Files
crawl4ai/crawl4ai/content_scrapping_strategy.py
2024-10-27 19:24:46 +08:00

457 lines
20 KiB
Python

from abc import ABC, abstractmethod
from typing import Dict, Any
from bs4 import BeautifulSoup
from concurrent.futures import ThreadPoolExecutor
import asyncio, requests, re, os
from .config import *
from bs4 import element, NavigableString, Comment
from urllib.parse import urljoin
from requests.exceptions import InvalidSchema
from .content_cleaning_strategy import ContentCleaningStrategy
from .utils import (
sanitize_input_encode,
sanitize_html,
extract_metadata,
InvalidCSSSelectorError,
CustomHTML2Text,
normalize_url,
is_external_url
)
class ContentScrappingStrategy(ABC):
@abstractmethod
def scrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
pass
@abstractmethod
async def ascrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
pass
class WebScrappingStrategy(ContentScrappingStrategy):
def scrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
return self._get_content_of_website_optimized(url, html, is_async=False, **kwargs)
async def ascrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
return await asyncio.to_thread(self._get_content_of_website_optimized, url, html, **kwargs)
def _get_content_of_website_optimized(self, url: str, html: str, word_count_threshold: int = MIN_WORD_THRESHOLD, css_selector: str = None, **kwargs) -> Dict[str, Any]:
success = True
if not html:
return None
soup = BeautifulSoup(html, 'html.parser')
body = soup.body
image_description_min_word_threshold = kwargs.get('image_description_min_word_threshold', IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD)
for tag in kwargs.get('excluded_tags', []) or []:
for el in body.select(tag):
el.decompose()
if css_selector:
selected_elements = body.select(css_selector)
if not selected_elements:
return {
'markdown': '',
'cleaned_html': '',
'success': True,
'media': {'images': [], 'videos': [], 'audios': []},
'links': {'internal': [], 'external': []},
'metadata': {},
'message': f"No elements found for CSS selector: {css_selector}"
}
# raise InvalidCSSSelectorError(f"Invalid CSS selector, No elements found for CSS selector: {css_selector}")
body = soup.new_tag('div')
for el in selected_elements:
body.append(el)
links = {'internal': [], 'external': []}
media = {'images': [], 'videos': [], 'audios': []}
internal_links_dict = {}
external_links_dict = {}
# Extract meaningful text for media files from closest parent
def find_closest_parent_with_useful_text(tag):
current_tag = tag
while current_tag:
current_tag = current_tag.parent
# Get the text content of the parent tag
if current_tag:
text_content = current_tag.get_text(separator=' ',strip=True)
# Check if the text content has at least word_count_threshold
if len(text_content.split()) >= image_description_min_word_threshold:
return text_content
return None
def process_image(img, url, index, total_images):
#Check if an image has valid display and inside undesired html elements
def is_valid_image(img, parent, parent_classes):
style = img.get('style', '')
src = img.get('src', '')
classes_to_check = ['button', 'icon', 'logo']
tags_to_check = ['button', 'input']
return all([
'display:none' not in style,
src,
not any(s in var for var in [src, img.get('alt', ''), *parent_classes] for s in classes_to_check),
parent.name not in tags_to_check
])
#Score an image for it's usefulness
def score_image_for_usefulness(img, base_url, index, images_count):
# Function to parse image height/width value and units
def parse_dimension(dimension):
if dimension:
match = re.match(r"(\d+)(\D*)", dimension)
if match:
number = int(match.group(1))
unit = match.group(2) or 'px' # Default unit is 'px' if not specified
return number, unit
return None, None
# Fetch image file metadata to extract size and extension
def fetch_image_file_size(img, base_url):
#If src is relative path construct full URL, if not it may be CDN URL
img_url = urljoin(base_url,img.get('src'))
try:
response = requests.head(img_url)
if response.status_code == 200:
return response.headers.get('Content-Length',None)
else:
print(f"Failed to retrieve file size for {img_url}")
return None
except InvalidSchema as e:
return None
finally:
return
image_height = img.get('height')
height_value, height_unit = parse_dimension(image_height)
image_width = img.get('width')
width_value, width_unit = parse_dimension(image_width)
image_size = 0 #int(fetch_image_file_size(img,base_url) or 0)
image_src = img.get('src','')
