botasaurus 4.0.97
The All in One Web Scraping Framework
License: MIT
Requires Python: >=3.7
Keywords: crawler, framework, scraping, crawling, web-scraping, web-scraping-python, cloudflare-bypass, anti-detection, bot-detection, automation, webdriver, browser

<p align="center">
  <img src="https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/mascot.png" alt="botasaurus" />
</p>
  <div align="center" style="margin-top: 0;">
  <h1>🤖 Botasaurus 🤖</h1>
  </div>

<h3 align="center">
  The All in One Framework to Build Undefeatable Scrapers
</h3>

<p align="center">
  <b>The web has evolved. Finally, web scraping has too.</b>
</p>


<p align="center">
  <img src="https://views.whatilearened.today/views/github/omkarcloud/botasaurus.svg" width="80px" height="28px" alt="View" />
</p>


<p align="center">
  <a href="https://gitpod.io/#https://github.com/omkarcloud/botasaurus-starter">
    <img alt="Run in Gitpod" src="https://gitpod.io/button/open-in-gitpod.svg" />
  </a>
</p>

## 🐿️ Botasaurus In a Nutshell

How wonderful that of all the web scraping tools out there, you chose to learn about Botasaurus. Congratulations! 

And now that you are here, you are in for an exciting, unusual, and rewarding journey that will make your web scraping life a lot easier.

Now, let me tell you about Botasaurus in bullet points. (Because as per marketing gurus, YOU as a member of the Developer Tribe have a VERY short attention span.)

*So, what is Botasaurus?*

Botasaurus is an all-in-one web scraping framework that enables you to build awesome scrapers in less time, with less code, and with more fun.

We have put all our web scraping experience and best practices into Botasaurus to save you hundreds of hours of development time! 

Now, for the magical powers awaiting you after learning Botasaurus:

- In terms of humaneness, what Superman is to Man, Botasaurus is to Selenium and Playwright. Easily pass every (Yes, E-V-E-R-Y) bot test, and build undetected scrapers.  
    
In the video below, watch as we **bypass some of the best bot detection systems**:

- ✅ [Cloudflare Web Application Firewall (WAF)](https://nopecha.com/demo/cloudflare)  
- ✅ [BrowserScan Bot Detection](https://www.browserscan.net/bot-detection)  
- ✅ [Fingerprint Bot Detection](https://fingerprint.com/products/bot-detection/)  
- ✅ [Datadome Bot Detection]((https://fingerprint-scan.com/))  
- ✅ [Cloudflare Turnstile CAPTCHA](https://turnstile.zeroclover.io/)

<p align="center">
<video src='https://github.com/user-attachments/assets/b4f6171f-f2a2-4255-9feb-2973ee9a25ae'/>
</p>

🔗 Want to try it yourself?
See the code behind these tests [here](https://github.com/omkarcloud/botasaurus/blob/master/bot_detection_tests.py)

- Perform realistic, human-like mouse movements and say sayonara to detection
![human-mode-demo](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/human-mode-demo.gif)

- Convert your scraper into a desktop app for Mac, Windows, and Linux in 1 day, so not only developers but everyone can use your web scraper.

![desktop-app-photo](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/desktop-app-photo.png)

- Turn your scraper into a beautiful website, making it easy for your customers to use it from anywhere, anytime.

![pro-gmaps-demo](https://raw.githubusercontent.com/omkarcloud/google-maps-scraper/master/screenshots/demo.gif)

- Save up to 97%, yes 97%, on browser proxy costs by using [browser-based fetch requests.](https://github.com/omkarcloud/botasaurus#how-to-significantly-reduce-proxy-costs-when-scraping-at-scale)

- Easily save hours of development time with easy parallelization, profiles, extensions, and proxy configuration. Botasaurus makes asynchronous, parallel scraping child's play.

- Use caching, sitemap, data cleaning, and other utilities to save hours of time spent writing and debugging code.

- Easily scale your scraper to multiple machines with Kubernetes, and get your data faster than ever.

And those are just the highlights. I mean! 

There is so much more to Botasaurus that you will be amazed at how much time you will save with it.

## 🚀 Getting Started with Botasaurus

Let's dive right in with a straightforward example to understand Botasaurus.

In this example, we will go through the steps to scrape the heading text from [https://www.omkar.cloud/](https://www.omkar.cloud/).

