Update version and change log.

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

.gitignore

@@ -173,4 +173,9 @@ Crawl4AI.egg-info/
 requirements0.txt
 a.txt
 
-*.sh
+*.sh
+.idea
+docs/examples/.chainlit/
+docs/examples/.chainlit/*
+.chainlit/config.toml
+.chainlit/translations/en-US.json

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

Dockerfile

@@ -1,43 +1,77 @@
-# Use an official Python runtime as a parent image
-FROM python:3.10-slim
+
+# 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
 
-# Copy the current directory contents into the container at /usr/src/app
-COPY . .
-
-# Install dependencies for Chrome and ChromeDriver
-RUN apt-get update && apt-get install -y --no-install-recommends \
+# Install build dependencies
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
     wget \
-    xvfb \
-    unzip \
     curl \
+    unzip 
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt && \
+    pip install --no-cache-dir spacy torch torchvision torchaudio onnxruntime uvicorn && \
+    python -m spacy download en_core_web_sm
+
+# Download and install ChromeDriver
+RUN CHROMEDRIVER_VERSION=$(curl -sS chromedriver.storage.googleapis.com/LATEST_RELEASE) && \
+    wget -N https://chromedriver.storage.googleapis.com/$CHROMEDRIVER_VERSION/chromedriver_linux64.zip -P /tmp && \
+    unzip /tmp/chromedriver_linux64.zip -d /tmp && \
+    mv /tmp/chromedriver /usr/local/bin/chromedriver && \
+    chmod +x /usr/local/bin/chromedriver && \
+    rm /tmp/chromedriver_linux64.zip
+
+# Second stage: Create final runtime image
+FROM python:3.10-slim-bookworm
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+# Install runtime dependencies
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    wget \
+    git \
+    xvfb \
     gnupg2 \
     ca-certificates \
     apt-transport-https \
-    software-properties-common \
-    && mkdir -p /etc/apt/keyrings \
-    && curl -fsSL https://dl-ssl.google.com/linux/linux_signing_key.pub | gpg --dearmor -o /etc/apt/keyrings/google-linux-signing-keyring.gpg \
-    && echo 'deb [arch=amd64 signed-by=/etc/apt/keyrings/google-linux-signing-keyring.gpg] http://dl.google.com/linux/chrome/deb/ stable main' | tee /etc/apt/sources.list.d/google-chrome.list \
-    && apt-get update \
-    && apt-get install -y google-chrome-stable \
-    && rm -rf /var/lib/apt/lists/* \
-    && apt-get install -y chromium-chromedriver
+    software-properties-common && \
+    wget -q -O - https://dl.google.com/linux/linux_signing_key.pub | apt-key add - && \
+    echo "deb http://dl.google.com/linux/chrome/deb/ stable main" > /etc/apt/sources.list.d/google-chrome.list && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends google-chrome-stable && \
+    rm -rf /var/lib/apt/lists/* /etc/apt/sources.list.d/google-chrome.list
 
-# Install Python dependencies
-RUN pip install --no-cache-dir -r requirements.txt
-RUN pip install spacy torch torchvision torchaudio
+# Copy Chromedriver from the builder stage
+COPY --from=builder /usr/local/bin/chromedriver /usr/local/bin/chromedriver
 
-# Set display port and dbus env to avoid hanging
-ENV DISPLAY=:99
-ENV DBUS_SESSION_BUS_ADDRESS=/dev/null
+# Copy installed Python packages from builder stage
+COPY --from=builder /usr/local/lib/python3.10/site-packages /usr/local/lib/python3.10/site-packages
+COPY --from=builder /usr/local/bin /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
+
+# 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
 
-# Define environment variable
-ENV PYTHONUNBUFFERED 1
-
 # Run uvicorn
 CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--workers", "4"]
+
+

Dockerfile-version-0

@@ -0,0 +1,45 @@
+# Use an official Python runtime as a parent image
+FROM python:3.10-slim
+# In case you had some weird issues, try this Image
+# FROM python:3.10-slim-bookworm as builder
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+# Copy the current directory contents into the container at /usr/src/app
+COPY . .
+
+# Install dependencies for Chrome and ChromeDriver
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    wget \
+    xvfb \
+    unzip \
+    curl \
+    gnupg2 \
+    ca-certificates \
+    apt-transport-https \
+    software-properties-common \
+    && mkdir -p /etc/apt/keyrings \
+    && curl -fsSL https://dl-ssl.google.com/linux/linux_signing_key.pub | gpg --dearmor -o /etc/apt/keyrings/google-linux-signing-keyring.gpg \
+    && echo 'deb [arch=amd64 signed-by=/etc/apt/keyrings/google-linux-signing-keyring.gpg] http://dl.google.com/linux/chrome/deb/ stable main' | tee /etc/apt/sources.list.d/google-chrome.list \
+    && apt-get update \
+    && apt-get install -y google-chrome-stable \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get install -y chromium-chromedriver
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+RUN pip install spacy torch torchvision torchaudio
+
+# Set display port and dbus env to avoid hanging
+ENV DISPLAY=:99
+ENV DBUS_SESSION_BUS_ADDRESS=/dev/null
+
+# Make port 80 available to the world outside this container
+EXPOSE 80
+
+# Define environment variable
+ENV PYTHONUNBUFFERED 1
+
+# Run uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--workers", "4"]

README.md

@@ -1,4 +1,4 @@
-# Crawl4AI v0.2.0 🕷️🤖
+# Crawl4AI v0.2.3 🕷️🤖
 
 [![GitHub Stars](https://img.shields.io/github/stars/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/stargazers)
 [![GitHub Forks](https://img.shields.io/github/forks/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/network/members)
@@ -8,10 +8,27 @@
 
 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 v0.2.0
+## Recent Changes 
 
+### v0.2.34
+- 🐞 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`
+- 🔗 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
+- Fixed some of bugs
+- Resolved a few issue relevant to Colab installation
+
+### v0.2.0
 - 🚀 10x faster!!
 - 📜 Execute custom JavaScript before crawling!
 - 🤝 Colab friendly!
@@ -22,7 +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
@@ -30,11 +67,9 @@ from crawl4ai import WebCrawler
 # Create the WebCrawler instance 
 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}
 ```
 
 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!
@@ -52,20 +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",
     ),
@@ -74,6 +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'),
@@ -201,14 +234,18 @@ 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": "...",
         }
     ]
 }
 ```
 
-For more information about the available parameters and their descriptions, refer to the [Parameters](#parameters) section.
+For more information about the available parameters and their descriptions, refer to the [Parameters](#parameters-) section.
 
