Playing with different Docker settings to find the best one

- Extract Links
- Extract Metadata
- Update Readme
- Update REST API document

.gitignore

@@ -179,3 +179,6 @@ docs/examples/.chainlit/
 docs/examples/.chainlit/*
 .chainlit/config.toml
 .chainlit/translations/en-US.json
+
+local/
+.files/

CHANGELOG.md

@@ -1 +1,5 @@
-# Changelog
+# Changelog
+
+## [0.2.4] - 2024-06-17
+### Fixed
+- Fix issue #22: Use MD5 hash for caching HTML files to handle long URLs

DockerfileTest

@@ -0,0 +1,37 @@
+
+# First stage: Build and install dependencies
+FROM python:3.10-slim-bookworm as builder
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+# Install build dependencies
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    wget \
+    curl \
+    unzip 
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt && \
+    pip install --no-cache-dir spacy
+
+# Copy the rest of the application code
+COPY . .
+
+# Set environment to use Chrome and ChromeDriver properly
+ENV CHROME_BIN=/usr/bin/google-chrome \
+    CHROMEDRIVER=/usr/local/bin/chromedriver \
+    DISPLAY=:99 \
+    DBUS_SESSION_BUS_ADDRESS=/dev/null \
+    PYTHONUNBUFFERED=1
+
+# Ensure the PATH environment variable includes the location of the installed packages
+ENV PATH /usr/local/bin:$PATH   
+
+# Make port 80 available to the world outside this container
+EXPOSE 80
+
+# Print helloworld when the container launches
+CMD ["echo", "Hello, World!"]

DockerfileTest2

@@ -0,0 +1,73 @@
+# First stage: Build and install dependencies
+FROM pytorch/pytorch:latest as builder
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+# Install build dependencies
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    wget \
+    git \
+    curl \
+    unzip \
+    gnupg \
+    xvfb \
+    ca-certificates \
+    apt-transport-https \
+    software-properties-common && \
+    rm -rf /var/lib/apt/lists/*    
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt && \
+    pip install --no-cache-dir spacy onnxruntime && \
+    python -m spacy download en_core_web_sm
+
+# Install Google Chrome and ChromeDriver
+RUN wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add - && \
+    sh -c 'echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google-chrome.list' && \
+    apt-get update && \
+    apt-get install -y google-chrome-stable && \
+    wget -O /tmp/chromedriver.zip http://chromedriver.storage.googleapis.com/`curl -sS chromedriver.storage.googleapis.com/LATEST_RELEASE`/chromedriver_linux64.zip && \
+    unzip /tmp/chromedriver.zip chromedriver -d /usr/local/bin/
+
+# Second stage: Create the final image
+FROM pytorch/pytorch:latest
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+# Copy Chromedriver and Chrome from the builder stage
+COPY --from=builder /usr/local/bin/chromedriver /usr/local/bin/chromedriver
+COPY --from=builder /usr/bin/google-chrome /usr/bin/google-chrome
+
+# Copy installed Python packages from builder stage
+COPY --from=builder /opt/conda/lib/python3.10/site-packages /opt/conda/lib/python3.10/site-packages
+COPY --from=builder /opt/conda/bin /opt/conda/bin
+
+# Copy the rest of the application code
+COPY . .
+
+# Set environment to use Chrome and ChromeDriver properly
+ENV CHROME_BIN=/usr/bin/google-chrome \
+    CHROMEDRIVER=/usr/local/bin/chromedriver \
+    DISPLAY=:99 \
+    DBUS_SESSION_BUS_ADDRESS=/dev/null \
+    PYTHONUNBUFFERED=1
+
+#  pip install -e .[all]
+RUN pip install --no-cache-dir -e .[all]
+
+# Ensure the PATH environment variable includes the location of the installed packages
+ENV PATH /opt/conda/bin:$PATH   
+
+# Make port 80 available to the world outside this container
+EXPOSE 80
+
+# Download models call cli "crawl4ai-download-models"
+RUN crawl4ai-download-models
+# RUN python crawl4ai/model_loader.py
+
+# Run uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--workers", "4"]

DockerfileTest3

@@ -0,0 +1,61 @@
+# First stage: Build and install dependencies
+FROM pytorch/pytorch:latest 
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+# Install build dependencies
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    wget \
+    git \
+    curl \
+    unzip \
+    gnupg \
+    xvfb \
+    ca-certificates \
+    apt-transport-https \
+    software-properties-common && \
+    rm -rf /var/lib/apt/lists/*    
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt && \
+    pip install --no-cache-dir spacy onnxruntime && \
+    python -m spacy download en_core_web_sm
+
+# Install Google Chrome and ChromeDriver
+RUN wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add - && \
+    sh -c 'echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google-chrome.list' && \
+    apt-get update && \
+    apt-get install -y google-chrome-stable && \
+    wget -O /tmp/chromedriver.zip http://chromedriver.storage.googleapis.com/`curl -sS chromedriver.storage.googleapis.com/LATEST_RELEASE`/chromedriver_linux64.zip && \
+    unzip /tmp/chromedriver.zip chromedriver -d /usr/local/bin/
+
+# Copy the rest of the application code
+COPY . .
+
+# Set environment to use Chrome and ChromeDriver properly
+ENV CHROME_BIN=/usr/bin/google-chrome \
+    CHROMEDRIVER=/usr/local/bin/chromedriver \
+    DISPLAY=:99 \
+    DBUS_SESSION_BUS_ADDRESS=/dev/null \
+    PYTHONUNBUFFERED=1
+
+#  pip install -e .[all]
+RUN pip install --no-cache-dir -e .[all]
+
+# Ensure the PATH environment variable includes the location of the installed packages
+ENV PATH /opt/conda/bin:$PATH   
+
+# Make port 80 available to the world outside this container
+EXPOSE 80
+
+# Download models call cli "crawl4ai-download-models"
+RUN crawl4ai-download-models
+# RUN python crawl4ai/model_loader.py
+
+# Run uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--workers", "4"]
+
+