if "data:image/" in image_src:
image_format = image_src.split(',')[0].split(';')[0].split('/')[1]
else:
image_format = os.path.splitext(img.get('src',''))[1].lower()
# Remove . from format
image_format = image_format.strip('.').split('?')[0]
score = 0
if height_value:
if height_unit == 'px' and height_value > 150:
score += 1
if height_unit in ['%','vh','vmin','vmax'] and height_value >30:
score += 1
if width_value:
if width_unit == 'px' and width_value > 150:
score += 1
if width_unit in ['%','vh','vmin','vmax'] and width_value >30:
score += 1
if image_size > 10000:
score += 1
if img.get('alt') != '':
score+=1
if any(image_format==format for format in ['jpg','png','webp']):
score+=1
if index/images_count<0.5:
score+=1
return score
if not is_valid_image(img, img.parent, img.parent.get('class', [])):
return None
score = score_image_for_usefulness(img, url, index, total_images)
if score <= IMAGE_SCORE_THRESHOLD:
return None
return {
'src': img.get('src', ''),
'data-src': img.get('data-src', ''),
'alt': img.get('alt', ''),
'desc': find_closest_parent_with_useful_text(img),
'score': score,
'type': 'image'
}
def remove_unwanted_attributes(element, important_attrs, keep_data_attributes=False):
attrs_to_remove = []
for attr in element.attrs:
if attr not in important_attrs:
if keep_data_attributes:
if not attr.startswith('data-'):
attrs_to_remove.append(attr)
else:
attrs_to_remove.append(attr)
for attr in attrs_to_remove:
del element[attr]
def process_element(element: element.PageElement) -> bool:
try:
if isinstance(element, NavigableString):
if isinstance(element, Comment):
element.extract()
return False
# if element.name == 'img':
# process_image(element, url, 0, 1)
# return True
if element.name in ['script', 'style', 'link', 'meta', 'noscript']:
element.decompose()
return False
keep_element = False
exclude_social_media_domains = SOCIAL_MEDIA_DOMAINS + kwargs.get('exclude_social_media_domains', [])
exclude_social_media_domains = list(set(exclude_social_media_domains))
try:
if element.name == 'a' and element.get('href'):
href = element.get('href', '').strip()
if not href: # Skip empty hrefs
return False
url_base = url.split('/')[2]
# Normalize the URL
try:
normalized_href = normalize_url(href, url)
except ValueError as e:
# logging.warning(f"Invalid URL format: {href}, Error: {str(e)}")
return False
link_data = {
'href': normalized_href,
'text': element.get_text().strip(),
'title': element.get('title', '').strip()
}
# Check for duplicates and add to appropriate dictionary
is_external = is_external_url(normalized_href, url_base)
if is_external:
if normalized_href not in external_links_dict:
external_links_dict[normalized_href] = link_data
else:
if normalized_href not in internal_links_dict:
internal_links_dict[normalized_href] = link_data
keep_element = True
# Handle external link exclusions
if is_external:
if kwargs.get('exclude_external_links', False):
element.decompose()
return False
elif kwargs.get('exclude_social_media_links', False):
if any(domain in normalized_href.lower() for domain in exclude_social_media_domains):
element.decompose()
return False
elif kwargs.get('exclude_domains', []):
if any(domain in normalized_href.lower() for domain in kwargs.get('exclude_domains', [])):
element.decompose()
return False
except Exception as e:
raise Exception(f"Error processing links: {str(e)}")
try:
if element.name == 'img':
potential_sources = ['src', 'data-src', 'srcset' 'data-lazy-src', 'data-original']
src = element.get('src', '')
while not src and potential_sources:
src = element.get(potential_sources.pop(0), '')
if not src:
element.decompose()
return False
# If it is srcset pick up the first image
if 'srcset' in element.attrs:
src = element.attrs['srcset'].split(',')[0].split(' ')[0]
# Check flag if we should remove external images
if kwargs.get('exclude_external_images', False):
src_url_base = src.split('/')[2]
url_base = url.split('/')[2]
if url_base not in src_url_base:
element.decompose()
return False
if not kwargs.get('exclude_external_images', False) and kwargs.get('exclude_social_media_links', False):
src_url_base = src.split('/')[2]
url_base = url.split('/')[2]
if any(domain in src for domain in exclude_social_media_domains):
element.decompose()
return False
# Handle exclude domains
if kwargs.get('exclude_domains', []):
if any(domain in src for domain in kwargs.get('exclude_domains', [])):
element.decompose()
return False
return True # Always keep image elements
except Exception as e:
raise "Error processing images"
# Check if flag to remove all forms is set
if kwargs.get('remove_forms', False) and element.name == 'form':
element.decompose()
return False
if element.name in ['video', 'audio']:
media[f"{element.name}s"].append({
'src': element.get('src'),
'alt': element.get('alt'),
'type': element.name,
'description': find_closest_parent_with_useful_text(element)
})
source_tags = element.find_all('source')
for source_tag in source_tags:
media[f"{element.name}s"].append({
'src': source_tag.get('src'),
'alt': element.get('alt'),
'type': element.name,
'description': find_closest_parent_with_useful_text(element)
})
return True # Always keep video and audio elements
if element.name in ONLY_TEXT_ELIGIBLE_TAGS:
if kwargs.get('only_text', False):
element.replace_with(element.get_text())
try:
remove_unwanted_attributes(element, IMPORTANT_ATTRS, kwargs.get('keep_data_attributes', False))
except Exception as e:
print('Error removing unwanted attributes:', str(e))
# Process children
for child in list(element.children):
if isinstance(child, NavigableString) and not isinstance(child, Comment):
if len(child.strip()) > 0:
keep_element = True
else:
if process_element(child):
keep_element = True
# Check word count
if not keep_element:
word_count = len(element.get_text(strip=True).split())
keep_element = word_count >= word_count_threshold
if not keep_element:
element.decompose()
return keep_element
except Exception as e:
print('Error processing element:', str(e))
return False
#process images by filtering and extracting contextual text from the page
# imgs = body.find_all('img')
# media['images'] = [
# result for result in
# (process_image(img, url, i, len(imgs)) for i, img in enumerate(imgs))
# if result is not None
# ]
process_element(body)
# Update the links dictionary with unique links
links['internal'] = list(internal_links_dict.values())
links['external'] = list(external_links_dict.values())
# # Process images using ThreadPoolExecutor
imgs = body.find_all('img')
with ThreadPoolExecutor() as executor:
image_results = list(executor.map(process_image, imgs, [url]*len(imgs), range(len(imgs)), [len(imgs)]*len(imgs)))
media['images'] = [result for result in image_results if result is not None]
def flatten_nested_elements(node):
if isinstance(node, NavigableString):
return node
if len(node.contents) == 1 and isinstance(node.contents[0], element.Tag) and node.contents[0].name == node.name:
return flatten_nested_elements(node.contents[0])
node.contents = [flatten_nested_elements(child) for child in node.contents]
return node
body = flatten_nested_elements(body)
base64_pattern = re.compile(r'data:image/[^;]+;base64,([^"]+)')
for img in imgs:
src = img.get('src', '')
if base64_pattern.match(src):
# Replace base64 data with empty string
img['src'] = base64_pattern.sub('', src)
try:
str(body)
except Exception as e:
# Reset body to the original HTML
success = False
body = BeautifulSoup(html, 'html.parser')
# Create a new div with a special ID
error_div = body.new_tag('div', id='crawl4ai_error_message')
error_div.string = '''
Crawl4AI Error: This page is not fully supported.
Possible reasons:
1. The page may have restrictions that prevent crawling.
2. The page might not be fully loaded.
Suggestions:
- Try calling the crawl function with these parameters:
magic=True,
- Set headless=False to visualize what's happening on the page.
If the issue persists, please check the page's structure and any potential anti-crawling measures.
'''
# Append the error div to the body
body.body.append(error_div)
print(f"[LOG] 😧 Error: After processing the crawled HTML and removing irrelevant tags, nothing was left in the page. Check the markdown for further details.")
cleaned_html = str(body).replace('\n\n', '\n').replace(' ', ' ')
try:
h = CustomHTML2Text()
h.update_params(**kwargs.get('html2text', {}))
markdown = h.handle(cleaned_html)
except Exception as e:
markdown = h.handle(sanitize_html(cleaned_html))
markdown = markdown.replace(' ```', '```')
try:
meta = extract_metadata(html, soup)
except Exception as e:
print('Error extracting metadata:', str(e))
meta = {}
cleaner = ContentCleaningStrategy()
fit_html = cleaner.clean(cleaned_html)
fit_markdown = h.handle(fit_html)
cleaned_html = sanitize_html(cleaned_html)
return {
'markdown': markdown,
'fit_markdown': fit_markdown,
'fit_html': fit_html,
'cleaned_html': cleaned_html,
'success': success,
'media': media,
'links': links,
'metadata': meta
}