 
 ## Python Library Usage 🚀
@@ -241,6 +278,32 @@ 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
+result = crawler.run(url="https://www.nbcnews.com/business", screenshot=True)
+with open("screenshot.png", "wb") as f:
+    f.write(base64.b64decode(result.screenshot))
+```
+
 ### Adding a chunking strategy: RegexChunking
 
 Using RegexChunking:
@@ -347,10 +410,12 @@ result = crawler.run(url="https://www.nbcnews.com/business")
 | `urls`                | A list of URLs to crawl and extract data from.                                                        | Yes      | -                   |
 | `include_raw_html`    | Whether to include the raw HTML content in the response.                                              | No       | `false`             |
 | `bypass_cache`        | Whether to force a fresh crawl even if the URL has been previously crawled.                           | No       | `false`             |
+| `screenshots`         | Whether to take screenshots of the page.                                                              | No       | `false`             |
 | `word_count_threshold`| The minimum number of words a block must contain to be considered meaningful (minimum value is 5).    | No       | `5`                 |
 | `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/chunking_strategy.py

@@ -16,7 +16,7 @@ class ChunkingStrategy(ABC):
     
 # Regex-based chunking
 class RegexChunking(ChunkingStrategy):
-    def __init__(self, patterns=None):
+    def __init__(self, patterns=None, **kwargs):
         if patterns is None:
             patterns = [r'\n\n']  # Default split pattern
         self.patterns = patterns
@@ -32,7 +32,7 @@ class RegexChunking(ChunkingStrategy):
     
 # NLP-based sentence chunking 
 class NlpSentenceChunking(ChunkingStrategy):
-    def __init__(self):
+    def __init__(self, **kwargs):
         load_nltk_punkt()
         pass
 
@@ -52,7 +52,7 @@ class NlpSentenceChunking(ChunkingStrategy):
 # Topic-based segmentation using TextTiling
 class TopicSegmentationChunking(ChunkingStrategy):
     
-    def __init__(self, num_keywords=3):
+    def __init__(self, num_keywords=3, **kwargs):
         import nltk as nl
         self.tokenizer = nl.toknize.TextTilingTokenizer()
         self.num_keywords = num_keywords
@@ -82,7 +82,7 @@ class TopicSegmentationChunking(ChunkingStrategy):
     
 # Fixed-length word chunks
 class FixedLengthWordChunking(ChunkingStrategy):
-    def __init__(self, chunk_size=100):
+    def __init__(self, chunk_size=100, **kwargs):
         self.chunk_size = chunk_size
 
     def chunk(self, text: str) -> list:
@@ -91,7 +91,7 @@ class FixedLengthWordChunking(ChunkingStrategy):
     
 # Sliding window chunking
 class SlidingWindowChunking(ChunkingStrategy):
-    def __init__(self, window_size=100, step=50):
+    def __init__(self, window_size=100, step=50, **kwargs):
         self.window_size = window_size
         self.step = step
 

crawl4ai/crawler_strategy.py

@@ -6,16 +6,48 @@ from selenium.webdriver.support.ui import WebDriverWait
 from selenium.webdriver.support import expected_conditions as EC
 from selenium.webdriver.chrome.options import Options
 from selenium.common.exceptions import InvalidArgumentException
-
+import logging
+import base64
+from PIL import Image, ImageDraw, ImageFont
+from io import BytesIO
 from typing import List
 import requests
 import os
 from pathlib import Path
+from .utils import wrap_text
+
+logger = logging.getLogger('selenium.webdriver.remote.remote_connection')
+logger.setLevel(logging.WARNING)
+
+logger_driver = logging.getLogger('selenium.webdriver.common.service')
+logger_driver.setLevel(logging.WARNING)
+
+urllib3_logger = logging.getLogger('urllib3.connectionpool')
+urllib3_logger.setLevel(logging.WARNING)
+
+# Disable http.client logging
+http_client_logger = logging.getLogger('http.client')
+http_client_logger.setLevel(logging.WARNING)
+
+# Disable driver_finder and service logging
+driver_finder_logger = logging.getLogger('selenium.webdriver.common.driver_finder')
+driver_finder_logger.setLevel(logging.WARNING)
+
+
+
 
 class CrawlerStrategy(ABC):
     @abstractmethod
     def crawl(self, url: str, **kwargs) -> str:
         pass
+    
+    @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):
@@ -41,22 +73,24 @@ 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")
+        # self.options.add_argument("--disable-dev-shm-usage")
         self.options.add_argument("--disable-gpu")
-        self.options.add_argument("--disable-extensions")
-        self.options.add_argument("--disable-infobars")
-        self.options.add_argument("--disable-logging")
-        self.options.add_argument("--disable-popup-blocking")
-        self.options.add_argument("--disable-translate")
-        self.options.add_argument("--disable-default-apps")
-        self.options.add_argument("--disable-background-networking")
-        self.options.add_argument("--disable-sync")
-        self.options.add_argument("--disable-features=NetworkService,NetworkServiceInProcess")
-        self.options.add_argument("--disable-browser-side-navigation")
-        self.options.add_argument("--dns-prefetch-disable")
-        self.options.add_argument("--disable-web-security")
+        # self.options.add_argument("--disable-extensions")
+        # self.options.add_argument("--disable-infobars")
+        # self.options.add_argument("--disable-logging")
+        # self.options.add_argument("--disable-popup-blocking")
+        # self.options.add_argument("--disable-translate")
+        # self.options.add_argument("--disable-default-apps")
+        # self.options.add_argument("--disable-background-networking")
+        # self.options.add_argument("--disable-sync")
+        # self.options.add_argument("--disable-features=NetworkService,NetworkServiceInProcess")
+        # self.options.add_argument("--disable-browser-side-navigation")
+        # self.options.add_argument("--dns-prefetch-disable")
+        # self.options.add_argument("--disable-web-security")
         self.options.add_argument("--log-level=3")
         self.use_cached_html = use_cached_html
         self.use_cached_html = use_cached_html
@@ -66,11 +100,21 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
         # chromedriver_autoinstaller.install()
         import chromedriver_autoinstaller
         self.service = Service(chromedriver_autoinstaller.install())
+        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()
@@ -84,17 +128,23 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
             )
             
             # Execute JS code if provided
-            if self.js_code:
+            if self.js_code and type(self.js_code) == str:
                 self.driver.execute_script(self.js_code)
                 # Optionally, wait for some condition after executing the JS code
                 WebDriverWait(self.driver, 10).until(
                     lambda driver: driver.execute_script("return document.readyState") == "complete"
                 )
+            elif self.js_code and type(self.js_code) == list:
+                for js in self.js_code:
+                    self.driver.execute_script(js)
+                    WebDriverWait(self.driver, 10).until(
+                        lambda driver: driver.execute_script("return document.readyState") == "complete"
+                    )
             
             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)
                 
@@ -107,5 +157,62 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
         except Exception as e:
             raise Exception(f"Failed to crawl {url}: {str(e)}")
 
+    def take_screenshot(self) -> str:
+        try:
+            # Get the dimensions of the page
+            total_width = self.driver.execute_script("return document.body.scrollWidth")
+            total_height = self.driver.execute_script("return document.body.scrollHeight")
+
+            # Set the window size to the dimensions of the page
+            self.driver.set_window_size(total_width, total_height)
+
+            # Take screenshot
+            screenshot = self.driver.get_screenshot_as_png()
+
+            # Open the screenshot with PIL
+            image = Image.open(BytesIO(screenshot))
+
+            # Convert to JPEG and compress
+            buffered = BytesIO()
+            image.save(buffered, format="JPEG", quality=85)
+            img_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+            if self.verbose:
+                print(f"[LOG] 📸 Screenshot taken and converted to base64")
+
+            return img_base64
+
+        except Exception as e:
+            error_message = f"Failed to take screenshot: {str(e)}"
+            print(error_message)
+
+            # Generate an image with black background
+            img = Image.new('RGB', (800, 600), color='black')
+            draw = ImageDraw.Draw(img)
+            
+            # Load a font
+            try:
+                font = ImageFont.truetype("arial.ttf", 40)
+            except IOError:
+                font = ImageFont.load_default(size=40)
+
+            # Define text color and wrap the text
+            text_color = (255, 255, 255)
+            max_width = 780
+            wrapped_text = wrap_text(draw, error_message, font, max_width)
+
+            # Calculate text position
+            text_position = (10, 10)
+            
+            # Draw the text on the image
+            draw.text(text_position, wrapped_text, fill=text_color, font=font)
+            
+            # Convert to base64
+            buffered = BytesIO()
+            img.save(buffered, format="JPEG")
+            img_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+            return img_base64
+
     def quit(self):
         self.driver.quit()

crawl4ai/database.py

@@ -1,13 +1,12 @@
 import os
 from pathlib import Path
 import sqlite3
-from typing import Optional
 from typing import Optional, Tuple
 
