, Set, Optional
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from urllib.parse import urljoin, urlparse, unquote
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from playwright.async_api import async_playwright, Page
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from bs4 import BeautifulSoup, Comment
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# Настройка логирования
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler(f'crawler_db_{datetime.now().strftime("%Y%m%d_%H%M%S")}.log'),
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logging.StreamHandler()
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]
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)
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logger = logging.getLogger(__name__)
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# Конфигурация БД
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DB_CONFIG = {
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'host': "147.45.189.234",
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'port': 5432,
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'database': "default_db",
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'user': "gen_user",
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'password': unquote("2~~9_%5EkVsU%3F2%5CS")
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}
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# Конфигурация краулинга
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MAX_PAGES_PER_SITE = 20
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PAGE_TIMEOUT = 45000
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NAVIGATION_TIMEOUT = 40000
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class TextCleaner:
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"""Продвинутая очистка HTML с сохранением важных данных"""
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# Теги для удаления (только мусор!)
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REMOVE_TAGS = ['script', 'style', 'noscript']
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# Классы/ID для удаления (только явная реклама)
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REMOVE_PATTERNS = ['advertisement', 'ad-banner', 'google-ad']
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# ВАЖНЫЕ классы/ID которые НЕ удаляем (контакты!)
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KEEP_PATTERNS = ['contact', 'phone', 'email', 'address', 'footer', 'info', 'about']
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@staticmethod
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def clean_html(html: str) -> str:
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"""Бережная очистка HTML - сохраняем контакты и важные данные"""
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soup = BeautifulSoup(html, 'html.parser')
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# 1. Удаляем комментарии
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for comment in soup.find_all(string=lambda text: isinstance(text, Comment)):
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comment.extract()
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# 2. Удаляем только явный мусор (скрипты, стили)
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for tag_name in TextCleaner.REMOVE_TAGS:
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for tag in soup.find_all(tag_name):
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tag.decompose()
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# 3. Удаляем только явную рекламу (но проверяем, чтобы не было важных данных)
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for pattern in TextCleaner.REMOVE_PATTERNS:
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for tag in soup.find_all(class_=re.compile(pattern, re.I)):
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# Проверяем, нет ли там важных данных
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tag_text = tag.get_text().lower()
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has_important = any(kw in tag_text for kw in ['телефон', 'email', 'адрес', '@', '+7', '8-'])
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if not has_important:
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tag.decompose()
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# 4. Извлекаем текст (с переносами строк для читаемости)
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text = soup.get_text(separator='\n', strip=True)
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# 5. Убираем лишние пробелы, но сохраняем структуру
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lines = [line.strip() for line in text.split('\n') if line.strip()]
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text = '\n'.join(lines)
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# 6. Убираем повторяющиеся переносы
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text = re.sub(r'\n{3,}', '\n\n', text)
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return text.strip()
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@staticmethod
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def extract_structured_data(html: str, text: str) -> Dict:
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"""Извлечь структурированные данные"""
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data = {
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'phones': [],
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'emails': [],
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'inn': [],
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'ogrn': [],
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'addresses': []
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}
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# Телефоны
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phones = re.findall(r'\+?[78][\s\-]?\(?(\d{3})\)?[\s\-]?(\d{3})[\s\-]?(\d{2})[\s\-]?(\d{2})', text)
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data['phones'] = list(set([''.join(p) for p in phones]))[:10]
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# Email
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emails = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', text)
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data['emails'] = list(set(emails))[:10]
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# ИНН (10 или 12 цифр, контекст "ИНН")
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inn_matches = re.findall(r'ИНН[:\s]*(\d{10}|\d{12})', text, re.IGNORECASE)
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data['inn'] = list(set(inn_matches))[:3]
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# ОГРН (13 или 15 цифр, контекст "ОГРН")
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ogrn_matches = re.findall(r'ОГРН[:\s]*(\d{13}|\d{15})', text, re.IGNORECASE)
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data['ogrn'] = list(set(ogrn_matches))[:3]
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# Адреса (упрощенно - строки с "адрес:", "г.", "ул.")