README.md

@@ -8,13 +8,20 @@
 
 Crawl4AI has one clear task: to simplify crawling and extract useful information from web pages, making it accessible for large language models (LLMs) and AI applications. 🆓🌐
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wz8u30rvbq6Scodye9AGCw8Qg_Z8QGsk)
+- Use as REST API: Check  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zODYjhemJ5bUmYceWpVoBMVpd0ofzNBZ?usp=sharing)
+- Use as Python library: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wz8u30rvbq6Scodye9AGCw8Qg_Z8QGsk)
 
 ## Recent Changes 
 
+### v0.2.4
+- 🐞 Resolve the issue with the long url. (Issue #22)
+
 ### v0.2.3
 - 🎨 Extract and return all media tags (Images, Audio, and Video). Check `result.media`
-- 🖼️ Take [screenshots](#taking-screenshots-) of the page.
+- 🔗 Extrat all external and internal links. Check `result.links`
+- 📚 Extract metadata from the page. Check `result.metadata`
+- 🕵️ Support `user_agent` parameter to set the user agent for the HTTP requests.
+- 🖼️ Take [screenshots](#taking-screenshots) of the page.
 
 ### v0.2.2
 - Support multiple JS scripts
@@ -32,7 +39,27 @@ Crawl4AI has one clear task: to simplify crawling and extract useful information
 
 ## Power and Simplicity of Crawl4AI 🚀
 
-To show the simplicity take a look at the first example:
+The most easy way! If you don't want to install any library, you can use the REST API on my server. But remember, this is just a simple server. I may improve its capacity if I see there is demand. You can find ll examples of REST API in this colab notebook. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zODYjhemJ5bUmYceWpVoBMVpd0ofzNBZ?usp=sharing)
+
+```python
+import requests
+
+data = {
+  "urls": [
+    "https://www.nbcnews.com/business"
+  ],
+  "screenshot": True
+}
+
+response = requests.post("https://crawl4ai.com/crawl", json=data) # OR local host if your run locally 
+response_data = response.json()
+print(response_data['results'][0].keys())
+# dict_keys(['url', 'html', 'success', 'cleaned_html', 'media', 
+# 'links', 'screenshot', 'markdown', 'extracted_content', 
+# 'metadata', 'error_message'])
+```
+
+But you muore control then take a look at the first example of using the Python library.
 
 ```python
 from crawl4ai import WebCrawler
@@ -42,24 +69,7 @@ crawler = WebCrawler()
 
 # Run the crawler with keyword filtering and CSS selector
 result = crawler.run(url="https://www.nbcnews.com/business")
-print(result) # {url, html, markdown, extracted_content, metadata}
+print(result) # {url, html, cleaned_html, markdown, media, links, extracted_content, metadata, screenshots}
-```
-
-If you don't want to install Selenium, you can use the REST API or local server. 
-
-```python
-import requests
-
-data = {
-  "urls": [
-    "https://www.nbcnews.com/business"
-  ],
-  "word_count_threshold": 10,
-  "extraction_strategy": "NoExtractionStrategy",
-}
-
-response = requests.post("https://crawl4ai.com/crawl", json=data) # OR local host if your run locally 
-print(response.json())
 ```
 
 Now let's try a complex task. Below is an example of how you can execute JavaScript, filter data using keywords, and use a CSS selector to extract specific content—all in one go!
@@ -77,20 +87,17 @@ from crawl4ai.extraction_strategy import *
 from crawl4ai.crawler_strategy import *
 
 # Define the JavaScript code to click the "Load More" button
-js_code = """
+js_code = ["""
 const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
 loadMoreButton && loadMoreButton.click();
-"""
+"""]
-
-# Define the crawling strategy
-crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
-
-# Create the WebCrawler instance with the defined strategy
-crawler = WebCrawler(crawler_strategy=crawler_strategy)
 