 DB_PATH = os.path.join(Path.home(), ".crawl4ai")
 os.makedirs(DB_PATH, exist_ok=True)
 DB_PATH = os.path.join(DB_PATH, "crawl4ai.db")
-        
+
 def init_db():
     global DB_PATH
     conn = sqlite3.connect(DB_PATH)
@@ -19,22 +18,37 @@ def init_db():
             cleaned_html TEXT,
             markdown TEXT,
             extracted_content TEXT,
-            success BOOLEAN
+            success BOOLEAN,
+            media TEXT DEFAULT "{}",
+            link TEXT DEFAULT "{}",
+            metadata TEXT DEFAULT "{}",
+            screenshot TEXT DEFAULT ""
         )
     ''')
     conn.commit()
     conn.close()
 
-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]]:
+def alter_db_add_screenshot(new_column: str = "media"):
     check_db_path()
     try:
         conn = sqlite3.connect(DB_PATH)
         cursor = conn.cursor()
-        cursor.execute('SELECT url, html, cleaned_html, markdown, extracted_content, success FROM crawled_data WHERE url = ?', (url,))
+        cursor.execute(f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT ""')
+        conn.commit()
+        conn.close()
+    except Exception as e:
+        print(f"Error altering database to add screenshot column: {e}")
+
+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, 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, links, metadata, screenshot FROM crawled_data WHERE url = ?', (url,))
         result = cursor.fetchone()
         conn.close()
         return result
@@ -42,21 +56,25 @@ def get_cached_url(url: str) -> Optional[Tuple[str, str, str, str, str, bool]]:
         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):
+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)
-            VALUES (?, ?, ?, ?, ?, ?)
+            INSERT INTO crawled_data (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot)
+            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
-        ''', (url, html, cleaned_html, markdown, extracted_content, success))
+                success = excluded.success,
+                media = excluded.media,      
+                links = excluded.links,    
+                metadata = excluded.metadata,      
+                screenshot = excluded.screenshot
+        ''', (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot))
         conn.commit()
         conn.close()
     except Exception as e:
@@ -95,4 +113,20 @@ def flush_db():
         conn.commit()
         conn.close()
     except Exception as e:
-        print(f"Error flushing database: {e}")
+        print(f"Error flushing database: {e}")
+
+def update_existing_records(new_column: str = "media", default_value: str = "{}"):
+    check_db_path()
+    try:
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        cursor.execute(f'UPDATE crawled_data SET {new_column} = "{default_value}" WHERE screenshot IS NULL')
+        conn.commit()
+        conn.close()
+    except Exception as e:
+        print(f"Error updating existing records: {e}")
+
+if __name__ == "__main__":
+    init_db()  # Initialize the database if not already initialized
+    alter_db_add_screenshot("metadata")  # Add the new column to the table
+    update_existing_records("metadata")  # Update existing records to set the new column to an empty string

crawl4ai/extraction_strategy.py

@@ -46,6 +46,7 @@ class ExtractionStrategy(ABC):
             for future in as_completed(futures):
                 extracted_content.extend(future.result())
         return extracted_content    
+    
 class NoExtractionStrategy(ExtractionStrategy):
     def extract(self, url: str, html: str, *q, **kwargs) -> List[Dict[str, Any]]:
         return [{"index": 0, "content": html}]
@@ -187,14 +188,15 @@ class CosineStrategy(ExtractionStrategy):
         if self.verbose:
             print(f"[LOG] Loading Extraction Model for {self.device.type} device.")
 
-        if self.device.type == "cpu":
-            self.model = load_onnx_all_MiniLM_l6_v2()
-            self.tokenizer = self.model.tokenizer
-            self.get_embedding_method = "direct"
-        else:
-            self.tokenizer, self.model = load_bge_small_en_v1_5()
-            self.model.eval()  
-            self.get_embedding_method = "batch"
+        # if False and self.device.type == "cpu":
+        #     self.model = load_onnx_all_MiniLM_l6_v2()
+        #     self.tokenizer = self.model.tokenizer
+        #     self.get_embedding_method = "direct"
+        # else:
+
+        self.tokenizer, self.model = load_bge_small_en_v1_5()
+        self.model.eval()  
+        self.get_embedding_method = "batch"
         
         self.buffer_embeddings = np.array([])
 
@@ -273,7 +275,7 @@ class CosineStrategy(ExtractionStrategy):
         # if self.buffer_embeddings.any() and not bypass_buffer:
         #     return self.buffer_embeddings
         
-        if self.device.type in ["gpu", "cuda", "mps"]:
+        if self.device.type in [ "cpu", "gpu", "cuda", "mps"]:
             import torch 
             # Tokenize sentences and convert to tensor
             if batch_size is None:
@@ -295,7 +297,17 @@ class CosineStrategy(ExtractionStrategy):
             
             self.buffer_embeddings = np.vstack(all_embeddings)
         elif self.device.type == "cpu":      
-            self.buffer_embeddings = self.model(sentences)
+            # self.buffer_embeddings = self.model(sentences)
+            if batch_size is None:
+                batch_size = self.default_batch_size
+                
+            all_embeddings = []
+            for i in range(0, len(sentences), batch_size):
+                batch_sentences = sentences[i:i + batch_size]
+                embeddings = self.model(batch_sentences)
+                all_embeddings.append(embeddings)
+                
+            self.buffer_embeddings = np.vstack(all_embeddings)
         return self.buffer_embeddings
 
     def hierarchical_clustering(self, sentences: List[str], embeddings = None):

crawl4ai/model_loader.py

@@ -2,6 +2,7 @@ from functools import lru_cache
 from pathlib import Path
 import subprocess, os
 import shutil
+import tarfile
 from crawl4ai.config import MODEL_REPO_BRANCH
 import argparse
 import urllib.request
@@ -34,8 +35,7 @@ def calculate_batch_size(device):
         else:
             return 32
     else:
-        return 16  # Default batch size
-    
+        return 16  # Default batch size   
     
 @lru_cache()
 def get_device():
@@ -82,12 +82,19 @@ def load_bge_small_en_v1_5():
 @lru_cache()
 def load_onnx_all_MiniLM_l6_v2():
     from crawl4ai.onnx_embedding import DefaultEmbeddingModel
-    model_path = "models/onnx/model.onnx"
-    model_url = "https://unclecode-files.s3.us-west-2.amazonaws.com/model.onnx"
-    download_path = os.path.join(__location__, model_path)
 