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address_patterns = [
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r'[Аа]дрес[:\s]+([^\n]{20,150})',
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r'г\.\s*[А-Я][а-я\-]+[,\s]+ул\.\s*[^\n]{10,100}'
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]
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for pattern in address_patterns:
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addresses = re.findall(pattern, text)
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data['addresses'].extend(addresses[:3])
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data['addresses'] = list(set(data['addresses']))[:5]
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return data
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class WebsiteCrawlerDB:
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"""Crawler с сохранением в PostgreSQL"""
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def __init__(self, hotel_id: str, hotel_name: str, website: str):
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self.hotel_id = hotel_id
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self.hotel_name = hotel_name
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self.website = self.normalize_url(website)
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self.domain = self.extract_domain(self.website)
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self.visited_urls: Set[str] = set()
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self.pages_data: List[Dict] = []
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self.cleaner = TextCleaner()
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self.conn = None
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self.start_time = None
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@staticmethod
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def normalize_url(url: str) -> str:
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"""Нормализация URL"""
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if not url.startswith(('http://', 'https://')):
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url = 'https://' + url
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return url.rstrip('/')
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@staticmethod
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def extract_domain(url: str) -> str:
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"""Извлечь домен"""
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parsed = urlparse(url)
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return parsed.netloc.lower()
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def is_internal_link(self, url: str) -> bool:
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"""Проверка внутренней ссылки"""
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try:
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parsed = urlparse(url)
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link_domain = parsed.netloc.lower()
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return (link_domain == self.domain or
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link_domain.endswith('.' + self.domain) or
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self.domain.endswith('.' + link_domain))
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except:
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return False
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def connect_db(self):
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"""Подключение к БД"""
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self.conn = psycopg2.connect(**DB_CONFIG)
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logger.info(" ✓ Подключено к PostgreSQL")
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def init_meta(self):
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"""Инициализация метаинформации"""
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cur = self.conn.cursor()
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cur.execute("""
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INSERT INTO hotel_website_meta
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(hotel_id, domain, main_url, crawl_status, crawl_started_at)
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VALUES (%s, %s, %s, 'in_progress', %s)
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ON CONFLICT (hotel_id) DO UPDATE SET
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crawl_status = 'in_progress',
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crawl_started_at = EXCLUDED.crawl_started_at,
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updated_at = CURRENT_TIMESTAMP
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""", (self.hotel_id, self.domain, self.website, self.start_time))
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self.conn.commit()
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cur.close()
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def save_page(self, url: str, title: str, html: str, status_code: int,
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response_time: int, depth: int, cleaned_text: str, structured_data: Dict):
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"""Сохранить страницу в БД"""
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cur = self.conn.cursor()
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try:
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# Сохраняем сырой HTML
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cur.execute("""
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INSERT INTO hotel_website_raw
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(hotel_id, url, page_title, html, status_code, response_time_ms, depth)
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VALUES (%s, %s, %s, %s, %s, %s, %s)
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ON CONFLICT (hotel_id, url) DO UPDATE SET
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html = EXCLUDED.html,
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page_title = EXCLUDED.page_title,
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status_code = EXCLUDED.status_code,
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response_time_ms = EXCLUDED.response_time_ms,
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crawled_at = CURRENT_TIMESTAMP
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RETURNING id
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""", (self.hotel_id, url, title, html, status_code, response_time, depth))
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raw_page_id = cur.fetchone()[0]
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# Сохраняем обработанный текст
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cur.execute("""
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INSERT INTO hotel_website_processed
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(raw_page_id, hotel_id, url, cleaned_text, extracted_data,
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has_forms, has_booking, text_length)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
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ON CONFLICT DO NOTHING
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""", (raw_page_id, self.hotel_id, url, cleaned_text, Json(structured_data),
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structured_data.get('has_forms', False),
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structured_data.get('has_booking', False),
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len(cleaned_text)))
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self.conn.commit()
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except Exception as e:
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logger.error(f"Ошибка сохранения страницы {url}: {e}")
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self.conn.rollback()
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finally:
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cur.close()
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def update_meta(self, status: str, error_msg: Optional[str] = None):
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"""Обновить метаинформацию"""
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cur = self.conn.cursor()
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total_size = sum(len(p.get('html', '')) for p in self.pages_data)
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cur.execute("""
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UPDATE hotel_website_meta SET
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pages_crawled = %s,
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total_size_bytes = %s,
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crawl_status = %s,
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crawl_finished_at = %s,
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error_message = %s,
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updated_at = CURRENT_TIMESTAMP
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WHERE hotel_id = %s
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""", (len(self.pages_data), total_size, status, datetime.now(), error_msg, self.hotel_id))
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self.conn.commit()
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cur.close()
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async def extract_page_data(self, page: Page, url: str, depth: int) -> Optional[Dict]:
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"""Извлечь данные со страницы"""
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start_time = datetime.now()
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try:
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title = await page.title()
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html = await page.content()
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# Очищаем текст
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cleaned_text = self.cleaner.clean_html(html)
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# Извлекаем структурированные данные
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structured_data = self.cleaner.extract_structured_data(html, cleaned_text)
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# Извлекаем ссылки
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links = await page.evaluate("""
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() => Array.from(document.querySelectorAll('a[href]'))
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.map(a => a.href)
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.filter(href => href && !href.startsWith('mailto:') && !href.startsWith('tel:'))
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""")
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# Проверки
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structured_data['has_forms'] = await page.evaluate("() => document.querySelectorAll('form').length > 0")
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structured_data['has_booking'] = 'бронирован' in cleaned_text.lower() or 'booking' in cleaned_text.lower()
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response_time = int((datetime.now() - start_time).total_seconds() * 1000)
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page_data = {
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'url': url,
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'title': title,
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'html': html,
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'cleaned_text': cleaned_text,
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'structured_data': structured_data,
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'links': list(set(links)),
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'status_code': 200,
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'response_time': response_time,
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'depth': depth,
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'text_length': len(cleaned_text)
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}
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# Сохраняем в БД
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self.save_page(
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url, title, html, 200, response_time, depth,
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cleaned_text, structured_data
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)
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return page_data
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except Exception as e:
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logger.error(f"Ошибка извлечения данных с {url}: {e}")
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return None
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async def crawl_page(self, page: Page, url: str, depth: int = 0):
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"""Парсинг одной страницы"""
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if url in self.visited_urls or len(self.visited_urls) >= MAX_PAGES_PER_SITE:
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return
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# Пропускаем PDF и файлы
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if url.lower().endswith(('.pdf', '.doc', '.docx', '.zip', '.jpg', '.png')):
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return
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try:
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logger.info(f" Парсинг (depth={depth}): {url[:80]}...")
