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import os, time
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from pathlib import Path
from .models import UrlModel, CrawlResult
from .database import init_db, get_cached_url, cache_url, DB_PATH, flush_db
from .utils import *
from .chunking_strategy import *
from .extraction_strategy import *
from .crawler_strategy import *
from typing import List
from concurrent.futures import ThreadPoolExecutor
from .content_scraping_strategy import WebScrapingStrategy
from .config import *
import warnings
import json
warnings.filterwarnings("ignore", message='Field "model_name" has conflict with protected namespace "model_".')
class WebCrawler:
def __init__(self, crawler_strategy: CrawlerStrategy = None, always_by_pass_cache: bool = False, verbose: bool = False):
self.crawler_strategy = crawler_strategy or LocalSeleniumCrawlerStrategy(verbose=verbose)
self.always_by_pass_cache = always_by_pass_cache
self.crawl4ai_folder = os.path.join(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai")
os.makedirs(self.crawl4ai_folder, exist_ok=True)
os.makedirs(f"{self.crawl4ai_folder}/cache", exist_ok=True)
init_db()
self.ready = False
def warmup(self):
print("[LOG] 🌤️ Warming up the WebCrawler")
self.run(
url='https://google.com/',
word_count_threshold=5,
extraction_strategy=NoExtractionStrategy(),
bypass_cache=False,
verbose=False
)
self.ready = True
print("[LOG] 🌞 WebCrawler is ready to crawl")
def fetch_page(
self,
url_model: UrlModel,
provider: str = DEFAULT_PROVIDER,
api_token: str = None,
extract_blocks_flag: bool = True,
word_count_threshold=MIN_WORD_THRESHOLD,
css_selector: str = None,
screenshot: bool = False,
use_cached_html: bool = False,
extraction_strategy: ExtractionStrategy = None,
chunking_strategy: ChunkingStrategy = RegexChunking(),
**kwargs,
) -> CrawlResult:
return self.run(
url_model.url,
word_count_threshold,
extraction_strategy or NoExtractionStrategy(),
chunking_strategy,
bypass_cache=url_model.forced,
css_selector=css_selector,
screenshot=screenshot,
**kwargs,
)
pass
def fetch_pages(
self,
url_models: List[UrlModel],
provider: str = DEFAULT_PROVIDER,
api_token: str = None,
extract_blocks_flag: bool = True,
word_count_threshold=MIN_WORD_THRESHOLD,
use_cached_html: bool = False,
css_selector: str = None,
screenshot: bool = False,
extraction_strategy: ExtractionStrategy = None,
chunking_strategy: ChunkingStrategy = RegexChunking(),
**kwargs,
) -> List[CrawlResult]:
extraction_strategy = extraction_strategy or NoExtractionStrategy()
def fetch_page_wrapper(url_model, *args, **kwargs):
return self.fetch_page(url_model, *args, **kwargs)
with ThreadPoolExecutor() as executor:
results = list(
executor.map(
fetch_page_wrapper,
url_models,
[provider] * len(url_models),
[api_token] * len(url_models),
[extract_blocks_flag] * len(url_models),
[word_count_threshold] * len(url_models),
[css_selector] * len(url_models),
[screenshot] * len(url_models),
[use_cached_html] * len(url_models),
[extraction_strategy] * len(url_models),
[chunking_strategy] * len(url_models),
*[kwargs] * len(url_models),
)
)
return results
def run(
self,
url: str,
word_count_threshold=MIN_WORD_THRESHOLD,
extraction_strategy: ExtractionStrategy = None,
chunking_strategy: ChunkingStrategy = RegexChunking(),
bypass_cache: bool = False,
css_selector: str = None,
screenshot: bool = False,
user_agent: str = None,
verbose=True,
**kwargs,
) -> CrawlResult:
try:
extraction_strategy = extraction_strategy or NoExtractionStrategy()
extraction_strategy.verbose = verbose
if not isinstance(extraction_strategy, ExtractionStrategy):
raise ValueError("Unsupported extraction strategy")
if not isinstance(chunking_strategy, ChunkingStrategy):
