# Scrapling Interactive Shell Guide

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**Powerful Web Scraping REPL for Developers and Data Scientists**

The Scrapling Interactive Shell is an enhanced IPython-based environment designed specifically for Web Scraping tasks. It provides instant access to all Scrapling features, clever shortcuts, automatic page management, and advanced tools like curl command conversion.

## Why use the Interactive Shell?

The interactive shell transforms web scraping from a slow script-and-run cycle into a fast, exploratory experience. It's perfect for:

- **Rapid prototyping**: Test scraping strategies instantly
- **Data exploration**: Interactively navigate and extract from websites  
- **Learning Scrapling**: Experiment with features in real-time
- **Debugging scrapers**: Step through requests and inspect results
- **Converting workflows**: Transform curl commands from browser DevTools to a Fetcher request in a one-liner

## Getting Started

### Launch the Shell

```bash
# Start the interactive shell
scrapling shell

# Execute code and exit (useful for scripting)
scrapling shell -c "get('https://quotes.toscrape.com'); print(len(page.css('.quote')))"

# Set logging level
scrapling shell --loglevel info
```

Once launched, you'll see the Scrapling banner and can immediately start scraping as the video above shows:

```python
# No imports needed - everything is ready!
>>> get('https://news.ycombinator.com')

>>> # Explore the page structure
>>> page.css('a')[:5]  # Look at first 5 links

>>> # Refine your selectors
>>> stories = page.css('.titleline>a')
>>> len(stories)
30

>>> # Extract specific data
>>> for story in stories[:3]:
...     title = story.text
...     url = story['href']
...     print(f"{title}: {url}")

>>> # Try different approaches
>>> titles = page.css('.titleline>a::text')  # Direct text extraction
>>> urls = page.css('.titleline>a::attr(href)')  # Direct attribute extraction
```

## Built-in Shortcuts

The shell provides convenient shortcuts that eliminate boilerplate code:

- **`get(url, **kwargs)`** - HTTP GET request (instead of `Fetcher.get`)
- **`post(url, **kwargs)`** - HTTP POST request (instead of `Fetcher.post`)
- **`put(url, **kwargs)`** - HTTP PUT request (instead of `Fetcher.put`)
- **`delete(url, **kwargs)`** - HTTP DELETE request (instead of `Fetcher.delete`)
- **`fetch(url, **kwargs)`** - Browser-based fetch (instead of `DynamicFetcher.fetch`) 
- **`stealthy_fetch(url, **kwargs)`** - Stealthy browser fetch (instead of `StealthyFetcher.fetch`)

The most commonly used classes are automatically available without any import, including `Fetcher`, `AsyncFetcher`, `DynamicFetcher`, `StealthyFetcher`, and `Selector`.

### Smart Page Management

The shell automatically tracks your requests and pages:

- **Current Page Access**

    The `page` and `response` commands are automatically updated with the last fetched page:
    
    ```python
    >>> get('https://quotes.toscrape.com')
    >>> # 'page' and 'response' both refer to the last fetched page
    >>> page.url
    'https://quotes.toscrape.com'
    >>> response.status  # Same as page.status
    200
    ```

- **Page History**

    The `pages` command keeps track of the last five pages (it's a `Selectors` object):
    
    ```python
    >>> get('https://site1.com')
    >>> get('https://site2.com') 
    >>> get('https://site3.com')
    
    >>> # Access last 5 pages
    >>> len(pages)  # `Selectors` object with `page` history
    3
    >>> pages[0].url  # First page in history
    'https://site1.com'
    >>> pages[-1].url  # Most recent page
    'https://site3.com'
    
    >>> # Work with historical pages
    >>> for i, old_page in enumerate(pages):
    ...     print(f"Page {i}: {old_page.url} - {old_page.status}")
    ```

## Additional helpful commands

### Page Visualization

View scraped pages in your browser:

```python
>>> get('https://quotes.toscrape.com')
>>> view(page)  # Opens the page HTML in your default browser
```

### Curl Command Integration

The shell provides a few functions to help you convert curl commands from the browser DevTools to `Fetcher` requests, which are `uncurl` and `curl2fetcher`. First, you need to copy a request as a curl command like the following:

<img src="../../assets/scrapling_shell_curl.png" title="Copying a request as a curl command from Chrome" alt="Copying a request as a curl command from Chrome" style="width: 70%;"/>

- **Convert Curl command to Request Object**

    ```python
    >>> curl_cmd = '''curl 'https://scrapling.requestcatcher.com/post' \
    ...   -X POST \
    ...   -H 'Content-Type: application/json' \
    ...   -d '{"name": "test", "value": 123}' '''
    
    >>> request = uncurl(curl_cmd)
    >>> request.method
    'post'
    >>> request.url
    'https://scrapling.requestcatcher.com/post'
    >>> request.headers
    {'Content-Type': 'application/json'}
    ```

- **Execute Curl Command Directly**

    ```python
    >>> # Convert and execute in one step
    >>> curl2fetcher(curl_cmd)
    >>> page.status
    200
    >>> page.json()['json']
    {'name': 'test', 'value': 123}
    ```

### IPython Features

The shell inherits all IPython capabilities:

```python
>>> # Magic commands
>>> %time page = get('https://example.com')  # Time execution
>>> %history  # Show command history
>>> %save filename.py 1-10  # Save commands 1-10 to file

>>> # Tab completion works everywhere
>>> page.c<TAB>  # Shows: css, css_first, cookies, etc.
>>> Fetcher.<TAB>  # Shows all Fetcher methods

>>> # Object inspection
>>> get? # Show get documentation
```

## Examples

Here are a few examples generated via AI:

#### E-commerce Data Collection

```python
>>> # Start with product listing page
>>> catalog = get('https://shop.example.com/products')

>>> # Find product links
>>> product_links = catalog.css('.product-link::attr(href)')
>>> print(f"Found {len(product_links)} products")

>>> # Sample a few products first
>>> for link in product_links[:3]:
...     product = get(f"https://shop.example.com{link}")
...     name = product.css('.product-name::text').get('')
...     price = product.css('.price::text').get('')
...     print(f"{name}: {price}")

>>> # Scale up with sessions for efficiency
>>> from scrapling.fetchers import FetcherSession
>>> with FetcherSession() as session:
...     products = []
...     for link in product_links:
...         product = session.get(f"https://shop.example.com{link}")
...         products.append({
...             'name': product.css('.product-name::text').get(''),
...             'price': product.css('.price::text').get(''),
...             'url': link
...         })
```

#### API Integration and Testing

```python
>>> # Test API endpoints interactively
>>> response = get('https://jsonplaceholder.typicode.com/posts/1')
>>> response.json()
{'userId': 1, 'id': 1, 'title': 'sunt aut...', 'body': 'quia et...'}

>>> # Test POST requests
>>> new_post = post('https://jsonplaceholder.typicode.com/posts', 
...                 json={'title': 'Test Post', 'body': 'Test content', 'userId': 1})
>>> new_post.json()['id']
101

>>> # Test with different data
>>> updated = put(f'https://jsonplaceholder.typicode.com/posts/{new_post.json()["id"]}',
...               json={'title': 'Updated Title'})
```

## Getting Help

If you need help other than what is available in-terminal, you can:

- [Scrapling Documentation](https://scrapling.readthedocs.io/)
- [Discord Community](https://discord.gg/EMgGbDceNQ)
- [GitHub Issues](https://github.com/D4Vinci/Scrapling/issues)  

And that's it! Happy scraping! The shell makes web scraping as easy as a conversation.