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# Загружаем страницу
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try:
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await page.goto(url, wait_until='domcontentloaded', timeout=NAVIGATION_TIMEOUT)
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await page.wait_for_timeout(2000)
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except Exception as e:
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logger.warning(f" Пробуем load вместо domcontentloaded")
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await page.goto(url, wait_until='load', timeout=NAVIGATION_TIMEOUT)
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await page.wait_for_timeout(1000)
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self.visited_urls.add(url)
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# Извлекаем и сохраняем данные
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page_data = await self.extract_page_data(page, url, depth)
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if page_data:
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self.pages_data.append(page_data)
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logger.info(f" ✓ Сохранено {page_data['text_length']} символов в БД")
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# Парсим внутренние ссылки (только для depth=0)
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if depth == 0 and page_data.get('links'):
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internal_links = [
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link for link in page_data['links']
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if self.is_internal_link(link) and link not in self.visited_urls
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]
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logger.info(f" Найдено {len(internal_links)} внутренних ссылок")
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# Парсим depth=1
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for link in internal_links[:MAX_PAGES_PER_SITE - 1]:
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if len(self.visited_urls) >= MAX_PAGES_PER_SITE:
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break
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await self.crawl_page(page, link, depth=1)
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except Exception as e:
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logger.error(f" ✗ Ошибка парсинга {url}: {e}")
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async def crawl(self) -> bool:
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"""Запуск парсинга сайта"""
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self.start_time = datetime.now()
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logger.info(f"\n{'='*70}")
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logger.info(f"🏨 {self.hotel_name[:60]}")
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logger.info(f"🌐 {self.website}")
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logger.info(f"{'='*70}")
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self.connect_db()
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self.init_meta()
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try:
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async with async_playwright() as p:
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browser = await p.chromium.launch(headless=True)
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page = await browser.new_page()
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# Парсим главную
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await self.crawl_page(page, self.website, depth=0)
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await browser.close()
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logger.info(f"✓ Спарсено {len(self.pages_data)} страниц")
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# Обновляем метаинформацию
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self.update_meta('completed')
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return len(self.pages_data) > 0
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except Exception as e:
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logger.error(f"✗ Критическая ошибка: {e}")
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self.update_meta('failed', str(e))
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return False
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finally:
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if self.conn:
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self.conn.close()
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async def main():
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"""Главная функция"""
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import sys
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hotels_file = sys.argv[1] if len(sys.argv) > 1 else 'test_single_hotel.json'
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with open(hotels_file, 'r', encoding='utf-8') as f:
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hotels = json.load(f)
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logger.info(f"\n{'='*70}")
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logger.info(f"🚀 ЗАПУСК КРАУЛИНГА С СОХРАНЕНИЕМ В POSTGRESQL")
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logger.info(f"📊 Отелей: {len(hotels)}")
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logger.info(f"💾 Таблицы: hotel_website_raw, hotel_website_meta")
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logger.info(f"{'='*70}\n")
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success_count = 0
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error_count = 0
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for idx, hotel in enumerate(hotels, 1):
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logger.info(f"\n[{idx}/{len(hotels)}] ====================================")
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try:
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crawler = WebsiteCrawlerDB(
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hotel_id=hotel['id'],
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hotel_name=hotel['name'],
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website=hotel['website']
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)
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if await crawler.crawl():
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success_count += 1
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else:
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error_count += 1
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# Задержка между отелями
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await asyncio.sleep(3)
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except Exception as e:
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logger.error(f"✗ Ошибка для отеля {hotel['name']}: {e}")
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error_count += 1
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logger.info(f"\n{'='*70}")
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logger.info(f"📊 ИТОГИ:")
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logger.info(f" ✅ Успешно: {success_count}/{len(hotels)}")
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logger.info(f" ✗ Ошибки: {error_count}/{len(hotels)}")
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logger.info(f"{'='*70}\n")
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if __name__ == "__main__":
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asyncio.run(main())
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