mirror of
https://github.com/R0m1k3/Priceflow.git
synced 2026-10-21 22:28:11 +02:00
446 lines
17 KiB
Python
446 lines
17 KiB
Python
"""
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Tiendeo.fr Scraper Service - Powered by Crawl4AI
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Scrapes promotional catalogs from Tiendeo.fr for configured enseignes.
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Uses Crawl4AI for intelligent content extraction instead of fragile CSS selectors.
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"""
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import asyncio
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import hashlib
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import logging
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import re
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from datetime import datetime, timedelta
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from typing import Any
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from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
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from pydantic import BaseModel, Field
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from sqlalchemy.orm import Session
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from app.models import Enseigne, Catalogue, CataloguePage, ScrapingLog
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logger = logging.getLogger(__name__)
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# Tiendeo site base URL
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TIENDEO_BASE_URL = "https://www.tiendeo.fr"
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# Date pattern for Tiendeo: "Expire le 31/12" or "mar. 25/11 - lun. 08/12"
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DATE_PATTERN = r"(\d{1,2}[/-]\d{1,2}(?:[/-]\d{2,4})?)"
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# ============================================================================
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# PYDANTIC SCHEMAS FOR EXTRACTION
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# ============================================================================
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class CatalogueCard(BaseModel):
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"""Schema for catalog card extraction"""
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title: str = Field(description="Catalogue title")
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url: str = Field(description="Full URL to the catalogue viewer")
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image_url: str | None = Field(default=None, description="Cover image URL")
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date_text: str | None = Field(default=None, description="Date validity text")
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class CataloguePageSchema(BaseModel):
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"""Schema for catalog page image extraction"""
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page_number: int = Field(description="Page number in sequence")
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image_url: str = Field(description="Full resolution image URL")
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width: int | None = Field(default=None, description="Image width in pixels")
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height: int | None = Field(default=None, description="Image height in pixels")
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# ============================================================================
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# DATE PARSING
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# ============================================================================
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def parse_tiendeo_dates(date_str: str) -> tuple[datetime, datetime]:
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"""
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Parse Tiendeo date format to datetime objects.
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Args:
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date_str: Date string like "Expire le 31/12" or "mar. 25/11 - lun. 08/12"
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Returns:
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Tuple of (date_debut, date_fin)
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"""
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matches = re.findall(DATE_PATTERN, date_str)
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if not matches:
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logger.warning(f"No dates found in '{date_str}', using default duration")
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raise ValueError(f"Invalid date format: {date_str}")
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today = datetime.now()
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current_year = today.year
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parsed_dates = []
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for date_match in matches:
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date_match = date_match.replace("-", "/")
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parts = date_match.split("/")
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day = int(parts[0])
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month = int(parts[1])
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year = int(parts[2]) if len(parts) > 2 else current_year
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if year < 100:
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year += 2000
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# Handle year rollover
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if len(parts) == 2:
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if month < today.month - 3:
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year += 1
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try:
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parsed_dates.append(datetime(year, month, day))
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except ValueError:
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continue
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if not parsed_dates:
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raise ValueError("No valid dates parsed")
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if len(parsed_dates) >= 2:
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return min(parsed_dates), max(parsed_dates)
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elif len(parsed_dates) == 1:
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# If only one date (expire date), use today as start
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return today, parsed_dates[0]
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return parsed_dates[0], parsed_dates[0]
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def compute_catalog_hash(enseigne_id: int, titre: str, date_debut: datetime) -> str:
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"""Compute SHA256 hash for duplicate detection."""
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content = f"{enseigne_id}|{titre}|{date_debut.isoformat()}"
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return hashlib.sha256(content.encode()).hexdigest()
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# ============================================================================
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# SCRAPING FUNCTIONS
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# ============================================================================
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async def scrape_catalog_list(crawler: AsyncWebCrawler, enseigne: Enseigne) -> list[dict[str, Any]]:
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"""Scrape the list of catalogs for an enseigne from Tiendeo using Crawl4AI."""