![Botasaurus in action](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/starter-bot-running.gif)

### Step 1: Install Botasaurus

First things first, you need to install Botasaurus. Run the following command in your terminal:

```shell
python -m pip install --upgrade botasaurus
```

### Step 2: Set Up Your Botasaurus Project

Next, let's set up the project:

1. Create a directory for your Botasaurus project and navigate into it:

```shell
mkdir my-botasaurus-project
cd my-botasaurus-project
code .  # This will open the project in VSCode if you have it installed
```

### Step 3: Write the Scraping Code

Now, create a Python script named `main.py` in your project directory and paste the following code:

```python
from botasaurus.browser import browser, Driver

@browser
def scrape_heading_task(driver: Driver, data):
    # Visit the Omkar Cloud website
    driver.get("https://www.omkar.cloud/")
    
    # Retrieve the heading element's text
    heading = driver.get_text("h1")

    # Save the data as a JSON file in output/scrape_heading_task.json
    return {
        "heading": heading
    }
     
# Initiate the web scraping task
scrape_heading_task()
```

Let's understand this code:

- We define a custom scraping task, `scrape_heading_task`, decorated with `@browser`:
```python
@browser
def scrape_heading_task(driver: Driver, data):
```  

- Botasaurus automatically provides a Humane Driver to our function:
```python
def scrape_heading_task(driver: Driver, data):
```  

- Inside the function, we:
    - Visit Omkar Cloud
    - Extract the heading text
    - Return the data to be automatically saved as `scrape_heading_task.json` by Botasaurus:
```python
    driver.get("https://www.omkar.cloud/")
    heading = driver.get_text("h1")
    return {"heading": heading}
```  

- Finally, we initiate the scraping task:
```python
# Initiate the web scraping task
scrape_heading_task()
```  

### Step 4: Run the Scraping Task

Time to run it:

```shell
python main.py
```

After executing the script, it will:
- Launch Google Chrome
- Visit [omkar.cloud](https://www.omkar.cloud/)
- Extract the heading text
- Save it automatically as `output/scrape_heading_task.json`.

![Botasaurus in action](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/starter-bot-running.gif)

Now, let's explore another way to scrape the heading using the `request` module. Replace the previous code in `main.py` with the following:

```python
from botasaurus.request import request, Request
from botasaurus.soupify import soupify

@request
def scrape_heading_task(request: Request, data):
    # Visit the Omkar Cloud website
    response = request.get("https://www.omkar.cloud/")

    # Create a BeautifulSoup object    
    soup = soupify(response)
    
    # Retrieve the heading element's text
    heading = soup.find('h1').get_text()

    # Save the data as a JSON file in output/scrape_heading_task.json
    return {
        "heading": heading
    }     
# Initiate the web scraping task
scrape_heading_task()
```

In this code:

- We scrape the HTML using `request`, which is specifically designed for making browser-like humane requests.
- Next, we parse the HTML into a `BeautifulSoup` object using `soupify()` and extract the heading.

### Step 5: Run the Scraping Task (which makes Humane HTTP Requests)

Finally, run it again:

```shell
python main.py
```

This time, you will observe the exact same result as before, but instead of opening a whole browser, we are making browser-like humane HTTP requests.

## 💡 Understanding Botasaurus

### What is Botasaurus Driver, and why should I use it over Selenium and Playwright?

Botasaurus Driver is a web automation driver like Selenium, and the single most important reason to use it is because it is truly humane. You will not, and I repeat NOT, have any issues accessing any website.

Plus, it is super fast to launch and use, and the API is designed by and for web scrapers, and you will love it.

### How do I access Cloudflare-protected pages using Botasaurus?

Cloudflare is the most popular protection system on the web. So, let's see how Botasaurus can help you solve various Cloudflare challenges.

**Connection Challenge**

This is the single most popular challenge and requires making a browser-like connection with appropriate headers. It's commonly used for:
- Product Pages
- Blog Pages 
- Search Result Pages

<!-- Example Page: https://www.g2.com/products/github/reviews -->

#### What Works?

- Visiting the website via Google Referrer (which makes it seem as if the user has arrived from a Google search).

```python
from botasaurus.browser import browser, Driver

@browser
def scrape_heading_task(driver: Driver, data):
    # Visit the website via Google Referrer
    driver.google_get("https://www.cloudflare.com/en-in/")
    driver.prompt()
    heading = driver.get_text('h1')
    return heading

scrape_heading_task()
```

- Use the request module. The Request Object is smart and, by default, visits any link with a Google Referrer. Although it works, you will need to use retries.

```python
from botasaurus.request import request, Request

@request(max_retry=10)
def scrape_heading_task(request: Request, data):
    response = request.get("https://www.cloudflare.com/en-in/")
    print(response.status_code)
    response.raise_for_status()
    return response.text

scrape_heading_task()
```

**JS with Captcha Challenge**

This challenge requires performing JS computations that differentiate a Chrome controlled by Selenium/Puppeteer/Playwright from a real Chrome. It also involves solving a Captcha. It's used to for pages which are rarely but sometimes visited by people, like:
- 5th Review page
- Auth pages

Example Page: https://nopecha.com/demo/cloudflare

#### What Does Not Work?
Using `@request` does not work because although it can make browser-like HTTP requests, it cannot run JavaScript to solve the challenge.