+crawler = WebCrawler(verbose=True)
+crawler.warmup()
 # Run the crawler with keyword filtering and CSS selector
 result = crawler.run(
     url="https://www.nbcnews.com/business",
+    js = js_code,
     extraction_strategy=CosineStrategy(
         semantic_filter="technology",
     ),
@@ -99,6 +106,7 @@ result = crawler.run(
 # Run the crawler with LLM extraction strategy
 result = crawler.run(
     url="https://www.nbcnews.com/business",
+    js = js_code,
     extraction_strategy=LLMExtractionStrategy(
         provider="openai/gpt-4o",
         api_token=os.getenv('OPENAI_API_KEY'),
@@ -180,7 +188,7 @@ pip install -e .[all]
 # docker build --platform linux/amd64 -t crawl4ai .
 # For other users
 # docker build -t crawl4ai .
-docker run -d -p 8000:80 crawl4ai
+docker run -d -p 8000:80 --name crawl4ai_container_1 crawl4ai
 ```
 
 
@@ -226,8 +234,12 @@ To use the REST API, send a POST request to `http://localhost:8000/crawl` with t
             "url": "https://www.nbcnews.com/business",
             "extracted_content": "...",
             "html": "...",
+            "cleaned_html": "...",
             "markdown": "...",
-            "metadata": {...}
+            "media": {...},
+            "links": {...},
+            "metadata": {...},
+            "screenshots": "...",
         }
     ]
 }
@@ -266,6 +278,24 @@ Crawl result without raw HTML content:
 result = crawler.run(url="https://www.nbcnews.com/business", include_raw_html=False)
 ```
 
