AI_SEO_Crawler / parser.py
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"""
HTML Parser and URL Extractor component for web crawler
"""
import logging
import re
from typing import Dict, List, Set, Tuple, Optional, Any
from urllib.parse import urlparse, urljoin, unquote
from bs4 import BeautifulSoup
import tldextract
import hashlib
import os
from models import URL, Page, Priority, normalize_url
import config
# Configure logging
logging.basicConfig(
level=getattr(logging, config.LOG_LEVEL),
format=config.LOG_FORMAT
)
logger = logging.getLogger(__name__)
class HTMLParser:
"""
Parses HTML content and extracts URLs and other information
"""
def __init__(self):
"""Initialize HTML parser"""
# Compile URL filter regex patterns for efficiency
self.url_filters = [re.compile(pattern) for pattern in config.URL_FILTERS]
def parse(self, page: Page, base_url: Optional[str] = None) -> Tuple[List[str], Dict[str, Any]]:
"""
Parse HTML content and extract URLs and metadata
Args:
page: Page object containing HTML content
base_url: Base URL for resolving relative links (defaults to page URL)
Returns:
Tuple of (extracted URLs, metadata)
"""
if not page or not page.content:
return [], {}
# Use page URL as base URL if not provided
if not base_url:
base_url = page.url
# Parse HTML content
soup = BeautifulSoup(page.content, 'html.parser')
# Extract URLs
urls = self._extract_urls(soup, base_url)
# Extract metadata
metadata = self._extract_metadata(soup)
return urls, metadata
def _extract_urls(self, soup: BeautifulSoup, base_url: str) -> List[str]:
"""
Extract and normalize URLs from HTML content
Args:
soup: BeautifulSoup object
base_url: Base URL for resolving relative links
Returns:
List of normalized URLs
"""
urls = set()
all_urls = set() # Track all URLs before filtering
filtered_urls = set() # Track filtered URLs
logger.debug(f"Extracting URLs from page: {base_url}")
# Extract URLs from <a> tags
for link in soup.find_all('a', href=True):
href = link['href'].strip()
if href and not href.startswith(('#', 'javascript:', 'mailto:', 'tel:')):
# Resolve relative URLs
try:
absolute_url = urljoin(base_url, href)
all_urls.add(absolute_url)
# Normalize URL
normalized_url = normalize_url(absolute_url)
# Apply URL filters
if self._should_allow_url(normalized_url):
urls.add(normalized_url)
else:
filtered_urls.add(normalized_url)
except Exception as e:
logger.debug(f"Error processing URL {href}: {e}")
# Extract URLs from other elements like <iframe>, <frame>, <img>, etc.
for tag_name, attr in [('frame', 'src'), ('iframe', 'src'), ('img', 'src'),
('link', 'href'), ('script', 'src'), ('area', 'href')]:
for tag in soup.find_all(tag_name, attrs={attr: True}):
url = tag[attr].strip()
if url and not url.startswith(('#', 'javascript:', 'data:', 'mailto:', 'tel:')):
try:
absolute_url = urljoin(base_url, url)
all_urls.add(absolute_url)
normalized_url = normalize_url(absolute_url)
if self._should_allow_url(normalized_url):
urls.add(normalized_url)
else:
filtered_urls.add(normalized_url)
except Exception as e:
logger.debug(f"Error processing URL {url}: {e}")
# Log statistics
logger.debug(f"Found {len(all_urls)} total URLs")
logger.debug(f"Filtered {len(filtered_urls)} URLs")
logger.debug(f"Accepted {len(urls)} URLs")
# Log some example filtered URLs for debugging
if filtered_urls:
sample_filtered = list(filtered_urls)[:5]
logger.debug(f"Sample filtered URLs: {sample_filtered}")
# Return list of unique URLs
return list(urls)
def _should_allow_url(self, url: str) -> bool:
"""
Check if URL should be allowed based on filters
Args:
url: URL to check
Returns:
True if URL should be allowed, False otherwise
"""
try:
parsed = urlparse(url)
# Check scheme
if parsed.scheme not in config.ALLOWED_SCHEMES:
logger.debug(f"URL filtered - invalid scheme: {url}")
return False
# Check domain restrictions
domain = self._extract_domain(url)
# Check allowed domains if set
if config.ALLOWED_DOMAINS and domain not in config.ALLOWED_DOMAINS:
logger.debug(f"URL filtered - domain not allowed: {url} (domain: {domain}, allowed: {config.ALLOWED_DOMAINS})")
return False
# Check excluded domains
if domain in config.EXCLUDED_DOMAINS:
logger.debug(f"URL filtered - domain excluded: {url}")
return False
# Check URL filters
for pattern in self.url_filters:
if pattern.match(url):
logger.debug(f"URL filtered - pattern match: {url}")