#### What Works?
Pass the `bypass_cloudflare=True` argument to the `google_get` method.

```python
from botasaurus.browser import browser, Driver

@browser
def scrape_heading_task(driver: Driver, data):
    driver.google_get("https://nopecha.com/demo/cloudflare", bypass_cloudflare=True)
    driver.prompt()

scrape_heading_task()
```
![Cloudflare JS with Captcha Challenge Demo](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/cloudflare-js-captcha-demo.gif)

### What are the benefits of a UI scraper?

Here are some benefits of creating a scraper with a user interface:

- Simplify your scraper usage for customers, eliminating the need to teach them how to modify and run your code.
- Protect your code by hosting the scraper on the web and offering a monthly subscription, rather than providing full access to your code. This approach:
  - Safeguards your Python code from being copied and reused, increasing your customer's lifetime value.
  - Generate monthly recurring revenue via subscription from your customers, surpassing a one-time payment.
- Enable sorting, filtering, and downloading of data in various formats (JSON, Excel, CSV, etc.).
- Provide access via a REST API for seamless integration.
- Create a polished frontend, backend, and API integration with minimal code.

### How to run a UI-based scraper?

Let's run the Botasaurus Starter Template (the recommended template for greenfield Botasaurus projects), which scrapes the heading of the provided link by following these steps:

1. Clone the Starter Template:
   ```
   git clone https://github.com/omkarcloud/botasaurus-starter my-botasaurus-project
   cd my-botasaurus-project
   ```

2. Install dependencies (will take a few minutes):
   ```
   python -m pip install -r requirements.txt
   python run.py install
   ```

3. Run the scraper:
   ```
   python run.py
   ```

Your browser will automatically open up at http://localhost:3000/. Then, enter the link you want to scrape (e.g., https://www.omkar.cloud/) and click on the Run Button.

![starter-scraper-demo](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/starter-scraper-demo.gif)

After some seconds, the data will be scraped.
![starter-scraper-demo-result](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/starter-scraper-demo-result.png)

Visit http://localhost:3000/output to see all the tasks you have started.

![starter-scraper-demo-tasks](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/starter-scraper-demo-tasks.png)

Go to http://localhost:3000/about to see the rendered README.md file of the project.

![starter-scraper-demo-readme](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/starter-scraper-demo-readme.png)

Finally, visit http://localhost:3000/api-integration to see how to access the scraper via API.

![starter-scraper-demo-api](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/starter-scraper-demo-api.png)

The API documentation is generated dynamically based on your scraper's inputs, sorts, filters, etc., and is unique to your scraper. 

So, whenever you need to run the scraper via API, visit this tab and copy the code specific to your scraper.

### How to create a UI scraper using Botasaurus?

Creating a UI scraper with Botasaurus is a simple 3-step process:
1. Create your scraper function
2. Add the scraper to the server using 1 line of code 
3. Define the input controls for the scraper

To understand these steps, let's go through the code of the Botasaurus Starter Template that you just ran.

#### Step 1: Create the Scraper Function

In `src/scrape_heading_task.py`, we define a scraping function that basically does the following:

1. Receives a `data` object and extracts the "link".
2. Retrieves the HTML content of the webpage using the "link".
3. Converts the HTML into a BeautifulSoup object.
4. Locates the heading element, extracts its text content, and returns it.

```python
from botasaurus.request import request, Request
from botasaurus.soupify import soupify

@request
def scrape_heading_task(request: Request, data):
    # Visit the Link
    response = request.get(data["link"])

    # Create a BeautifulSoup object    
    soup = soupify(response)
    
    # Retrieve the heading element's text
    heading = soup.find('h1').get_text()

    # Save the data as a JSON file in output/scrape_heading_task.json
    return {
        "heading": heading
    }
```

#### Step 2: Add the Scraper to the Server

In `backend/scrapers.py`, we:
- Import our scraping function
- Use `Server.add_scraper()` to register the scraper

```python
from botasaurus_server.server import Server
from src.scrape_heading_task import scrape_heading_task

# Add the scraper to the server
Server.add_scraper(scrape_heading_task)
```