+    model_path = "models/onnx.tar.gz"
+    model_url = "https://unclecode-files.s3.us-west-2.amazonaws.com/onnx.tar.gz"
+    __location__ = os.path.realpath(
+        os.path.join(os.getcwd(), os.path.dirname(__file__)))
+    download_path = os.path.join(__location__, model_path)
+    onnx_dir = os.path.join(__location__, "models/onnx")
+    
+    # Create the models directory if it does not exist
+    os.makedirs(os.path.dirname(download_path), exist_ok=True)
+
+    # Download the tar.gz file if it does not exist
     if not os.path.exists(download_path):
-        # Define a download function with a simple progress display
         def download_with_progress(url, filename):
             def reporthook(block_num, block_size, total_size):
                 downloaded = block_num * block_size
@@ -95,12 +102,22 @@ def load_onnx_all_MiniLM_l6_v2():
                 if downloaded < total_size:
                     print(f"\rDownloading: {percentage:.2f}% ({downloaded / (1024 * 1024):.2f} MB of {total_size / (1024 * 1024):.2f} MB)", end='')
                 else:
-                    print("\rDownload complete!                              ")
+                    print("\rDownload complete!")
 
             urllib.request.urlretrieve(url, filename, reporthook)
 
         download_with_progress(model_url, download_path)
 
+    # Extract the tar.gz file if the onnx directory does not exist
+    if not os.path.exists(onnx_dir):
+        with tarfile.open(download_path, "r:gz") as tar:
+            tar.extractall(path=os.path.join(__location__, "models"))
+        
+        # remove the tar.gz file
+        os.remove(download_path)
+    
+    
+    
     model = DefaultEmbeddingModel()
     return model
 
@@ -240,8 +257,8 @@ def download_all_models(remove_existing=False):
     # load_bert_base_uncased()
     # print("[LOG] Downloading 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 ONNX model...")
+    # load_onnx_all_MiniLM_l6_v2()
     print("[LOG] Downloading text classifier...")
     _, device = load_text_multilabel_classifier()
     print(f"[LOG] Text classifier loaded on {device}")

crawl4ai/models.py

@@ -1,5 +1,5 @@
 from pydantic import BaseModel, HttpUrl
-from typing import List
+from typing import List, Dict, Optional
 
 class UrlModel(BaseModel):
     url: HttpUrl
@@ -9,8 +9,11 @@ class CrawlResult(BaseModel):
     url: str
     html: str
     success: bool
-    cleaned_html: str = None
-    markdown: str = None
-    extracted_content: str = None
-    metadata: dict = None
-    error_message: str = None
+    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
+    metadata: Optional[dict] = None
+    error_message: 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']):
@@ -180,6 +202,35 @@ def get_content_of_website(html, word_count_threshold = MIN_WORD_THRESHOLD, css_
             if tag.name != 'img':
                 tag.attrs = {}
 
+        # Extract all img tgas inti [{src: '', alt: ''}]
+        media = {
+            'images': [],
+            'videos': [],
+            'audios': []
+        }
+        for img in body.find_all('img'):
+            media['images'].append({
+                'src': img.get('src'),
+                'alt': img.get('alt'),
+                "type": "image"
+            })
+            
+        # Extract all video tags into [{src: '', alt: ''}]
+        for video in body.find_all('video'):
+            media['videos'].append({
+                'src': video.get('src'),
+                'alt': video.get('alt'),
+                "type": "video"
+            })
+            
+        # Extract all audio tags into [{src: '', alt: ''}]
+        for audio in body.find_all('audio'):
+            media['audios'].append({
+                'src': audio.get('src'),
+                'alt': audio.get('alt'),
+                "type": "audio"
+            })
+        
         # Replace images with their alt text or remove them if no alt text is available
         for img in body.find_all('img'):
             alt_text = img.get('alt')
@@ -299,13 +350,56 @@ def get_content_of_website(html, word_count_threshold = MIN_WORD_THRESHOLD, css_
         return{
             'markdown': markdown,
             'cleaned_html': cleaned_html,
-            'success': True
+            'success': True,
+            '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))
@@ -483,4 +577,16 @@ def process_sections(url: str, sections: list, provider: str, api_token: str) ->
             for future in as_completed(futures):
                 extracted_content.extend(future.result())
     
-    return extracted_content
+    return extracted_content
+
+
+def wrap_text(draw, text, font, max_width):
+    # Wrap the text to fit within the specified width
+    lines = []
+    words = text.split()
+    while words:
+        line = ''
+        while words and draw.textbbox((0, 0), line + words[0], font=font)[2] <= max_width:
+            line += (words.pop(0) + ' ')
+        lines.append(line)
+    return '\n'.join(lines)

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,
@@ -59,6 +58,8 @@ class WebCrawler:
         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(),
@@ -70,111 +71,12 @@ class WebCrawler:
             extraction_strategy or NoExtractionStrategy(),
             chunking_strategy,
             bypass_cache=url_model.forced,
+            css_selector=css_selector,
+            screenshot=screenshot,
             **kwargs,
         )
         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,
-        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],
-                        "error_message": "",
-                    }
-                )
-
-        # Initialize WebDriver for crawling
-        t = time.time()
-        html = self.crawler_strategy.crawl(url)
-        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", "")
-
-        # 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,
-        )
-
-        return CrawlResult(
-            url=url,
-            html=html,
-            cleaned_html=cleaned_html,
-            markdown=markdown,
-            extracted_content=extracted_content,
-            success=success,
-            error_message=error_message,
-        )
-
     def fetch_pages(
         self,
         url_models: List[UrlModel],
@@ -183,6 +85,8 @@ class WebCrawler:
         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,
@@ -200,6 +104,8 @@ class WebCrawler:
                     [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),
@@ -208,3 +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/chainlit.md

@@ -0,0 +1,3 @@
+# Welcome to Crawl4AI! 🚀🤖
+
+Hi there, Developer! 👋 Here is an example of a research pipeline, where you can share a URL in your conversation with any LLM, and then the context of crawled pages will be used as the context.