+### Result Structure
+
+The result object contains the following fields:
+```python
+class CrawlResult(BaseModel):
+    url: str
+    html: str
+    success: bool
+    cleaned_html: Optional[str] = None
+    media: Dict[str, List[Dict]] = {} # Media tags in the page {"images": [], "audio": [], "video": []}
+    links: Dict[str, List[Dict]] = {} # Links in the page {"external": [], "internal": []}
+    screenshot: Optional[str] = None # Base64 encoded screenshot
+    markdown: Optional[str] = None
+    extracted_content: Optional[str] = None
+    metadata: Optional[dict] = None
+    error_message: Optional[str] = None
+```
+
 ### Taking Screenshots
 
 ```python
@@ -385,6 +415,7 @@ result = crawler.run(url="https://www.nbcnews.com/business")
 | `extraction_strategy` | The strategy to use for extracting content from the HTML (e.g., "CosineStrategy").                    | No       | `NoExtractionStrategy`    |
 | `chunking_strategy`   | The strategy to use for chunking the text before processing (e.g., "RegexChunking").                  | No       | `RegexChunking`     |
 | `css_selector`        | The CSS selector to target specific parts of the HTML for extraction.                                 | No       | `None`              |
+| `user_agent`          | The user agent to use for the HTTP requests.                                                          | No       | `Mozilla/5.0`       |
 | `verbose`             | Whether to enable verbose logging.                                                                    | No       | `true`              |
 
 ## Chunking Strategies 📚

crawl4ai/crawler_strategy.py

@@ -44,6 +44,10 @@ class CrawlerStrategy(ABC):
     @abstractmethod
     def take_screenshot(self, save_path: str):
         pass
+    
+    @abstractmethod
+    def update_user_agent(self, user_agent: str):
+        pass
 
 class CloudCrawlerStrategy(CrawlerStrategy):
     def __init__(self, use_cached_html = False):
@@ -69,6 +73,8 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
         print("[LOG] 🚀 Initializing LocalSeleniumCrawlerStrategy")
         self.options = Options()
         self.options.headless = True
+        if kwargs.get("user_agent"):
+            self.options.add_argument("--user-agent=" + kwargs.get("user_agent"))
         self.options.add_argument("--no-sandbox")
         self.options.add_argument("--headless")
         # self.options.add_argument("--disable-dev-shm-usage")
@@ -97,9 +103,18 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
         self.service.log_path = "NUL"
         self.driver = webdriver.Chrome(service=self.service, options=self.options)
 
+    def update_user_agent(self, user_agent: str):
+        self.options.add_argument(f"user-agent={user_agent}")
+        self.driver.quit()
+        self.driver = webdriver.Chrome(service=self.service, options=self.options)
+
     def crawl(self, url: str) -> str:
+        # Create md5 hash of the URL
+        import hashlib
+        url_hash = hashlib.md5(url.encode()).hexdigest()
+        
         if self.use_cached_html:
-            cache_file_path = os.path.join(Path.home(), ".crawl4ai", "cache", url.replace("/", "_"))
+            cache_file_path = os.path.join(Path.home(), ".crawl4ai", "cache", url_hash)
             if os.path.exists(cache_file_path):
                 with open(cache_file_path, "r") as f:
                     return f.read()
@@ -129,7 +144,7 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
             html = self.driver.page_source
             
             # Store in cache
-            cache_file_path = os.path.join(Path.home(), ".crawl4ai", "cache", url.replace("/", "_"))
+            cache_file_path = os.path.join(Path.home(), ".crawl4ai", "cache", url_hash)
             with open(cache_file_path, "w") as f:
                 f.write(html)
                 

crawl4ai/database.py

@@ -20,6 +20,8 @@ def init_db():
             extracted_content TEXT,
             success BOOLEAN,
             media TEXT DEFAULT "{}",
+            link TEXT DEFAULT "{}",
+            metadata TEXT DEFAULT "{}",
             screenshot TEXT DEFAULT ""
         )
     ''')
@@ -41,12 +43,12 @@ def check_db_path():
     if not DB_PATH:
         raise ValueError("Database path is not set or is empty.")
 
-def get_cached_url(url: str) -> Optional[Tuple[str, str, str, str, str, bool, str]]:
+def get_cached_url(url: str) -> Optional[Tuple[str, str, str, str, str, str, str, bool, str]]:
     check_db_path()
     try:
         conn = sqlite3.connect(DB_PATH)
         cursor = conn.cursor()
-        cursor.execute('SELECT url, html, cleaned_html, markdown, extracted_content, success, media, screenshot FROM crawled_data WHERE url = ?', (url,))
+        cursor.execute('SELECT url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot FROM crawled_data WHERE url = ?', (url,))
         result = cursor.fetchone()
         conn.close()
         return result
@@ -54,23 +56,25 @@ def get_cached_url(url: str) -> Optional[Tuple[str, str, str, str, str, bool, st
         print(f"Error retrieving cached URL: {e}")
         return None
 
-def cache_url(url: str, html: str, cleaned_html: str, markdown: str, extracted_content: str, success: bool, media : str = "{}", screenshot: str = ""):
+def cache_url(url: str, html: str, cleaned_html: str, markdown: str, extracted_content: str, success: bool, media : str = "{}", links : str = "{}", metadata : str = "{}", screenshot: str = ""):
     check_db_path()
     try:
         conn = sqlite3.connect(DB_PATH)
         cursor = conn.cursor()
         cursor.execute('''
-            INSERT INTO crawled_data (url, html, cleaned_html, markdown, extracted_content, success, screenshot)
+            INSERT INTO crawled_data (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot)
-            VALUES (?, ?, ?, ?, ?, ?, ?)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
             ON CONFLICT(url) DO UPDATE SET
                 html = excluded.html,
                 cleaned_html = excluded.cleaned_html,
                 markdown = excluded.markdown,
                 extracted_content = excluded.extracted_content,
                 success = excluded.success,
-                media = excluded.media,                
+                media = excluded.media,      
+                links = excluded.links,    
+                metadata = excluded.metadata,      
                 screenshot = excluded.screenshot
-        ''', (url, html, cleaned_html, markdown, extracted_content, success, media, screenshot))
+        ''', (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot))
         conn.commit()
         conn.close()
     except Exception as e:
@@ -124,5 +128,5 @@ def update_existing_records(new_column: str = "media", default_value: str = "{}"
 