#### Step 3: Define the Input Controls

In `backend/inputs/scrape_heading_task.js`, we:
- Define a `getInput` function that takes the controls parameter
- Add a link input control to it
- Use JSDoc comments to enable IntelliSense Code Completion in VSCode as you won't be able to remember all the controls in botasaurus.


```js
/**
 * @typedef {import('../../frontend/node_modules/botasaurus-controls/dist/index').Controls} Controls
 */

/**
 * @param {Controls} controls
 */
function getInput(controls) {
    controls
        // Render a Link Input, which is required, defaults to "https://stackoverflow.blog/open-source". 
        .link('link', { isRequired: true, defaultValue: "https://stackoverflow.blog/open-source" })
}
```

Above was a simple example; below is a real-world example with multi-text, number, switch, select, section, and other controls.

```js
/**
 * @typedef {import('../../frontend/node_modules/botasaurus-controls/dist/index').Controls} Controls
 */


/**
 * @param {Controls} controls
 */
function getInput(controls) {
    controls
        .listOfTexts('queries', {
            defaultValue: ["Web Developers in Bangalore"],
            placeholder: "Web Developers in Bangalore",
            label: 'Search Queries',
            isRequired: true
        })
        .section("Email and Social Links Extraction", (section) => {
            section.text('api_key', {
                placeholder: "2e5d346ap4db8mce4fj7fc112s9h26s61e1192b6a526af51n9",
                label: 'Email and Social Links Extraction API Key',
                helpText: 'Enter your API key to extract email addresses and social media links.',
            })
        })
        .section("Reviews Extraction", (section) => {
            section
                .switch('enable_reviews_extraction', {
                    label: "Enable Reviews Extraction"
                })
                .numberGreaterThanOrEqualToZero('max_reviews', {
                    label: 'Max Reviews per Place (Leave empty to extract all reviews)',
                    placeholder: 20,
                    isShown: (data) => data['enable_reviews_extraction'], defaultValue: 20,
                })
                .choose('reviews_sort', {
                    label: "Sort Reviews By",
                    isRequired: true, isShown: (data) => data['enable_reviews_extraction'], defaultValue: 'newest', options: [{ value: 'newest', label: 'Newest' }, { value: 'most_relevant', label: 'Most Relevant' }, { value: 'highest_rating', label: 'Highest Rating' }, { value: 'lowest_rating', label: 'Lowest Rating' }]
                })
        })
        .section("Language and Max Results", (section) => {
            section
                .addLangSelect()
                .numberGreaterThanOrEqualToOne('max_results', {
                    placeholder: 100,
                    label: 'Max Results per Search Query (Leave empty to extract all places)'
                })
        })
        .section("Geo Location", (section) => {
            section
                .text('coordinates', {
                    placeholder: '12.900490, 77.571466'
                })
                .numberGreaterThanOrEqualToOne('zoom_level', {
                    label: 'Zoom Level (1-21)',
                    defaultValue: 14,
                    placeholder: 14
                })
        })
}
```

I encourage you to paste the above code into `backend/inputs/scrape_heading_task.js` and reload the page, and you will see a complex set of input controls like the image shown.

![complex-input](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/complex-input.png)

Now, to use the Botasaurus UI for adding new scrapers, remember these points:

1. Create a `backend/inputs/{your_scraping_function_name}.js` file for each scraping function.
2. Define the `getInput` function in the file with the necessary controls.
3. Use JSDoc comments to enable IntelliSense code completion in VSCode, as you won't be able to remember all the controls in Botasaurus.

Use this template as a starting point for new scraping function's input controls js file:

```js
/**
 * @typedef {import('../../frontend/node_modules/botasaurus-controls/dist/index').Controls} Controls
 */

/**
 * @param {Controls} controls
 */
function getInput(controls) {
    // Define your controls here.
}
```

That's it! With these simple steps, you can create a fully functional UI scraper using Botasaurus.

Later, you will learn how to add sorts and filters to make your UI scraper even more powerful and user-friendly.

![sorts-filters](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/sorts-filters.png)

### What is a Desktop Extractor?
A **Desktop Extractor** is a standalone application that runs on your computer and extracts specific data from websites, PDFs, Excel files, and other documents. Unlike web-based tools, desktop extractors run locally, giving **faster performance** and **zero cloud costs**.