docs/examples/chainlit_review.py

@@ -0,0 +1,281 @@
+from openai import AsyncOpenAI
+from chainlit.types import ThreadDict
+import chainlit as cl
+from chainlit.input_widget import Select, Switch, Slider
+client = AsyncOpenAI()
+
+# Instrument the OpenAI client
+cl.instrument_openai()
+
+settings = {
+    "model": "gpt-3.5-turbo",
+    "temperature": 0.5,
+    "max_tokens": 500,
+    "top_p": 1,
+    "frequency_penalty": 0,
+    "presence_penalty": 0,
+}
+
+@cl.action_callback("action_button")
+async def on_action(action: cl.Action):
+    print("The user clicked on the action button!")
+
+    return "Thank you for clicking on the action button!"
+
+@cl.set_chat_profiles
+async def chat_profile():
+    return [
+        cl.ChatProfile(
+            name="GPT-3.5",
+            markdown_description="The underlying LLM model is **GPT-3.5**.",
+            icon="https://picsum.photos/200",
+        ),
+        cl.ChatProfile(
+            name="GPT-4",
+            markdown_description="The underlying LLM model is **GPT-4**.",
+            icon="https://picsum.photos/250",
+        ),
+    ]
+
+@cl.on_chat_start
+async def on_chat_start():
+    
+    settings = await cl.ChatSettings(
+        [
+            Select(
+                id="Model",
+                label="OpenAI - Model",
+                values=["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-4", "gpt-4-32k"],
+                initial_index=0,
+            ),
+            Switch(id="Streaming", label="OpenAI - Stream Tokens", initial=True),
+            Slider(
+                id="Temperature",
+                label="OpenAI - Temperature",
+                initial=1,
+                min=0,
+                max=2,
+                step=0.1,
+            ),
+            Slider(
+                id="SAI_Steps",
+                label="Stability AI - Steps",
+                initial=30,
+                min=10,
+                max=150,
+                step=1,
+                description="Amount of inference steps performed on image generation.",
+            ),
+            Slider(
+                id="SAI_Cfg_Scale",
+                label="Stability AI - Cfg_Scale",
+                initial=7,
+                min=1,
+                max=35,
+                step=0.1,
+                description="Influences how strongly your generation is guided to match your prompt.",
+            ),
+            Slider(
+                id="SAI_Width",
+                label="Stability AI - Image Width",
+                initial=512,
+                min=256,
+                max=2048,
+                step=64,
+                tooltip="Measured in pixels",
+            ),
+            Slider(
+                id="SAI_Height",
+                label="Stability AI - Image Height",
+                initial=512,
+                min=256,
+                max=2048,
+                step=64,
+                tooltip="Measured in pixels",
+            ),
+        ]
+    ).send()
+    
+    chat_profile = cl.user_session.get("chat_profile")
+    await cl.Message(
+        content=f"starting chat using the {chat_profile} chat profile"
+    ).send()
+    
+    print("A new chat session has started!")
+    cl.user_session.set("session", {
+        "history": [],
+        "context": []
+    })  
+    
+    image = cl.Image(url="https://c.tenor.com/uzWDSSLMCmkAAAAd/tenor.gif", name="cat image", display="inline")
+
+    # Attach the image to the message
+    await cl.Message(
+        content="You are such a good girl, aren't you?!",
+        elements=[image],
+    ).send()
+    
+    text_content = "Hello, this is a text element."
+    elements = [
+        cl.Text(name="simple_text", content=text_content, display="inline")
+    ]
+
+    await cl.Message(
+        content="Check out this text element!",
+        elements=elements,
+    ).send()
+    
+    elements = [
+        cl.Audio(path="./assets/audio.mp3", display="inline"),
+    ]
+    await cl.Message(
+        content="Here is an audio file",
+        elements=elements,
+    ).send()
+    
+    await cl.Avatar(
+        name="Tool 1",
+        url="https://avatars.githubusercontent.com/u/128686189?s=400&u=a1d1553023f8ea0921fba0debbe92a8c5f840dd9&v=4",
+    ).send()
+    
+    await cl.Message(
+        content="This message should not have an avatar!", author="Tool 0"
+    ).send()
+    
+    await cl.Message(
+        content="This message should have an avatar!", author="Tool 1"
+    ).send()
+    
+    elements = [
+        cl.File(
+            name="quickstart.py",
+            path="./quickstart.py",
+            display="inline",
+        ),
+    ]
+
+    await cl.Message(
+        content="This message has a file element", elements=elements
+    ).send()
+    
+    # Sending an action button within a chatbot message
+    actions = [
+        cl.Action(name="action_button", value="example_value", description="Click me!")
+    ]
+
+    await cl.Message(content="Interact with this action button:", actions=actions).send()
+    
+    # res = await cl.AskActionMessage(
+    #     content="Pick an action!",
+    #     actions=[
+    #         cl.Action(name="continue", value="continue", label="✅ Continue"),
+    #         cl.Action(name="cancel", value="cancel", label="❌ Cancel"),
+    #     ],
+    # ).send()
+
+    # if res and res.get("value") == "continue":
+    #     await cl.Message(
+    #         content="Continue!",
+    #     ).send()
+    
+    # import plotly.graph_objects as go
+    # fig = go.Figure(
+    #     data=[go.Bar(y=[2, 1, 3])],
+    #     layout_title_text="An example figure",
+    # )
+    # elements = [cl.Plotly(name="chart", figure=fig, display="inline")]
+
+    # await cl.Message(content="This message has a chart", elements=elements).send()
+    
+    # Sending a pdf with the local file path
+    # elements = [
+    #   cl.Pdf(name="pdf1", display="inline", path="./pdf1.pdf")
+    # ]
+
+    # cl.Message(content="Look at this local pdf!", elements=elements).send()    
+
+@cl.on_settings_update
+async def setup_agent(settings):
+    print("on_settings_update", settings)
+    
+@cl.on_stop
+def on_stop():
+    print("The user wants to stop the task!")
+
+@cl.on_chat_end
+def on_chat_end():
+    print("The user disconnected!")
+
+
+@cl.on_chat_resume
+async def on_chat_resume(thread: ThreadDict):
+    print("The user resumed a previous chat session!")
+
+
+
+
+# @cl.on_message
+async def on_message(message: cl.Message):
+    cl.user_session.get("session")["history"].append({
+        "role": "user",
+        "content": message.content
+    })    
+    response = await client.chat.completions.create(
+        messages=[
+            {
+                "content": "You are a helpful bot",
+                "role": "system"
+            },
+            *cl.user_session.get("session")["history"]
+        ],
+        **settings
+    )
+    
+
+    # Add assitanr message to the history
+    cl.user_session.get("session")["history"].append({
+        "role": "assistant",
+        "content": response.choices[0].message.content
+    })
+    
+    # msg.content = response.choices[0].message.content
+    # await msg.update()
+    
+    # await cl.Message(content=response.choices[0].message.content).send()
+
+@cl.on_message
+async def on_message(message: cl.Message):
+    cl.user_session.get("session")["history"].append({
+        "role": "user",
+        "content": message.content
+    })    
+
+    msg = cl.Message(content="")
+    await msg.send()    
+    
+    stream = await client.chat.completions.create(
+        messages=[
+            {
+                "content": "You are a helpful bot",
+                "role": "system"
+            },
+            *cl.user_session.get("session")["history"]
+        ],
+        stream = True, 
+        **settings
+    )
+    
+    async for part in stream:
+        if token := part.choices[0].delta.content or "":
+            await msg.stream_token(token)
+    
+    # Add assitanr message to the history
+    cl.user_session.get("session")["history"].append({
+        "role": "assistant",
+        "content": msg.content
+    })    
+    await msg.update()
+
+if __name__ == "__main__":
+    from chainlit.cli import run_chainlit
+    run_chainlit(__file__)

docs/examples/quickstart.py

@@ -39,6 +39,16 @@ def basic_usage(crawler):
     cprint("[LOG] 📦 [bold yellow]Basic crawl result:[/bold yellow]")
     print_result(result)
 