return False
return True
except Exception as e:
logger.debug(f"Error checking URL {url}: {e}")
return False
def _extract_metadata(self, soup: BeautifulSoup) -> Dict[str, Any]:
"""
Extract metadata from HTML content
Args:
soup: BeautifulSoup object
Returns:
Dictionary of metadata
"""
metadata = {}
# Extract title
title_tag = soup.find('title')
if title_tag and title_tag.string:
metadata['title'] = title_tag.string.strip()
# Extract meta description
description_tag = soup.find('meta', attrs={'name': 'description'})
if description_tag and description_tag.get('content'):
metadata['description'] = description_tag['content'].strip()
# Extract meta keywords
keywords_tag = soup.find('meta', attrs={'name': 'keywords'})
if keywords_tag and keywords_tag.get('content'):
metadata['keywords'] = [k.strip() for k in keywords_tag['content'].split(',')]
# Extract canonical URL
canonical_tag = soup.find('link', attrs={'rel': 'canonical'})
if canonical_tag and canonical_tag.get('href'):
metadata['canonical_url'] = canonical_tag['href'].strip()
# Extract robots meta
robots_tag = soup.find('meta', attrs={'name': 'robots'})
if robots_tag and robots_tag.get('content'):
metadata['robots'] = robots_tag['content'].strip()
# Extract Open Graph metadata
og_metadata = {}
for meta_tag in soup.find_all('meta', attrs={'property': re.compile('^og:')}):
if meta_tag.get('content'):
property_name = meta_tag['property'][3:] # Remove 'og:' prefix
og_metadata[property_name] = meta_tag['content'].strip()
if og_metadata:
metadata['open_graph'] = og_metadata
# Extract Twitter Card metadata
twitter_metadata = {}
for meta_tag in soup.find_all('meta', attrs={'name': re.compile('^twitter:')}):
if meta_tag.get('content'):
property_name = meta_tag['name'][8:] # Remove 'twitter:' prefix
twitter_metadata[property_name] = meta_tag['content'].strip()
if twitter_metadata:
metadata['twitter_card'] = twitter_metadata
# Extract schema.org structured data (JSON-LD)
schema_metadata = []
for script in soup.find_all('script', attrs={'type': 'application/ld+json'}):
if script.string:
try:
import json
schema_data = json.loads(script.string)
schema_metadata.append(schema_data)
except Exception as e:
logger.debug(f"Error parsing JSON-LD: {e}")
if schema_metadata:
metadata['structured_data'] = schema_metadata
# Extract text content statistics
text_content = soup.get_text(separator=' ', strip=True)
if text_content:
word_count = len(text_content.split())
metadata['word_count'] = word_count
metadata['text_length'] = len(text_content)
return metadata
def _extract_domain(self, url: str) -> str:
"""Extract domain from URL"""
parsed = tldextract.extract(url)
return f"{parsed.domain}.{parsed.suffix}" if parsed.suffix else parsed.domain
def calculate_priority(self, url: str, metadata: Dict[str, Any]) -> Priority:
"""
Calculate priority for a URL based on various factors
Args:
url: URL to calculate priority for
metadata: Metadata extracted from the page
Returns:
Priority enum value
"""
# Default priority
priority = Priority.MEDIUM
try:
# Extract path depth
parsed = urlparse(url)
path = parsed.path
depth = len([p for p in path.split('/') if p])
# Prioritize URLs with shorter paths
if depth <= 1:
priority = Priority.HIGH
elif depth <= 3:
priority = Priority.MEDIUM
else:
priority = Priority.LOW
# Prioritize URLs with certain keywords in path
if re.search(r'(article|blog|news|post)', path, re.IGNORECASE):
priority = Priority.HIGH
# Deprioritize URLs with pagination patterns
if re.search(r'(page|p|pg)=\d+', url, re.IGNORECASE):
priority = Priority.LOW
# Check metadata
if metadata:
# Prioritize based on title
title = metadata.get('title', '')
if title and len(title) > 10:
priority = min(priority, Priority.MEDIUM) # Raise priority if it's lower
# Prioritize based on description
description = metadata.get('description', '')
if description and len(description) > 50:
priority = min(priority, Priority.MEDIUM) # Raise priority if it's lower
# Prioritize based on word count
word_count = metadata.get('word_count', 0)
if word_count > 1000:
priority = min(priority, Priority.HIGH) # High priority for content-rich pages
elif word_count > 500:
priority = min(priority, Priority.MEDIUM)
return priority
except Exception as e:
logger.debug(f"Error calculating priority for URL {url}: {e}")
return Priority.MEDIUM