![Desktop Extractor showing an application interface with extraction options](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/desktop-app-photo.png)

### What advantages do Desktop Scrapers have over web-based scrapers?
**Desktop Scrapers** offer key advantages over web-based scraper solutions like Outscraper:  

- **Zero Infrastructure Costs**: 
   - Runs on the user's machine, eliminating expensive cloud computing fees.  
   - Lower cloud costs allow you to offer lower pricing, attracting more customers and increasing revenue.

- **Faster Execution**:  
  - Instant execution, no delays for cloud resource allocation.  
  - Uses the user's system, which is much faster than shared cloud servers.  

- **Increased Customer Engagement**:  
  The app sits right on the user's desktop, encouraging frequent use compared to web tools they must actively visit via browser.  

- **Cross-Platform Deployment in 1 Day**:  
  With **Botasaurus**, you can launch a desktop scraper for **Windows, macOS, and Linux** within a day. No need to build a website, manage servers, or handle scaling issues. Bota Desktop includes built-in features such as:
  - Task management
  - Data Table
  - Data export (Excel, CSV, etc.)
  - Sorting & Filtering
  - Caching and many more

With zero usage costs, faster performance, and easier development, Desktop Scrapers outperform web-based alternatives.

### How to Build a Desktop Extractor
Creating Desktop Extractors is easier than you think! All you need is a basic understanding of JavaScript. Once you're ready, read the [Desktop Extraction Tutorial](https://www.omkar.cloud/botasaurus/docs/botasaurus-desktop/quick-start), where we'll guide you through building two practical extractors:
- **Yahoo Finance Stock Scraper** – Extracts real-time stock prices from Yahoo Finance.

![Stock Scraper Demo showing the application extracting stock prices from Yahoo Finance](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/stock-scraper-preview.gif) 

- **Amazon Invoice PDF Extractor** – Automates the extraction of key invoice data like Document Number, Document Date, and Place of Supply from Amazon PDFs.  

![PDF Extraction Demo showing the application extracting data from Amazon PDF invoices](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/pdf-extract-preview.gif)  

As a web scraper, you might naturally want to focus on web scraping. Still, I want you to create the **Amazon Invoice PDF Extractor** project. Why? Because many developers overlook the immense potential of extracting data from PDFs, Excel files, and other documents.  

**Document Data Extraction is a large untapped market.** For example, even in most developed countries, accountants often spend hundreds of hours manually entering invoice data for tax filings. A desktop extractor can transform this tedious, error-prone process into a task that takes just minutes, delivering 100% accurate results.

Please read the step-by-step tutorial [here](https://www.omkar.cloud/botasaurus/docs/botasaurus-desktop/quick-start). By the end of this short guide, you'll be able to create powerful desktop extractors in very little time.

### What is Botasaurus, and what are its main features?

Botasaurus is an all-in-one web scraping framework designed to achieve three main goals:  

1. Provide essential web scraping utilities to streamline the scraping process.

To accomplish these goals, Botasaurus gives you 3 decorators:
- `@browser`: For scraping web pages using a humane browser.
- `@request`: For scraping web pages using lightweight and humane HTTP requests.
- `@task`: 
  - For scraping web pages using third-party libraries like `playwright` or `selenium`.
  - or, For running non-web scraping tasks, such as data processing (e.g., converting video to audio). Botasaurus is not limited to web scraping tasks; any Python function can be made accessible with a stunning UI and user-friendly API.

In practice, while developing with Botasaurus, you will spend most of your time in the following areas:
- Configuring your scrapers via decorators with settings like:
  - Which proxy to use
  - How many scrapers to run in parallel, etc.
- Writing your core web scraping logic using BeautifulSoup (bs4) or the Botasaurus Driver.

Additionally, you will utilize the following Botasaurus utilities for debugging and development:
- `bt`: Mainly for writing JSON, EXCEL, and HTML temporary files, and for data cleaning.
- `Sitemap`: For accessing the website's links and sitemap.
- Minor utilities like:
  - `LocalStorage`: For storing scraper state.
  - `soupify`: For creating BeautifulSoup objects from Driver, Requests response, Driver Element, or HTML string.
  - `IPUtils`: For obtaining information (IP, country, etc.) about the current IP address.
  - `Cache`: For managing the cache.

By simply configuring these three decorators (`@browser`, `@request`, and `@task`) with arguments, you can easily create `real-time scrapers` and `large-scale datasets`, thus saving you countless hours that would otherwise be spent writing and debugging code from scratch.