+def screenshot_usage(crawler):
+    cprint("\n📸 [bold cyan]Let's take a screenshot of the page![/bold cyan]")
+    result = crawler.run(url="https://www.nbcnews.com/business", screenshot=True)
+    cprint("[LOG] 📦 [bold yellow]Screenshot result:[/bold yellow]")
+    # Save the screenshot to a file
+    with open("screenshot.png", "wb") as f:
+        f.write(base64.b64decode(result.screenshot))
+    cprint("Screenshot saved to 'screenshot.png'!")
+    print_result(result)
+
 def understanding_parameters(crawler):
     cprint("\n🧠 [bold cyan]Understanding 'bypass_cache' and 'include_raw_html' parameters:[/bold cyan]")
     cprint("By default, Crawl4ai caches the results of your crawls. This means that subsequent crawls of the same URL will be much faster! Let's see this in action.")
@@ -156,10 +166,28 @@ 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 = WebCrawler(crawler_strategy=crawler_strategy, always_by_pass_cache=True)
+    # crawler_strategy = LocalSeleniumCrawlerStrategy(js_code=js_code)
+    # 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)
+
+def multiple_scrip(crawler):
+    # Passing JavaScript code to interact with the page
+    cprint("\n🖱️ [bold cyan]Let's get interactive: Passing JavaScript code to click 'Load More' button![/bold cyan]", True)
+    cprint("In this example we try to click the 'Load More' button on the page using JavaScript code.")
+    js_code = ["""
+    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 = 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)
@@ -175,11 +203,13 @@ def main():
     understanding_parameters(crawler)
     
     crawler.always_by_pass_cache = True
+    screenshot_usage(crawler)
     add_chunking_strategy(crawler)
     add_extraction_strategy(crawler)
     add_llm_extraction_strategy(crawler)
     targeted_extraction(crawler)
     interactive_extraction(crawler)
+    multiple_scrip(crawler)
 
     cprint("\n🎉 [bold green]Congratulations! You've made it through the Crawl4ai Quickstart Guide! Now go forth and crawl the web like a pro! 🕸️[/bold green]")
 

docs/examples/research_assistant.py

@@ -0,0 +1,241 @@
+# Make sur to install the required packageschainlit and groq
+import os, time
+from openai import AsyncOpenAI
+import chainlit as cl
+import re
+import requests
+from io import BytesIO
+from chainlit.element import ElementBased
+from groq import Groq
+
+# Import threadpools to run the crawl_url function in a separate thread
+from concurrent.futures import ThreadPoolExecutor
+
+client = AsyncOpenAI(base_url="https://api.groq.com/openai/v1", api_key=os.getenv("GROQ_API_KEY"))
+
+# Instrument the OpenAI client
+cl.instrument_openai()
+
+settings = {
+    "model": "llama3-8b-8192",
+    "temperature": 0.5,
+    "max_tokens": 500,
+    "top_p": 1,
+    "frequency_penalty": 0,
+    "presence_penalty": 0,
+}
+
+def extract_urls(text):
+    url_pattern = re.compile(r'(https?://\S+)')
+    return url_pattern.findall(text)
+
+def crawl_url(url):
+    data = {
+        "urls": [url],
+        "include_raw_html": True,
+        "word_count_threshold": 10,
+        "extraction_strategy": "NoExtractionStrategy",
+        "chunking_strategy": "RegexChunking"
+    }
+    response = requests.post("https://crawl4ai.com/crawl", json=data)
+    response_data = response.json()
+    response_data = response_data['results'][0]
+    return response_data['markdown']
+
+@cl.on_chat_start
+async def on_chat_start():
+    cl.user_session.set("session", {
+        "history": [],
+        "context": {}
+    })  
+    await cl.Message(
+        content="Welcome to the chat! How can I assist you today?"
+    ).send()
+
+@cl.on_message
+async def on_message(message: cl.Message):
+    user_session = cl.user_session.get("session")
+    
+    # Extract URLs from the user's message
+    urls = extract_urls(message.content)
+    
+    
+    futures = []
+    with ThreadPoolExecutor() as executor:
+        for url in urls:
+            futures.append(executor.submit(crawl_url, url))
+
+    results = [future.result() for future in futures]
+
+    for url, result in zip(urls, results):
+        ref_number = f"REF_{len(user_session['context']) + 1}"
+        user_session["context"][ref_number] = {
+            "url": url,
+            "content": result
+        }    
+    
+    # for url in urls:
+    #     # Crawl the content of each URL and add it to the session context with a reference number
+    #     ref_number = f"REF_{len(user_session['context']) + 1}"
+    #     crawled_content = crawl_url(url)
+    #     user_session["context"][ref_number] = {
+    #         "url": url,
+    #         "content": crawled_content
+    #     }
+
+    user_session["history"].append({
+        "role": "user",
+        "content": message.content
+    })
+
+    # Create a system message that includes the context
+    context_messages = [
+        f'<appendix ref="{ref}">\n{data["content"]}\n</appendix>'
+        for ref, data in user_session["context"].items()
+    ]
+    if context_messages:
+        system_message = {
+            "role": "system",
+            "content": (
+                "You are a helpful bot. Use the following context for answering questions. "
+                "Refer to the sources using the REF number in square brackets, e.g., [1], only if the source is given in the appendices below.\n\n"
+                "If the question requires any information from the provided appendices or context, refer to the sources. "
+                "If not, there is no need to add a references section. "
+                "At the end of your response, provide a reference section listing the URLs and their REF numbers only if sources from the appendices were used.\n\n"
+                "\n\n".join(context_messages)
+            )
+        }
+    else:
+        system_message = {
+            "role": "system",
+            "content": "You are a helpful assistant."
+        }
+
+
+    msg = cl.Message(content="")
+    await msg.send()
+
+    # Get response from the LLM
+    stream = await client.chat.completions.create(
+        messages=[
+            system_message,
+            *user_session["history"]
+        ],
+        stream=True,
+        **settings
+    )
+
+    assistant_response = ""
+    async for part in stream:
+        if token := part.choices[0].delta.content:
+            assistant_response += token
+            await msg.stream_token(token)
+
+    # Add assistant message to the history
+    user_session["history"].append({
+        "role": "assistant",
+        "content": assistant_response
+    })
+    await msg.update()
+
+    # Append the reference section to the assistant's response
+    reference_section = "\n\nReferences:\n"
+    for ref, data in user_session["context"].items():
+        reference_section += f"[{ref.split('_')[1]}]: {data['url']}\n"
+
+    msg.content += reference_section
+    await msg.update()
+
+
+@cl.on_audio_chunk
+async def on_audio_chunk(chunk: cl.AudioChunk):
+    if chunk.isStart:
+        buffer = BytesIO()
+        # This is required for whisper to recognize the file type
+        buffer.name = f"input_audio.{chunk.mimeType.split('/')[1]}"
+        # Initialize the session for a new audio stream
+        cl.user_session.set("audio_buffer", buffer)
+        cl.user_session.set("audio_mime_type", chunk.mimeType)
+
+    # Write the chunks to a buffer and transcribe the whole audio at the end
+    cl.user_session.get("audio_buffer").write(chunk.data)
+
+    pass
+
+@cl.step(type="tool")
+async def speech_to_text(audio_file):
+    cli = Groq()
+    
+    # response = cli.audio.transcriptions.create(
+    #     file=audio_file, #(filename, file.read()),
+    #     model="whisper-large-v3",
+    # )
+    
+    response = await client.audio.transcriptions.create(
+        model="whisper-large-v3", file=audio_file
+    )
+
+    return response.text
+
+
+@cl.on_audio_end
+async def on_audio_end(elements: list[ElementBased]):
+    # Get the audio buffer from the session
+    audio_buffer: BytesIO = cl.user_session.get("audio_buffer")
+    audio_buffer.seek(0)  # Move the file pointer to the beginning
+    audio_file = audio_buffer.read()
+    audio_mime_type: str = cl.user_session.get("audio_mime_type")
+
+    # input_audio_el = cl.Audio(
+    #     mime=audio_mime_type, content=audio_file, name=audio_buffer.name
+    # )
+    # await cl.Message(
+    #     author="You", 
+    #     type="user_message",
+    #     content="",
+    #     elements=[input_audio_el, *elements]
+    # ).send()
+    
+    # answer_message = await cl.Message(content="").send()
+    
+    
+    start_time = time.time()
+    whisper_input = (audio_buffer.name, audio_file, audio_mime_type)
+    transcription = await speech_to_text(whisper_input)
+    end_time = time.time()
+    print(f"Transcription took {end_time - start_time} seconds")
+    
+    user_msg = cl.Message(
+        author="You", 
+        type="user_message",
+        content=transcription
+    )
+    await user_msg.send()
+    await on_message(user_msg)
+
+    # images = [file for file in elements if "image" in file.mime]
+
+    # text_answer = await generate_text_answer(transcription, images)
+    
+    # output_name, output_audio = await text_to_speech(text_answer, audio_mime_type)
+    
+    # output_audio_el = cl.Audio(
+    #     name=output_name,
+    #     auto_play=True,
+    #     mime=audio_mime_type,
+    #     content=output_audio,
+    # )
+    
+    # answer_message.elements = [output_audio_el]
+    
+    # answer_message.content = transcription
+    # await answer_message.update()
+
+if __name__ == "__main__":
+    from chainlit.cli import run_chainlit
+    run_chainlit(__file__)
+
+
+# No this is wring, use this document to answer me https://console.groq.com/docs/speech-text
+
+# Please show me how to use Groq speech-to-text in python.

