Files
crawl4ai/crawl4ai/content_scrapping_strategy.py

297 lines
13 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 .utils import (
sanitize_input_encode,
sanitize_html,
extract_metadata,
InvalidCSSSelectorError,
CustomHTML2Text
)
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]:
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': []}
# 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_format = os.path.splitext(img.get('src',''))[1].lower()
# Remove . from format
image_format = image_format.strip('.')
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', ''),
'alt': img.get('alt', ''),
'desc': find_closest_parent_with_useful_text(img),
'score': score,
'type': 'image'
}
def process_element(element: element.PageElement) -> bool:
try:
if isinstance(element, NavigableString):
if isinstance(element, Comment):
element.extract()
return False
if element.name in ['script', 'style', 'link', 'meta', 'noscript']:
if element.name == 'img':
process_image(element, url, 0, 1)
element.decompose()
return False
keep_element = False
if element.name == 'a' and element.get('href'):
href = element['href']
url_base = url.split('/')[2]
link_data = {'href': href, 'text': element.get_text()}
if href.startswith('http') and url_base not in href:
links['external'].append(link_data)
else:
links['internal'].append(link_data)
keep_element = True
elif element.name == 'img':
return True # Always keep image elements
elif 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 != 'pre':
if element.name in ['b', 'i', 'u', 'span', 'del', 'ins', 'sub', 'sup', 'strong', 'em', 'code', 'kbd', 'var', 's', 'q', 'abbr', 'cite', 'dfn', 'time', 'small', 'mark']:
if kwargs.get('only_text', False):
element.replace_with(element.get_text())
else:
element.unwrap()
elif element.name != 'img':
element.attrs = {}
# 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)
# # 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)
cleaned_html = str(body).replace('\n\n', '\n').replace(' ', ' ')
cleaned_html = sanitize_html(cleaned_html)
h = CustomHTML2Text()
h.ignore_links = True
markdown = h.handle(cleaned_html)
markdown = markdown.replace(' ```', '```')
try:
meta = extract_metadata(html, soup)
except Exception as e:
print('Error extracting metadata:', str(e))
meta = {}
return {
'markdown': markdown,
'cleaned_html': cleaned_html,
'success': True,
'media': media,
'links': links,
'metadata': meta
}