2. Offering a Python-based UI scraper that allows non-technical users to run scrapers online by simply visiting a website link. (As described in the previous FAQ)  

3. Make it easy to create desktop applications for Mac, Windows, and Linux, using JavaScript. More details can be found in the [Botasaurus Desktop Documentation here](https://www.omkar.cloud/botasaurus/docs/botasaurus-desktop/introduction).

### How to use decorators in Botasaurus?

Decorators are the heart of Botasaurus. To use a decorator function, you can call it with:
- A single item
- A list of items

If a scraping function is given a list of items, it will sequentially call the scraping function for each data item.

For example, if you pass a list of three links to the `scrape_heading_task` function:
```python
from botasaurus.browser import browser, Driver

@browser
def scrape_heading_task(driver: Driver, link):
    driver.get(link)
    heading = driver.get_text("h1")
    return heading

scrape_heading_task(["https://www.omkar.cloud/", "https://www.omkar.cloud/blog/", "https://stackoverflow.com/"]) # <-- list of items
```

Then, Botasaurus will launch a new browser instance for each item, and the final results will be stored in `output/scrape_heading_task.json`.

![list-demo](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/list-demo.gif)

### How does Botasaurus help me in debugging?

Botasaurus helps you in debugging by:

- Easily viewing the result of the scraping function, as it is saved in `output/{your_scraping_function_name}.json`. Say goodbye to print statements.

![scraped data](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/scraped-data.png)

- Bringing your attention to errors in browser mode with a beep sound and pausing the browser, allowing you to debug the error on the spot.

![](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/error-prompt.png)

- Even if an exception is raised in headless mode, it will still open the website in your default browser, making it easier to debug code in a headless browser. (Isn't it cool?)

![headless-error](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/headless-error.png)

### How to configure the Browser Decorator?

The Browser Decorator allows you to easily configure various aspects of the browser, such as:

- Blocking images and CSS
- Setting up proxies
- Specifying profiles
- Enabling headless mode
- Using Chrome extensions
- Captcha Solving
- Selecting language
- Passing Arguments to Chrome

#### Blocking Images and CSS

Blocking images is one of the most important configurations when scraping at scale. Blocking images can significantly: 
- Speed up your web scraping tasks
- Reduce bandwidth usage
- And save money on proxies. (Best of All!)

For example, a page that originally takes 4 seconds and 12 MB to load might only take one second and 100 KB after blocking images and CSS.

To block images, use the `block_images` parameter:

```python
@browser(
    block_images=True,
)
```

To block both images and CSS, use `block_images_and_css`:

```python
@browser(
    block_images_and_css=True,
)    
```

#### Proxies

To use proxies, simply specify the `proxy` parameter:

```python
@browser(
    proxy="http://username:password@proxy-provider-domain:port"
)    
def visit_what_is_my_ip(driver: Driver, data):
    driver.get("https://whatismyipaddress.com/")
    driver.prompt()

visit_what_is_my_ip()
```

You can also pass a list of proxies, and the proxy will be automatically rotated:

```python
@browser(
    proxy=[
        "http://username:password@proxy-provider-domain:port", 
        "http://username2:password2@proxy-provider-domain:port"
    ]
)
def visit_what_is_my_ip(driver: Driver, data):
    driver.get("https://whatismyipaddress.com/")
    driver.prompt()

visit_what_is_my_ip() 
```

#### Profile

Easily specify the Chrome profile using the `profile` option:

```python
@browser(
    profile="pikachu"
)    
```

However, each Chrome profile can become very large (e.g., 100 MB) and can eat up all your computer storage. 

To solve this problem, use the `tiny_profile` option, which is a lightweight alternative to Chrome profiles. 

When creating hundreds of Chrome profiles, it is highly recommended to use the `tiny_profile` option because:

- Creating 1000 Chrome profiles will take at least 100 GB, whereas 1000 tiny profiles will take up only 1 MB of storage, making tiny profiles easy to store and back up.
- Tiny profiles are cross-platform, meaning you can create profiles on a Linux server, copy the `./profiles` folder to a Windows PC, and easily run them.

Under the hood, tiny profiles persist cookies from visited websites, making them extremely lightweight (around 1 KB) while providing the same session persistence.

Here's how to use the tiny profile:

```python
@browser(
    tiny_profile=True, 
    profile="pikachu",
)    
```

#### Headless Mode

Enable headless mode with `headless=True`:
```python
@browser(
    headless=True
)    
```
Note that if you use headless mode, you will surely be identified by services like Cloudflare and Datadome. Therefore, use headless mode only when scraping websites that don't use such services.