 if __name__ == "__main__":
     init_db()  # Initialize the database if not already initialized
-    alter_db_add_screenshot()  # Add the new column to the table
+    alter_db_add_screenshot("metadata")  # Add the new column to the table
-    update_existing_records()  # Update existing records to set the new column to an empty string
+    update_existing_records("metadata")  # Update existing records to set the new column to an empty string

crawl4ai/model_loader.py

@@ -53,7 +53,6 @@ def set_model_device(model):
     model.to(device)    
     return model, device
 
-@lru_cache()
 def get_home_folder():
     home_folder = os.path.join(Path.home(), ".crawl4ai")
     os.makedirs(home_folder, exist_ok=True)
@@ -202,7 +201,7 @@ def load_spacy_model():
         repo_folder = os.path.join(home_folder, "crawl4ai")
         model_folder = os.path.join(home_folder, name)
 
-        # print("[LOG] ⏬ Downloading Spacy model for the first time...")
+        print("[LOG] ⏬ Downloading Spacy model for the first time...")
 
         # Remove existing repo folder if it exists
         if Path(repo_folder).exists():
@@ -230,7 +229,7 @@ def load_spacy_model():
             shutil.rmtree(repo_folder)
 
             # Print completion message
-            # print("[LOG] ✅ Spacy Model downloaded successfully")
+            print("[LOG] ✅ Spacy Model downloaded successfully")
         except subprocess.CalledProcessError as e:
             print(f"An error occurred while cloning the repository: {e}")
         except Exception as e:
@@ -255,8 +254,8 @@ def download_all_models(remove_existing=False):
     # Load each model to trigger download
     # print("[LOG] Downloading BERT Base Uncased...")
     # load_bert_base_uncased()
-    # print("[LOG] Downloading BGE Small EN v1.5...")
+    print("[LOG] Downloading BGE Small EN v1.5...")
-    # load_bge_small_en_v1_5()
+    load_bge_small_en_v1_5()
     # print("[LOG] Downloading ONNX model...")
     # load_onnx_all_MiniLM_l6_v2()
     print("[LOG] Downloading text classifier...")

crawl4ai/models.py

@@ -11,6 +11,7 @@ class CrawlResult(BaseModel):
     success: bool
     cleaned_html: Optional[str] = None
     media: Dict[str, List[Dict]] = {}
+    links: Dict[str, List[Dict]] = {}
     screenshot: Optional[str] = None
     markdown: Optional[str] = None
     extracted_content: Optional[str] = None

crawl4ai/utils.py

@@ -151,7 +151,7 @@ class CustomHTML2Text(HTML2Text):
 
         super().handle_tag(tag, attrs, start)
 
-def get_content_of_website(html, word_count_threshold = MIN_WORD_THRESHOLD, css_selector = None):
+def get_content_of_website(url, html, word_count_threshold = MIN_WORD_THRESHOLD, css_selector = None):
     try:
         if not html:
             return None
@@ -170,6 +170,28 @@ def get_content_of_website(html, word_count_threshold = MIN_WORD_THRESHOLD, css_
             for el in selected_elements:
                 div_tag.append(el)
             body = div_tag
+            
+        links = {
+            'internal': [],
+            'external': []
+        }
+        
+        # Extract all internal and external links
+        for a in body.find_all('a', href=True):
+            href = a['href']
+            url_base = url.split('/')[2]
+            if href.startswith('http') and url_base not in href:
+                links['external'].append({
+                    'href': href,
+                    'text': a.get_text()
+                })
+            else:
+                links['internal'].append(
+                    {
+                        'href': href,
+                        'text': a.get_text()
+                    }
+                )
 
         # Remove script, style, and other tags that don't carry useful content from body
         for tag in body.find_all(['script', 'style', 'link', 'meta', 'noscript']):
@@ -329,13 +351,55 @@ def get_content_of_website(html, word_count_threshold = MIN_WORD_THRESHOLD, css_
             'markdown': markdown,
             'cleaned_html': cleaned_html,
             'success': True,
-            'media': media
+            'media': media,
+            'links': links
         }
 
     except Exception as e:
         print('Error processing HTML content:', str(e))
         raise InvalidCSSSelectorError(f"Invalid CSS selector: {css_selector}") from e
 
+
+
+def extract_metadata(html):
+    metadata = {}
+    
+    if not html:
+        return metadata
+    
+    # Parse HTML content with BeautifulSoup
+    soup = BeautifulSoup(html, 'html.parser')
+
+    # Title
+    title_tag = soup.find('title')
+    metadata['title'] = title_tag.string if title_tag else None
+
+    # Meta description
+    description_tag = soup.find('meta', attrs={'name': 'description'})
+    metadata['description'] = description_tag['content'] if description_tag else None
+
+    # Meta keywords
+    keywords_tag = soup.find('meta', attrs={'name': 'keywords'})
+    metadata['keywords'] = keywords_tag['content'] if keywords_tag else None
+
+    # Meta author
+    author_tag = soup.find('meta', attrs={'name': 'author'})
+    metadata['author'] = author_tag['content'] if author_tag else None
+
+    # Open Graph metadata
+    og_tags = soup.find_all('meta', attrs={'property': lambda value: value and value.startswith('og:')})
+    for tag in og_tags:
+        property_name = tag['property']
+        metadata[property_name] = tag['content']
+
+    # Twitter Card metadata
+    twitter_tags = soup.find_all('meta', attrs={'name': lambda value: value and value.startswith('twitter:')})
+    for tag in twitter_tags:
+        property_name = tag['name']
+        metadata[property_name] = tag['content']
+
+    return metadata
+
 def extract_xml_tags(string):
     tags = re.findall(r'<(\w+)>', string)
     return list(set(tags))

crawl4ai/web_crawler.back.py

@@ -0,0 +1,357 @@
+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 .config import *
+
+
+class WebCrawler:
+    def __init__(
+        self,
+        # db_path: str = None,
+        crawler_strategy: CrawlerStrategy = None,
+        always_by_pass_cache: bool = False,
+        verbose: bool = False,
+    ):
+        # self.db_path = db_path
+        self.crawler_strategy = crawler_strategy or LocalSeleniumCrawlerStrategy(verbose=verbose)
+        self.always_by_pass_cache = always_by_pass_cache
+
+        # Create the .crawl4ai folder in the user's home directory if it doesn't exist
+        self.crawl4ai_folder = os.path.join(Path.home(), ".crawl4ai")
+        os.makedirs(self.crawl4ai_folder, exist_ok=True)
+        os.makedirs(f"{self.crawl4ai_folder}/cache", exist_ok=True)
+
+        # If db_path is not provided, use the default path
+        # if not db_path:
+            # self.db_path = f"{self.crawl4ai_folder}/crawl4ai.db"
+        
+        # flush_db()
+        init_db()
+        
+        self.ready = False
+        
+    def warmup(self):
+        print("[LOG] 🌤️  Warming up the WebCrawler")
+        result = self.run(
+            url='https://crawl4ai.uccode.io/',
+            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 run_old(
+        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:
+        if user_agent:
+            self.crawler_strategy.update_user_agent(user_agent)
+        extraction_strategy = extraction_strategy or NoExtractionStrategy()
+        extraction_strategy.verbose = verbose
+        # Check if extraction strategy is an instance of ExtractionStrategy if not raise an error
+        if not isinstance(extraction_strategy, ExtractionStrategy):
+            raise ValueError("Unsupported extraction strategy")
+        if not isinstance(chunking_strategy, ChunkingStrategy):
+            raise ValueError("Unsupported chunking strategy")
+        
+        # make sure word_count_threshold is not lesser than MIN_WORD_THRESHOLD
+        if word_count_threshold < MIN_WORD_THRESHOLD:
+            word_count_threshold = MIN_WORD_THRESHOLD
+
+        # Check cache first
+        if not bypass_cache and not self.always_by_pass_cache:
+            cached = get_cached_url(url)
+            if cached:
+                return CrawlResult(
+                    **{
+                        "url": cached[0],
+                        "html": cached[1],
+                        "cleaned_html": cached[2],
+                        "markdown": cached[3],
+                        "extracted_content": cached[4],
+                        "success": cached[5],
+                        "media": json.loads(cached[6] or "{}"),
+                        "links": json.loads(cached[7] or "{}"),
+                        "metadata": json.loads(cached[8] or "{}"), # "metadata": "{}
+                        "screenshot": cached[9],
+                        "error_message": "",
+                    }
+                )
+
+        # Initialize WebDriver for crawling
+        t = time.time()
+        if kwargs.get("js", None):
+            self.crawler_strategy.js_code = kwargs.get("js")
+        html = self.crawler_strategy.crawl(url)
+        base64_image = None
+        if screenshot:
+            base64_image = self.crawler_strategy.take_screenshot()
+        success = True
+        error_message = ""
+        # Extract content from HTML
+        try:
+            result = get_content_of_website(url, html, word_count_threshold, css_selector=css_selector)
+            metadata = extract_metadata(html)
+            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 = result.get("cleaned_html", "")
+        markdown = result.get("markdown", "")
+        media = result.get("media", [])
+        links = result.get("links", [])
+
+        # Print a profession LOG style message, show time taken and say crawling is done
+        if verbose:
+            print(
+                f"[LOG] 🚀 Crawling done for {url}, success: {success}, time taken: {time.time() - t} seconds"
+            )
+
+        extracted_content = []
+        if verbose:
+            print(f"[LOG] 🔥 Extracting semantic blocks for {url}, Strategy: {extraction_strategy.name}")
+        t = time.time()
+        # Split markdown into sections
+        sections = chunking_strategy.chunk(markdown)
+        # sections = merge_chunks_based_on_token_threshold(sections, CHUNK_TOKEN_THRESHOLD)
+
+        extracted_content = extraction_strategy.run(
+            url, sections,
+        )
+        extracted_content = json.dumps(extracted_content)
+
+        if verbose:
+            print(
+                f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t} seconds."
+            )
+
+        # Cache the result
+        cleaned_html = beautify_html(cleaned_html)
+        cache_url(
+            url,
+            html,
+            cleaned_html,
+            markdown,
+            extracted_content,
+            success,
+            json.dumps(media),
+            json.dumps(links),
+            json.dumps(metadata),
+            screenshot=base64_image,
+        )
+
+        return CrawlResult(
+            url=url,
+            html=html,
+            cleaned_html=cleaned_html,
+            markdown=markdown,
+            media=media,
+            links=links,
+            metadata=metadata,
+            screenshot=base64_image,
+            extracted_content=extracted_content,
+            success=success,
+            error_message=error_message,
+        )
+
+    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:
+            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")
+            
+            if word_count_threshold < MIN_WORD_THRESHOLD:
+                word_count_threshold = MIN_WORD_THRESHOLD
+
+            # Check cache first
+            cached = None
+            extracted_content = None
+            if not bypass_cache and not self.always_by_pass_cache:
+                cached = get_cached_url(url)
+            
+            if cached:
+                html = cached[1]
+                extracted_content = cached[2]
+                if screenshot:
+                    screenshot = cached[9]
+            
+            else:
+                if user_agent:
+                    self.crawler_strategy.update_user_agent(user_agent)
+                html = self.crawler_strategy.crawl(url)
+                if screenshot:
+                    screenshot = self.crawler_strategy.take_screenshot()
+            
+            return self.process_html(url, html, extracted_content, word_count_threshold, extraction_strategy, chunking_strategy, css_selector, screenshot, verbose, bool(cached), **kwargs)
+
+    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:
+                result = get_content_of_website(url, html, word_count_threshold, css_selector=css_selector)
+                metadata = extract_metadata(html)
+                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 = result.get("cleaned_html", "")
+            markdown = result.get("markdown", "")
+            media = result.get("media", [])
+            links = result.get("links", [])
+
+            if verbose:
+                print(f"[LOG] 🚀 Crawling done for {url}, success: True, time taken: {time.time() - t} seconds")
+                        
+            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)
+
+                if verbose:
+                    print(f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t} 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=cleaned_html,
+                markdown=markdown,
+                media=media,