raise ValueError("Unsupported chunking strategy")
word_count_threshold = max(word_count_threshold, MIN_WORD_THRESHOLD)
cached = None
screenshot_data = None
extracted_content = None
if not bypass_cache and not self.always_by_pass_cache:
cached = get_cached_url(url)
if kwargs.get("warmup", True) and not self.ready:
return None
if cached:
html = sanitize_input_encode(cached[1])
extracted_content = sanitize_input_encode(cached[4])
if screenshot:
screenshot_data = cached[9]
if not screenshot_data:
cached = None
if not cached or not html:
if user_agent:
self.crawler_strategy.update_user_agent(user_agent)
t1 = time.time()
html = sanitize_input_encode(self.crawler_strategy.crawl(url, **kwargs))
t2 = time.time()
if verbose:
print(f"[LOG] 🚀 Crawling done for {url}, success: {bool(html)}, time taken: {t2 - t1:.2f} seconds")
if screenshot:
screenshot_data = self.crawler_strategy.take_screenshot()
crawl_result = self.process_html(url, html, extracted_content, word_count_threshold, extraction_strategy, chunking_strategy, css_selector, screenshot_data, verbose, bool(cached), **kwargs)
crawl_result.success = bool(html)
return crawl_result
except Exception as e:
if not hasattr(e, "msg"):
e.msg = str(e)
print(f"[ERROR] 🚫 Failed to crawl {url}, error: {e.msg}")
return CrawlResult(url=url, html="", success=False, error_message=e.msg)
def process_html(
self,
url: str,
html: str,
extracted_content: str,
word_count_threshold: int,
extraction_strategy: ExtractionStrategy,
chunking_strategy: ChunkingStrategy,
css_selector: str,
screenshot: bool,
verbose: bool,
is_cached: bool,
**kwargs,
) -> CrawlResult:
t = time.time()
# Extract content from HTML
try:
t1 = time.time()
scrapping_strategy = WebScrapingStrategy()
extra_params = {k: v for k, v in kwargs.items() if k not in ["only_text", "image_description_min_word_threshold"]}
result = scrapping_strategy.scrap(
url,
html,
word_count_threshold=word_count_threshold,
css_selector=css_selector,
only_text=kwargs.get("only_text", False),
image_description_min_word_threshold=kwargs.get(
"image_description_min_word_threshold", IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD
),
**extra_params,
)
# result = get_content_of_website_optimized(url, html, word_count_threshold, css_selector=css_selector, only_text=kwargs.get("only_text", False))
if verbose:
print(f"[LOG] 🚀 Content extracted for {url}, success: True, time taken: {time.time() - t1:.2f} seconds")
if result is None:
raise ValueError(f"Failed to extract content from the website: {url}")
except InvalidCSSSelectorError as e:
raise ValueError(str(e))
cleaned_html = sanitize_input_encode(result.get("cleaned_html", ""))
markdown = sanitize_input_encode(result.get("markdown", ""))
media = result.get("media", [])
links = result.get("links", [])
metadata = result.get("metadata", {})
if extracted_content is None:
if verbose:
print(f"[LOG] 🔥 Extracting semantic blocks for {url}, Strategy: {extraction_strategy.name}")
sections = chunking_strategy.chunk(markdown)
extracted_content = extraction_strategy.run(url, sections)
extracted_content = json.dumps(extracted_content, indent=4, default=str, ensure_ascii=False)
if verbose:
print(f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t:.2f} seconds.")
screenshot = None if not screenshot else screenshot
if not is_cached:
cache_url(
url,
html,
cleaned_html,
markdown,
extracted_content,
True,
json.dumps(media),
json.dumps(links),
json.dumps(metadata),
screenshot=screenshot,
)
return CrawlResult(
url=url,
html=html,
cleaned_html=format_html(cleaned_html),
markdown=markdown,
media=media,
links=links,
metadata=metadata,
screenshot=screenshot,
extracted_content=extracted_content,
success=True,
error_message="",
) |