#### Chrome Extensions

Botasaurus allows the use of ANY Chrome Extension with just 1 line of code. The example below shows how to use the Mouse Coordinates Chrome Extension to show current mouse X and Y coordinates on web pages:

```python
from botasaurus.browser import browser, Driver
from chrome_extension_python import Extension

@browser(
    extensions=[
        Extension(
            "https://chromewebstore.google.com/detail/mouse-coordinates/mfohnjojhopfcahiddmeljeholnciakl"
        )
    ],
)
def scrape_while_blocking_ads(driver: Driver, data):
    driver.get("https://example.com/")
    driver.prompt()

scrape_while_blocking_ads()
```

In some cases, an extension may require additional configuration, such as API keys or credentials. For such scenarios, you can create a custom extension. Learn more about creating and configuring custom extensions [here](https://github.com/omkarcloud/chrome-extension-python).

#### Captcha Solving

Encountering captchas is common in web scraping. You can use the [capsolver_extension_python](https://github.com/omkarcloud/capsolver-extension-python?tab=readme-ov-file#installation) package to automatically solve CAPTCHAs with Capsolver.  

To use it, first install the package:  

```bash
python -m pip install capsolver_extension_python
```

Then, integrate it into your code as follows:  

```python
from botasaurus.browser import browser, Driver
from capsolver_extension_python import Capsolver

# Replace "CAP-MY_KEY" with your actual CapSolver API key
@browser(extensions=[Capsolver(api_key="CAP-MY_KEY")])  
def solve_captcha(driver: Driver, data):
    driver.get("https://recaptcha-demo.appspot.com/recaptcha-v2-checkbox.php")
    driver.prompt()

solve_captcha()
```  

#### Language

Specify the language using the `lang` option:

```python
from botasaurus.lang import Lang

@browser(
    lang=Lang.Hindi,
)
```

#### User Agent and Window Size

To make the browser really humane, Botasaurus does not change browser fingerprints by default, because using fingerprints makes the browser easily identifiable by running CSS tests to find mismatches between the provided user agent and the actual user agent.

However, if you need fingerprinting, use the `user_agent` and `window_size` options:

```python
from botasaurus.browser import browser, Driver
from botasaurus.user_agent import UserAgent
from botasaurus.window_size import WindowSize

@browser(
    user_agent=UserAgent.RANDOM,
    window_size=WindowSize.RANDOM,
)
def visit_whatsmyua(driver: Driver, data):
    driver.get("https://www.whatsmyua.info/")
    driver.prompt()

visit_whatsmyua()
```

When working with profiles, you want the fingerprints to remain consistent. You don't want the user's user agent to be Chrome 106 on the first visit and then become Chrome 102 on the second visit. 

So, when using profiles, use the `HASHED` option to generate a consistent user agent and window size based on the profile's hash:

```python
from botasaurus.browser import browser, Driver
from botasaurus.user_agent import UserAgent
from botasaurus.window_size import WindowSize

@browser(
    profile="pikachu",
    user_agent=UserAgent.HASHED,
    window_size=WindowSize.HASHED,
)
def visit_whatsmyua(driver: Driver, data):
    driver.get("https://www.whatsmyua.info/")
    driver.prompt()
    
visit_whatsmyua()

# Everytime Same UserAgent and WindowSize
visit_whatsmyua()
```
#### Passing Arguments to Chrome

To pass arguments to Chrome, use the `add_arguments` option:

```python
@browser(
    add_arguments=['--headless=new'],
)
```

To dynamically generate arguments based on the `data` parameter, pass a function:

```python
def get_arguments(data):
    return ['--headless=new']

@browser(
    add_arguments=get_arguments,
)
```

#### Wait for Complete Page Load

By default, Botasaurus waits for all page resources (DOM, JavaScript, CSS, images, etc.) to load before calling your scraping function with the driver.

However, sometimes the DOM is ready, but JavaScript, images, etc., take forever to load. 

In such cases, you can set `wait_for_complete_page_load` to `False` to interact with the DOM as soon as the HTML is parsed and the DOM is ready:

```python
@browser(
    wait_for_complete_page_load=False,
)
```

#### Reuse Driver

Consider the following example:

```python
from botasaurus.browser import browser, Driver

@browser
def scrape_data(driver: Driver, link):
    driver.get(link)

scrape_data(["https://www.omkar.cloud/", "https://www.omkar.cloud/blog/", "https://stackoverflow.com/"])
```

If you run this code, the browser will be recreated on each page visit, which is inefficient. 