+                links=links,
+                metadata=metadata,
+                screenshot=screenshot,
+                extracted_content=extracted_content,
+                success=True,
+                error_message="",
+            )

crawl4ai/web_crawler.py

@@ -51,7 +51,6 @@ class WebCrawler:
         self.ready = True
         print("[LOG] 🌞 WebCrawler is ready to crawl")
         
-
     def fetch_page(
         self,
         url_model: UrlModel,
@@ -78,118 +77,6 @@ class WebCrawler:
         )
         pass
 
-
-    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,
-        verbose=True,
-        **kwargs,
-    ) -> CrawlResult:
-        extraction_strategy = extraction_strategy or NoExtractionStrategy()
-        extraction_strategy.verbose = verbose
-        # Check if extraction strategy is an instance of ExtractionStrategy if not raise an error
-        if not isinstance(extraction_strategy, ExtractionStrategy):
-            raise ValueError("Unsupported extraction strategy")
-        if not isinstance(chunking_strategy, ChunkingStrategy):
-            raise ValueError("Unsupported chunking strategy")
-        
-        # make sure word_count_threshold is not lesser than MIN_WORD_THRESHOLD
-        if word_count_threshold < MIN_WORD_THRESHOLD:
-            word_count_threshold = MIN_WORD_THRESHOLD
-
-        # Check cache first
-        if not bypass_cache and not self.always_by_pass_cache:
-            cached = get_cached_url(url)
-            if cached:
-                return CrawlResult(
-                    **{
-                        "url": cached[0],
-                        "html": cached[1],
-                        "cleaned_html": cached[2],
-                        "markdown": cached[3],
-                        "extracted_content": cached[4],
-                        "success": cached[5],
-                        "media": json.loads(cached[6] or "{}"),
-                        "screenshot": cached[7],
-                        "error_message": "",
-                    }
-                )
-
-        # Initialize WebDriver for crawling
-        t = time.time()
-        html = self.crawler_strategy.crawl(url)
-        base64_image = None
-        if screenshot:
-            base64_image = self.crawler_strategy.take_screenshot()
-        success = True
-        error_message = ""
-        # Extract content from HTML
-        try:
-            result = get_content_of_website(html, word_count_threshold, css_selector=css_selector)
-            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 = result.get("cleaned_html", html)
-        markdown = result.get("markdown", "")
-        media = result.get("media", [])
-
-        # Print a profession LOG style message, show time taken and say crawling is done
-        if verbose:
-            print(
-                f"[LOG] 🚀 Crawling done for {url}, success: {success}, time taken: {time.time() - t} seconds"
-            )
-
-        extracted_content = []
-        if verbose:
-            print(f"[LOG] 🔥 Extracting semantic blocks for {url}, Strategy: {extraction_strategy.name}")
-        t = time.time()
-        # Split markdown into sections
-        sections = chunking_strategy.chunk(markdown)
-        # sections = merge_chunks_based_on_token_threshold(sections, CHUNK_TOKEN_THRESHOLD)
-
-        extracted_content = extraction_strategy.run(
-            url, sections,
-        )
-        extracted_content = json.dumps(extracted_content)
-
-        if verbose:
-            print(
-                f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t} seconds."
-            )
-
-        # Cache the result
-        cleaned_html = beautify_html(cleaned_html)
-        cache_url(
-            url,
-            html,
-            cleaned_html,
-            markdown,
-            extracted_content,
-            success,
-            json.dumps(media),
-            screenshot=base64_image,
-        )
-
-        return CrawlResult(
-            url=url,
-            html=html,
-            cleaned_html=cleaned_html,
-            markdown=markdown,
-            media=media,
-            screenshot=base64_image,
-            extracted_content=extracted_content,
-            success=success,
-            error_message=error_message,
-        )
-
     def fetch_pages(
         self,
         url_models: List[UrlModel],
@@ -227,3 +114,120 @@ class WebCrawler:
             )
 
         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:
+            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")
+            
+            if word_count_threshold < MIN_WORD_THRESHOLD:
+                word_count_threshold = MIN_WORD_THRESHOLD
+
+            # Check cache first
+            cached = None
+            extracted_content = None
+            if not bypass_cache and not self.always_by_pass_cache:
+                cached = get_cached_url(url)
+            
+            if cached:
+                html = cached[1]
+                extracted_content = cached[2]
+                if screenshot:
+                    screenshot = cached[9]
+            
+            else:
+                if user_agent:
+                    self.crawler_strategy.update_user_agent(user_agent)
+                html = self.crawler_strategy.crawl(url)
+                if screenshot:
+                    screenshot = self.crawler_strategy.take_screenshot()
+            
+            return self.process_html(url, html, extracted_content, word_count_threshold, extraction_strategy, chunking_strategy, css_selector, screenshot, verbose, bool(cached), **kwargs)
+
+    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:
+                result = get_content_of_website(url, html, word_count_threshold, css_selector=css_selector)
+                metadata = extract_metadata(html)
+                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 = result.get("cleaned_html", "")
+            markdown = result.get("markdown", "")
+            media = result.get("media", [])
+            links = result.get("links", [])
+
+            if verbose:
+                print(f"[LOG] 🚀 Crawling done for {url}, success: True, time taken: {time.time() - t} seconds")
+                        
+            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)
+
+                if verbose:
+                    print(f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t} 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=cleaned_html,
+                markdown=markdown,
+                media=media,
+                links=links,
+                metadata=metadata,
+                screenshot=screenshot,
+                extracted_content=extracted_content,
+                success=True,