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# Tiendeo uses store URLs like: /Magasins/{city}/{enseigne-slug}
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url = f"{TIENDEO_BASE_URL}/Magasins/nancy/{enseigne.slug_bonial.lower()}"
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logger.info(f"Scraping catalog list for {enseigne.nom} from {url}")
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# Configure crawler for this page
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config = CrawlerRunConfig(
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cache_mode=CacheMode.BYPASS,
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wait_for_images=True,
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process_iframes=False,
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remove_overlay_elements=True,
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wait_until="networkidle",
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)
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result = await crawler.arun(url=url, config=config)
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if not result.success:
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logger.error(f"Failed to crawl {url}: {result.error_message}")
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return []
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# Extract catalog cards using JavaScript
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catalog_data = await crawler.crawler_strategy.execute_js(
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"""
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() => {
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const results = [];
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// Find all links to catalog pages (format: /Catalogues/{id})
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const catalogLinks = document.querySelectorAll('a[href*="/Catalogues/"]');
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catalogLinks.forEach((link) => {
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const href = link.href;
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const catalogId = href.split('/Catalogues/')[1];
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if (!catalogId) return;
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// Extract title from link text or nearby heading
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let title = link.textContent.trim();
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// Try to find h3 or h4 near this link for better title
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const parentContainer = link.closest('div, section, article');
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if (parentContainer) {
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const heading = parentContainer.querySelector('h3, h4, h2');
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if (heading && heading.textContent.trim().length > title.length) {
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title = heading.textContent.trim();
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}
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}
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// Look for image in the same container
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let imgSrc = null;
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if (parentContainer) {
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const img = parentContainer.querySelector('img');
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if (img) {
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imgSrc = img.src || img.getAttribute('data-src') || img.getAttribute('srcset')?.split(' ')[0];
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}
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}
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// Extract date information from container text
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const containerText = parentContainer ? parentContainer.textContent : link.textContent;
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// Avoid duplicates
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if (!results.some(r => r.url === href)) {
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results.push({
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title: title,
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url: href,
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image: imgSrc,
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containerText: containerText.substring(0, 300), // For date parsing
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});
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}
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});
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return results;
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}
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"""
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)
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logger.info(f"Found {len(catalog_data)} catalog links for {enseigne.nom}")
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catalogues = []
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for idx, cat_data in enumerate(catalog_data):
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try:
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titre = cat_data.get('title', 'Catalogue')
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image_url = cat_data.get('image')
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catalogue_url = cat_data.get('url')
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container_text = cat_data.get('containerText', '')
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if not catalogue_url:
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logger.warning(f"Skipping catalog {idx}: missing URL")
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continue
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# Clean title - remove extra info
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titre = titre.replace('Gifi', '').replace('Action', '').replace('Centrakor', '')
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titre = re.sub(r'\d+\.\d+\s*km', '', titre) # Remove "3.2 km"
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titre = re.sub(r'Nancy', '', titre, flags=re.IGNORECASE)
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titre = re.sub(r'Expire\s+le.*', '', titre) # Remove expire info from title
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titre = titre.strip()
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# Skip if title is empty or too short
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if len(titre) < 3:
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logger.warning(f"Skipping catalog {idx}: title too short after cleaning")
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continue
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# Ensure absolute URL
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if image_url and image_url.startswith('//'):
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image_url = 'https:' + image_url
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# Parse dates from container text
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date_debut, date_fin = datetime.now(), datetime.now() + timedelta(days=30)
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try:
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date_debut, date_fin = parse_tiendeo_dates(container_text)
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except ValueError:
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# Default to 30 days validity
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logger.debug(f"Could not parse dates for '{titre}', using 30 days default")
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catalogues.append({
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"titre": titre,
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"date_debut": date_debut,
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"date_fin": date_fin,
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"image_couverture_url": image_url or "",
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"catalogue_url": catalogue_url,
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})
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logger.info(f"Extracted catalog: {titre}")
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except Exception as e:
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logger.error(f"Error processing catalog {idx} for {enseigne.nom}: {e}")
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continue
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logger.info(f"Successfully extracted {len(catalogues)} catalogs for {enseigne.nom}")
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return catalogues
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async def scrape_catalog_pages(crawler: AsyncWebCrawler, catalogue_url: str) -> list[dict[str, Any]]:
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"""Scrape all pages of a catalog from the Tiendeo viewer using Crawl4AI."""