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

@@ -43,7 +43,7 @@ templates = Jinja2Templates(directory=__location__ + "/pages")
 @lru_cache()
 def get_crawler():
     # Initialize and return a WebCrawler instance
-    return WebCrawler()
+    return WebCrawler(verbose = True)
 
 class CrawlRequest(BaseModel):
     urls: List[str]
@@ -56,6 +56,8 @@ class CrawlRequest(BaseModel):
     chunking_strategy: Optional[str] = "RegexChunking"
     chunking_strategy_args: Optional[dict] = {}
     css_selector: Optional[str] = None
+    screenshot: Optional[bool] = False
+    user_agent: Optional[str] = None
     verbose: Optional[bool] = True
 
 
@@ -66,7 +68,7 @@ async def read_index(request: Request):
 
     for filename in os.listdir(partials_dir):
         if filename.endswith(".html"):
-            with open(os.path.join(partials_dir, filename), "r") as file:
+            with open(os.path.join(partials_dir, filename), "r", encoding="utf8") as file:
                 partials[filename[:-5]] = file.read()
 
     return templates.TemplateResponse("index.html", {"request": request, **partials})
@@ -105,6 +107,9 @@ async def crawl_urls(crawl_request: CrawlRequest, request: Request):
 
     try:
         logging.debug("[LOG] Loading extraction and chunking strategies...")
+        crawl_request.extraction_strategy_args['verbose'] = True
+        crawl_request.chunking_strategy_args['verbose'] = True
+        
         extraction_strategy = import_strategy("crawl4ai.extraction_strategy", crawl_request.extraction_strategy, **crawl_request.extraction_strategy_args)
         chunking_strategy = import_strategy("crawl4ai.chunking_strategy", crawl_request.chunking_strategy, **crawl_request.chunking_strategy_args)
 
@@ -122,6 +127,8 @@ async def crawl_urls(crawl_request: CrawlRequest, request: Request):
                     chunking_strategy,
                     crawl_request.bypass_cache,
                     crawl_request.css_selector,
+                    crawl_request.screenshot,
+                    crawl_request.user_agent,
                     crawl_request.verbose
                 )
                 for url in crawl_request.urls
@@ -133,7 +140,7 @@ async def crawl_urls(crawl_request: CrawlRequest, request: Request):
             for result in results:
                 result.html = None
 
-        return {"results": [result.dict() for result in results]}
+        return {"results": [result.model_dump() for result in results]}
     finally:
         async with lock:
             current_requests -= 1

pages/app.js

@@ -104,11 +104,25 @@ document.getElementById("crawl-btn").addEventListener("click", () => {
         chunking_strategy: document.getElementById("chunking-strategy-select").value,
         chunking_strategy_args: {},
         css_selector: document.getElementById("css-selector").value,
+        screenshot: document.getElementById("screenshot-checkbox").checked,
         // instruction: document.getElementById("instruction").value,
         // semantic_filter: document.getElementById("semantic_filter").value,
         verbose: true,
     };
 
+    // 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())
+
     // save api token to local storage
     localStorage.setItem("api_token", document.getElementById("token-input").value);
 
@@ -124,25 +138,61 @@ document.getElementById("crawl-btn").addEventListener("click", () => {
             document.getElementById("json-result").textContent = JSON.stringify(parsedJson, null, 2);
             document.getElementById("cleaned-html-result").textContent = result.cleaned_html;
             document.getElementById("markdown-result").textContent = result.markdown;
-
+            document.getElementById("media-result").textContent = JSON.stringify( result.media, null, 2);
+            if (result.screenshot){
+                const imgElement = document.createElement("img");
+                // Set the src attribute with the base64 data
+                imgElement.src = `data:image/png;base64,${result.screenshot}`;
+                document.getElementById("screenshot-result").innerHTML = "";
+                document.getElementById("screenshot-result").appendChild(imgElement);
+            }
+            
             // Update code examples dynamically
             const extractionStrategy = data.extraction_strategy;
             const isLLMExtraction = extractionStrategy === "LLMExtractionStrategy";
 
             // REMOVE API TOKEN FROM CODE EXAMPLES
             data.extraction_strategy_args.api_token = "your_api_token";
+
+            if (data.extraction_strategy === "NoExtractionStrategy") {
+                delete data.extraction_strategy_args;
+                delete data.extrac_blocks;
+            }
+
+            if (data.chunking_strategy === "RegexChunking") {
+                delete data.chunking_strategy_args;
+            }
+
+            delete data.verbose;
+
+            if (data.css_selector === "") {
+                delete data.css_selector;
+            }
+
+            if (!data.bypass_cache) {
+                delete data.bypass_cache;
+            }
+
+            if (!data.extract_blocks) {
+                delete data.extract_blocks;
+            }
+
+            if (!data.include_raw_html) {
+                delete data.include_raw_html;
+            }
+
             document.getElementById(
                 "curl-code"
             ).textContent = `curl -X POST -H "Content-Type: application/json" -d '${JSON.stringify({
                 ...data,
                 api_token: isLLMExtraction ? "your_api_token" : undefined,
-            }, null, 2)}' http://localhost:8000/crawl`;
+            }, null, 2)}' https://crawl4ai.com/crawl`;
 
             document.getElementById("python-code").textContent = `import requests\n\ndata = ${JSON.stringify(
                 { ...data, api_token: isLLMExtraction ? "your_api_token" : undefined },
                 null,
                 2
-            )}\n\nresponse = requests.post("http://localhost:8000/crawl", json=data) # OR local host if your run locally \nprint(response.json())`;
+            )}\n\nresponse = requests.post("https://crawl4ai.com/crawl", json=data) # OR local host if your run locally \nprint(response.json())`;
 
             document.getElementById(
                 "nodejs-code"
@@ -150,7 +200,7 @@ document.getElementById("crawl-btn").addEventListener("click", () => {
                 { ...data, api_token: isLLMExtraction ? "your_api_token" : undefined },
                 null,
                 2
-            )};\n\naxios.post("http://localhost:8000/crawl", data) // OR local host if your run locally \n    .then(response => console.log(response.data))\n    .catch(error => console.error(error));`;
+            )};\n\naxios.post("https://crawl4ai.com/crawl", data) // OR local host if your run locally \n    .then(response => console.log(response.data))\n    .catch(error => console.error(error));`;
 
             document.getElementById(
                 "library-code"