+                error_message="",
+            )

docs/examples/quickstart.py

@@ -166,10 +166,11 @@ def interactive_extraction(crawler):
     const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
     loadMoreButton && loadMoreButton.click();
     """
-    crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
+    # crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
-    crawler = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=True)
+    # crawler = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=True)
     result = crawler.run(
         url="https://www.nbcnews.com/business",
+        js = js_code
     )
     cprint("[LOG] 📦 [bold yellow]JavaScript Code (Load More button) result:[/bold yellow]")
     print_result(result)
@@ -182,10 +183,11 @@ def multiple_scrip(crawler):
     const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
     loadMoreButton && loadMoreButton.click();
     """] * 2
-    crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
+    # crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
-    crawler = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=True)
+    # crawler = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=True)
     result = crawler.run(
         url="https://www.nbcnews.com/business",
+        js = js_code  
     )
     cprint("[LOG] 📦 [bold yellow]JavaScript Code (Load More button) result:[/bold yellow]")
     print_result(result)

docs/examples/rest_call.py

@@ -0,0 +1,64 @@
+
+import requests, base64, os
+
+data = {
+    "urls": ["https://www.nbcnews.com/business"],
+    "screenshot": True,
+}
+
+response = requests.post("https://crawl4ai.com/crawl", json=data) 
+result = response.json()['results'][0]
+print(result.keys())
+# dict_keys(['url', 'html', 'success', 'cleaned_html', 'media', 
+# 'links', 'screenshot', 'markdown', 'extracted_content', 
+# 'metadata', 'error_message'])
+with open("screenshot.png", "wb") as f:
+    f.write(base64.b64decode(result['screenshot']))
+    
+# Example of filtering the content using CSS selectors
+data = {
+    "urls": [
+        "https://www.nbcnews.com/business"
+    ],
+    "css_selector": "article",
+    "screenshot": True,
+}
+
+# Example of executing a JS script on the page before extracting the content
+data = {
+    "urls": [
+        "https://www.nbcnews.com/business"
+    ],
+    "screenshot": True,
+    'js' : ["""
+    const loadMoreButton = Array.from(document.querySelectorAll('button')).
+    find(button => button.textContent.includes('Load More'));
+    loadMoreButton && loadMoreButton.click();
+    """]
+}
+
+# Example of using a custom extraction strategy
+data = {
+    "urls": [
+        "https://www.nbcnews.com/business"
+    ],
+    "extraction_strategy": "CosineStrategy",
+    "extraction_strategy_args": {
+        "semantic_filter": "inflation rent prices"
+    },
+}
+
+# Example of using LLM to extract content
+data = {
+    "urls": [
+        "https://www.nbcnews.com/business"
+    ],
+    "extraction_strategy": "LLMExtractionStrategy",
+    "extraction_strategy_args": {
+        "provider": "groq/llama3-8b-8192",
+        "api_token": os.environ.get("GROQ_API_KEY"),
+        "instruction": """I am interested in only financial news, 
+        and translate them in French."""
+    },
+}
+

main.py

@@ -57,6 +57,7 @@ class CrawlRequest(BaseModel):
     chunking_strategy_args: Optional[dict] = {}
     css_selector: Optional[str] = None
     screenshot: Optional[bool] = False
+    user_agent: Optional[str] = None
     verbose: Optional[bool] = True
 
 
@@ -127,6 +128,7 @@ async def crawl_urls(crawl_request: CrawlRequest, request: Request):
                     crawl_request.bypass_cache,
                     crawl_request.css_selector,
                     crawl_request.screenshot,
+                    crawl_request.user_agent,
                     crawl_request.verbose
                 )
                 for url in crawl_request.urls

pages/index.html

@@ -25,7 +25,7 @@
         <header class="bg-zinc-950 text-lime-500 py-4 flex">
             
             <div class="mx-auto px-4">
-                <h1 class="text-2xl font-bold">🔥🕷️ Crawl4AI: Web Data for your Thoughts v0.2.2</h1>
+                <h1 class="text-2xl font-bold">🔥🕷️ Crawl4AI: Web Data for your Thoughts v0.2.4</h1>
             </div>
             <div class="mx-auto px-4 flex font-bold text-xl gap-2">
                 <span>📊 Total Website Processed</span>

pages/partial/how_to_guide.html

@@ -157,9 +157,8 @@ with open("screenshot.png", "wb") as f:
 const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More'));
 loadMoreButton && loadMoreButton.click();
 """]
-crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
+crawler = WebCrawler(verbos=crawler_strategy, always_by_pass_cache=True)
-crawler = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=True)
+result = crawler.run(url="https://www.nbcnews.com/business", js = js_code)</code></pre>
-result = crawler.run(url="https://www.nbcnews.com/business")</code></pre>
         <div class="">Remember that you can pass multiple JavaScript code snippets in the list. They all will be executed in the order they are passed.</div>
         </div>
 

pages/partial/try_it.html

@@ -121,7 +121,7 @@
                 </div>
                 <div class="flex gap-3">
                     <div class="flex items-center gap-2">
-                        <input type="checkbox" id="bypass-cache-checkbox" checked />
+                        <input type="checkbox" id="bypass-cache-checkbox" />
                         <label for="bypass-cache-checkbox" class="text-lime-500 font-bold">Bypass Cache</label>
                     </div>
                     <div class="flex items-center gap-2">

requirements.txt

@@ -18,3 +18,4 @@ chromedriver-autoinstaller
 torch
 onnxruntime
 tokenizers
+pillow

setup.py

@@ -1,8 +1,18 @@
 from setuptools import setup, find_packages
-import os
+import os, sys
+from pathlib import Path
 import subprocess
 from setuptools.command.install import install
 
+def get_home_folder():
+    home_folder = os.path.join(Path.home(), ".crawl4ai")
+    os.makedirs(home_folder, exist_ok=True)
+    os.makedirs(f"{home_folder}/cache", exist_ok=True)
+    os.makedirs(f"{home_folder}/models", exist_ok=True)
+    return home_folder 
+
+home_folder = get_home_folder()
+
 # Read the requirements from requirements.txt
 with open("requirements.txt") as f:
     requirements = f.read().splitlines()
@@ -26,7 +36,7 @@ class CustomInstallCommand(install):
 
 setup(
     name="Crawl4AI",
-    version="0.2.3",
+    version="0.2.4",
     description="🔥🕷️ Crawl4AI: Open-source LLM Friendly Web Crawler & Scrapper",
     long_description=open("README.md").read(),
     long_description_content_type="text/markdown",