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logger.info(f"Scraping catalog pages from {catalogue_url}")
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# Configure crawler to wait for images
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config = CrawlerRunConfig(
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cache_mode=CacheMode.BYPASS,
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wait_for_images=True,
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process_iframes=True,
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remove_overlay_elements=True,
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wait_until="networkidle",
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delay_before_return_html=3.0, # Wait for viewer to fully load
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)
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result = await crawler.arun(url=catalogue_url, config=config)
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if not result.success:
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logger.error(f"Failed to crawl {catalogue_url}: {result.error_message}")
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return []
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# Extract catalog page images using JavaScript with intelligent filtering
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pages_data = await crawler.crawler_strategy.execute_js(
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"""
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() => {
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const results = [];
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const seenUrls = new Set();
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// Find all images - prioritize those in viewer containers
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const allImages = document.querySelectorAll('img');
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allImages.forEach((img) => {
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// Get image source (try multiple attributes)
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let src = img.src || img.getAttribute('data-src');
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// Try srcset as fallback
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if (!src && img.srcset) {
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const srcsetParts = img.srcset.split(',')[0].trim().split(' ');
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src = srcsetParts[0];
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}
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if (!src || seenUrls.has(src)) return;
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// Skip obvious non-catalog images
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if (src.includes('logo') || src.includes('icon') || src.includes('avatar')) {
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return;
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}
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// Get dimensions
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const width = img.naturalWidth || img.width;
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const height = img.naturalHeight || img.height;
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// Filter by dimensions - catalog pages are typically portrait and high-res
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// Minimum 400px width, aspect ratio close to 3:4 or similar
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if (width < 400 || height < 400) return;
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const aspectRatio = width / height;
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// Portrait images (0.5 to 0.9 ratio) - catalog pages are usually portrait
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if (aspectRatio > 0.5 && aspectRatio < 0.9) {
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seenUrls.add(src);
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results.push({
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image_url: src,
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width: width,
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height: height,
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});
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}
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});
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// Sort by area (largest first) to prioritize full resolution images
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results.sort((a, b) => (b.width * b.height) - (a.width * a.height));
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// Assign page numbers
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return results.map((item, index) => ({
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...item,
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numero_page: index + 1,
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}));
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}
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"""
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)
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logger.info(f"Found {len(pages_data)} pages in catalog")
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return pages_data
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async def scrape_enseigne(enseigne: Enseigne, db: Session) -> ScrapingLog:
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"""Scrape all catalogs for a specific enseigne from Tiendeo using Crawl4AI."""
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start_time = datetime.now()
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log = ScrapingLog(
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enseigne_id=enseigne.id,
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statut="error",
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catalogues_trouves=0,
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catalogues_nouveaux=0,
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catalogues_mis_a_jour=0,
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)
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try:
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# Configure browser for Crawl4AI
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browser_config = BrowserConfig(
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headless=True,
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verbose=False,
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extra_args=["--disable-gpu", "--no-sandbox", "--disable-dev-shm-usage"],
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)
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async with AsyncWebCrawler(config=browser_config) as crawler:
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catalogues_data = await scrape_catalog_list(crawler, enseigne)
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log.catalogues_trouves = len(catalogues_data)
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if not catalogues_data:
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log.statut = "success"
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log.message_erreur = "No catalogs found (may be normal)"
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return log
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for cat_data in catalogues_data:
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try:
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content_hash = compute_catalog_hash(
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enseigne.id,
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cat_data["titre"],
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cat_data["date_debut"]
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)
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existing = db.query(Catalogue).filter_by(content_hash=content_hash).first()
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if existing:
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logger.debug(f"Catalog already exists: {cat_data['titre']}")
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continue
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await asyncio.sleep(2) # Respectful delay
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pages_data = await scrape_catalog_pages(crawler, cat_data["catalogue_url"])
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if not pages_data:
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logger.warning(f"No pages found for catalog: {cat_data['titre']}")
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continue
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catalogue = Catalogue(
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enseigne_id=enseigne.id,
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titre=cat_data["titre"],
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date_debut=cat_data["date_debut"],
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date_fin=cat_data["date_fin"],
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image_couverture_url=cat_data["image_couverture_url"],
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catalogue_url=cat_data["catalogue_url"],
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statut="actif",
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nombre_pages=len(pages_data),
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content_hash=content_hash,
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)
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db.add(catalogue)
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db.flush()
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for page_data in pages_data:
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catalogue_page = CataloguePage(
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catalogue_id=catalogue.id,
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numero_page=page_data["numero_page"],
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image_url=page_data["image_url"],
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largeur=page_data.get("width"),
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hauteur=page_data.get("height"),
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)
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db.add(catalogue_page)
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log.catalogues_nouveaux += 1
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logger.info(f"Added new catalog: {cat_data['titre']} with {len(pages_data)} pages")
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except Exception as e:
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logger.error(f"Error processing catalog: {e}")
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continue
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db.commit()
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log.statut = "success"
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except Exception as e:
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logger.error(f"Error scraping {enseigne.nom}: {e}")
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log.statut = "error"
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log.message_erreur = str(e)
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db.rollback()
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log.duree_secondes = (datetime.now() - start_time).total_seconds()
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db.add(log)
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db.commit()
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return log
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async def scrape_all_enseignes(db: Session) -> list[ScrapingLog]:
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"""Scrape all active enseignes from Tiendeo."""
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enseignes = db.query(Enseigne).filter_by(is_active=True).all()
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logger.info(f"Starting Tiendeo scraping for {len(enseignes)} active enseignes")
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logs = []
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for enseigne in enseignes:
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try:
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await asyncio.sleep(2)
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log = await scrape_enseigne(enseigne, db)
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logs.append(log)
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except Exception as e:
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logger.error(f"Failed to scrape {enseigne.nom}: {e}")
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logger.info(f"Tiendeo scraping complete: {len(logs)} enseignes processed")
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return logs
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