![list-demo-omkar](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/list-demo.gif)

To solve this problem, use the `reuse_driver` option which is great for cases like:
- Scraping a large number of links and reusing the same browser instance for all page visits.
- Running your scraper in a cloud server to scrape data on demand, without recreating Chrome on each request.

Here's how to use `reuse_driver` which will reuse the same Chrome instance for visiting each link.

```python
from botasaurus.browser import browser, Driver

@browser(
    reuse_driver=True
)
def scrape_data(driver: Driver, link):
    driver.get(link)

scrape_data(["https://www.omkar.cloud/", "https://www.omkar.cloud/blog/", "https://stackoverflow.com/"])
```
**Result**
![list-demo-reuse-driver.gif](https://raw.githubusercontent.com/omkarcloud/botasaurus/master/images/list-demo-reuse-driver.gif)
 
---

Also, by default, whenever the program ends or is canceled, Botasaurus smartly closes any open Chrome instances, leaving no instances running in the background.

In rare cases, you may want to explicitly close the Chrome instance. For such scenarios, you can use the `.close()` method on the scraping function:

```python
scrape_data.close()
```

This will close any Chrome instances that remain open after the scraping function ends.


### How to Significantly Reduce Proxy Costs When Scraping at Scale?

Recently, we had a project requiring access to around 100,000 pages from a well-protected website, necessitating the use of Residential Proxies.

Even after blocking images, we still required 250GB of proxy bandwidth, costing approximately $1050 (at $4.2 per GB with IP Royal). 

This was beyond our budget :(

To solve this, we implemented a smart strategy:
- We first visited the website normally.
- We then made requests for subsequent pages using the browser's `fetch` API.

Since we were only requesting the HTML, which was well compressed by the browser, we reduced our proxy bandwidth needs to just 5GB, costing only $30.

This resulted in savings of around $1000!

Here's an example of how you can do something similar in Botasaurus:

```python
from botasaurus.browser import browser, Driver
from botasaurus.soupify import soupify

@browser(
    reuse_driver=True,  # Reuse the browser
    max_retry=5,        # Retry up to 5 times on failure
)
def scrape_data(driver: Driver, link):
    # If the browser is newly opened, first visit the link
    if driver.config.is_new:
        driver.google_get(link)
    
    # Make requests using the browser fetch API
    response = driver.requests.get(link)
    response.raise_for_status()  # Ensure the request was successful
    html = response.text

    # Parse the HTML to extract the desired data
    soup = soupify(html)
    stock_name = soup.select_one('[data-testid="quote-hdr"] h1').get_text()
    stock_price = soup.select_one('[data-testid="qsp-price"]').get_text()
    
    return {
        "stock_name": stock_name,
        "stock_price": stock_price,
    }

# List of URLs to scrape
links = [
    "https://finance.yahoo.com/quote/AAPL/",
    "https://finance.yahoo.com/quote/GOOG/",
    "https://finance.yahoo.com/quote/MSFT/",
]

# Execute the scraping function for the list of links
scrape_data(links)
```

Note:
1. **Dealing with 429 (Too Many Requests) Errors**

   If you encounter a 429 error, add a delay before making another request. Most websites using Nginx, setting a rate limit of 1 request per second. To respect this limit, a delay of 1.13 seconds is recommended.

   ```python
   driver.sleep(1.13)  # Delay to respect the rate limit
   response = driver.requests.get(link)
   ```

2. **Handling 400 Errors Due to Large Cookies**

   If you encounter a 400 error with a "cookie too large" message, delete the cookies and retry the request.

   ```python
   response = driver.requests.get(link)

   if response.status_code == 400:
       driver.delete_cookies()  # Delete cookies to resolve the error
       driver.short_random_sleep()  # Short delay before retrying
       response = driver.requests.get(link)
   ```
3. You can also use `driver.requests.get_mank(links)` to make multiple requests in parallel, which is faster than making them sequentially.

### How to Configure the Browser's Chrome Profile, Language, and Proxy Dynamically Based on Data Parameters?

The decorators in Botasaurus are really flexible, allowing you to pass a function that can derive the browser configuration based on the data item parameter. This is particularly useful when working with multiple Chrome profiles.

You can dynamically configure the browser's Chrome profile and proxy using decorators in two ways:

1. Using functions to extract configuration values from data:
   - Define functions to extract the desired configuration values from the `data` parameter.
   - Pass these functions as arguments to the `@browser` decorator.

   Example:
   ```python
   from botasaurus.browser import browser, Driver

   def get_profile(data):
       return data["profile"]

   def get_proxy(data):
       return data[