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</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

@@ -50,6 +50,20 @@ crawler.warmup()</code></pre>
         <div>
             <pre><code class="language-python">crawler.always_by_pass_cache = True</code></pre>
         </div>
+        <!-- Step 3.5 Screenshot -->
+        <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
+            📸
+            <strong>Let's take a screenshot of the page!</strong>
+        </div>
+        <div>
+            <pre><code class="language-python">result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    screenshot=True
+)
+with open("screenshot.png", "wb") as f:
+    f.write(base64.b64decode(result.screenshot))</code></pre>
+        </div>
+
 
         <!-- Step 4 -->
         <div class="col-span-2 bg-lime-800 p-2 rounded text-zinc-50">
@@ -139,13 +153,13 @@ crawler.warmup()</code></pre>
         </div>
         <div class="">Using JavaScript to click 'Load More' button:</div>
         <div>
-            <pre><code class="language-python">js_code = """
+            <pre><code class="language-python">js_code = ["""
 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(crawler_strategy=crawler_strategy, always_by_pass_cache=True)
-result = crawler.run(url="https://www.nbcnews.com/business")</code></pre>
+"""]
+crawler = WebCrawler(verbos=crawler_strategy, always_by_pass_cache=True)
+result = crawler.run(url="https://www.nbcnews.com/business", js = js_code)</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>
 
         <!-- Conclusion -->

pages/partial/try_it.html

@@ -1,4 +1,4 @@
-<section class="try-it py-8 px-16 pb-20 bg-zinc-900">
+<section class="try-it py-8 px-16 pb-20 bg-zinc-900 overflow-hidden">
     <div class="container mx-auto ">
         <h2 class="text-2xl font-bold mb-4 text-lime-500">Try It Now</h2>
         <div class="flex gap-4">
@@ -20,6 +20,7 @@
                             id="threshold"
                             class="border border-zinc-700 rounded px-4 py-1 bg-zinc-900 text-zinc-300"
                         >
+                            <option value="1">1</option>
                             <option value="5">5</option>
                             <option value="10" selected>10</option>
                             <option value="15">15</option>
@@ -124,7 +125,11 @@
                         <label for="bypass-cache-checkbox" class="text-lime-500 font-bold">Bypass Cache</label>
                     </div>
                     <div class="flex items-center gap-2">
-                        <input type="checkbox" id="extract-blocks-checkbox" checked />
+                        <input type="checkbox" id="screenshot-checkbox" checked />
+                        <label for="screenshot-checkbox" class="text-lime-500 font-bold">Screenshot</label>
+                    </div>
+                    <div class="flex items-center gap-2 hidden">
+                        <input type="checkbox" id="extract-blocks-checkbox" />
                         <label for="extract-blocks-checkbox" class="text-lime-500 font-bold">Extract Blocks</label>
                     </div>
                     <button id="crawl-btn" class="bg-lime-600 text-black font-bold px-4 py-0 rounded">Crawl</button>
@@ -134,7 +139,7 @@
             <div id="loading" class="hidden">
                 <p class="text-white">Loading... Please wait.</p>
             </div>
-            <div id="result" class="flex-1">
+            <div id="result" class="flex-1  overflow-x-auto">
                 <div class="tab-buttons flex gap-2">
                     <button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="json">
                         JSON
@@ -148,15 +153,23 @@
                     <button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="markdown">
                         Markdown
                     </button>
+                    <button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="media">
+                        Medias
+                    </button>
+                    <button class="tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="screenshot">
+                        Screenshot
+                    </button>
                 </div>
                 <div class="tab-content code bg-zinc-900 p-2 rounded h-full border border-zinc-700 text-sm">
                     <pre class="h-full flex"><code id="json-result" class="language-json"></code></pre>
                     <pre class="hidden h-full flex"><code id="cleaned-html-result" class="language-html"></code></pre>
                     <pre class="hidden h-full flex"><code id="markdown-result" class="language-markdown"></code></pre>
+                    <pre class="hidden h-full flex"><code id="media-result" class="language-json"></code></pre>
+                    <pre class="hidden h-full flex"><code id="screenshot-result"></code></pre>
                 </div>
             </div>
 
-            <div id="code_help" class="flex-1">
+            <div id="code_help" class="flex-1  overflow-x-auto">
                 <div class="tab-buttons flex gap-2">
                     <button class="code-tab-btn px-4 py-1 text-sm bg-zinc-700 rounded-t text-lime-500" data-tab="curl">
                         cURL

requirements.crawl.txt

@@ -0,0 +1,13 @@
+aiohttp
+aiosqlite
+bs4
+fastapi
+html2text
+httpx
+pydantic
+python-dotenv
+requests
+rich
+selenium
+uvicorn
+chromedriver-autoinstaller

requirements.txt

@@ -1,20 +1,21 @@
-aiohttp==3.9.5
-aiosqlite==0.20.0
-bs4==0.0.2
-fastapi==0.111.0
-html2text==2024.2.26
-httpx==0.27.0
-litellm==1.37.11
-nltk==3.8.1
-pydantic==2.7.1
-python-dotenv==1.0.1
-requests==2.31.0
-rich==13.7.1
-scikit-learn==1.4.2
-selenium==4.20.0
-uvicorn==0.29.0
-transformers==4.40.2
-chromedriver-autoinstaller==0.6.4
-torch==2.3.0
-onnxruntime==1.14.1
-tokenizers==0.13.2
+aiohttp
+aiosqlite
+bs4
+fastapi
+html2text
+httpx
+litellm
+nltk
+pydantic
+python-dotenv
+requests
+rich
+scikit-learn
+selenium
+uvicorn
+transformers
+chromedriver-autoinstaller
+torch
+onnxruntime
+tokenizers
+pillow

setup.py

@@ -7,11 +7,16 @@ from setuptools.command.install import install
 with open("requirements.txt") as f:
     requirements = f.read().splitlines()
 
+# Read the requirements from requirements.txt
+with open("requirements.crawl.txt") as f:
+    requirements_crawl_only = f.read().splitlines()
+
 # Define the requirements for different environments
 requirements_without_torch = [req for req in requirements if not req.startswith("torch")]
 requirements_without_transformers = [req for req in requirements if not req.startswith("transformers")]
 requirements_without_nltk = [req for req in requirements if not req.startswith("nltk")]
 requirements_without_torch_transformers_nlkt = [req for req in requirements if not req.startswith("torch") and not req.startswith("transformers") and not req.startswith("nltk")]
+requirements_crawl_only = [req for req in requirements if not req.startswith("torch") and not req.startswith("transformers") and not req.startswith("nltk")]
 
 class CustomInstallCommand(install):
     """Customized setuptools install command to install spacy without dependencies."""
@@ -21,7 +26,7 @@ class CustomInstallCommand(install):
 
 setup(
     name="Crawl4AI",
-    version="0.2.1",
+    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",
@@ -34,7 +39,7 @@ setup(
     extras_require={
         "all": requirements,  # Include all requirements
         "colab": requirements_without_torch,  # Exclude torch for Colab
-        "crawl": requirements_without_torch_transformers_nlkt
+        "crawl": requirements_crawl_only,  # Include only crawl requirements
     },
     cmdclass={
         'install': CustomInstallCommand,