Pagination

from scrapegraph_py import Client
from pydantic import BaseModel
from typing import List

class Product(BaseModel):
    name: str
    price: str
    rating: str

class ProductList(BaseModel):
    products: List[Product]

client = Client(api_key="your-api-key")

# Basic pagination - scrape 3 pages
response = client.smartscraper(
    website_url="https://example-store.com/products",
    user_prompt="Extract all product information",
    output_schema=ProductList,
    total_pages=3
)

import { extract } from 'scrapegraph-js';

// Basic extraction
const result = await extract('your-api-key', {
  url: 'https://example-store.com/products',
  prompt: 'Extract all product information',
});

from scrapegraph_py import Client
from pydantic import BaseModel, Field
from typing import List, Optional

class Product(BaseModel):
    name: str = Field(description="Product name")
    price: str = Field(description="Product price")
    rating: Optional[str] = Field(description="Customer rating")
    image_url: Optional[str] = Field(description="Product image URL")
    availability: Optional[str] = Field(description="Product availability status")

class ProductCatalog(BaseModel):
    products: List[Product] = Field(description="List of products")

client = Client(api_key="your-api-key")

# Scrape 5 pages with scrolling and delays
response = client.smartscraper(
    website_url="https://amazon.com/s?k=laptops",
    user_prompt="Extract all laptop products with their details",
    output_schema=ProductCatalog,
    total_pages=5,
    number_of_scrolls=3,
    wait_for=2
)

class Article(BaseModel):
    title: str = Field(description="Article title")
    summary: str = Field(description="Article summary")
    author: str = Field(description="Author name")
    publish_date: str = Field(description="Publication date")
    url: str = Field(description="Article URL")

class NewsFeed(BaseModel):
    articles: List[Article] = Field(description="List of articles")

# Collect articles from multiple news pages
response = client.smartscraper(
    website_url="https://techcrunch.com/category/artificial-intelligence/",
    user_prompt="Extract all AI-related articles with their details",
    output_schema=NewsFeed,
    total_pages=4,
    number_of_scrolls=2
)

class JobListing(BaseModel):
    title: str = Field(description="Job title")
    company: str = Field(description="Company name")
    location: str = Field(description="Job location")
    salary: Optional[str] = Field(description="Salary information")
    requirements: List[str] = Field(description="Job requirements")

class JobBoard(BaseModel):
    jobs: List[JobListing] = Field(description="List of job listings")

# Gather job listings from multiple pages
response = client.smartscraper(
    website_url="https://linkedin.com/jobs/search?keywords=python",
    user_prompt="Extract all Python developer job listings",
    output_schema=JobBoard,
    total_pages=3,
    number_of_scrolls=5
)

# Traditional pagination (page=1, page=2, etc.)
response = client.smartscraper(
    website_url="https://example.com/products?page=1",
    user_prompt="Extract products from this page",
    total_pages=5
)

# Infinite scroll pagination
response = client.smartscraper(
    website_url="https://example.com/feed",
    user_prompt="Extract all posts from the feed",
    total_pages=1,  # Single page
    number_of_scrolls=10  # Multiple scrolls to load more content
)

# Hybrid: multiple pages with scrolling on each
response = client.smartscraper(
    website_url="https://example.com/category/electronics",
    user_prompt="Extract all electronic products",
    total_pages=3,  # 3 category pages
    number_of_scrolls=5  # 5 scrolls per page
)

# Start with 1-2 pages for testing
response = client.smartscraper(
    website_url="https://example.com",
    user_prompt="Extract basic information",
    total_pages=1  # Start small
)

# Then scale up based on results
response = client.smartscraper(
    website_url="https://example.com",
    user_prompt="Extract comprehensive data",
    total_pages=5  # Scale up
)

# Good: Specific about pagination context
user_prompt = """
Extract all product information from this page and any subsequent pages.
Include: name, price, rating, availability, and image URL.
Ensure you capture all products across multiple pages.
"""

# Better: Include pagination instructions
user_prompt = """
Extract product information from this e-commerce page.
For each product, get: name, price, rating, availability, image URL.
This is page 1 of a multi-page product listing.
Look for pagination controls and extract data from all visible pages.
"""

import time

# Implement delays between requests
for page in range(1, 6):
    response = client.smartscraper(
        website_url=f"https://example.com/products?page={page}",
        user_prompt="Extract products",
        total_pages=1,  # One page at a time
        wait_for=3  # Wait 3 seconds
    )
    
    # Additional delay between requests
    time.sleep(2)

import time
from scrapegraph_py.exceptions import APIError

def scrape_with_retry(client, url, prompt, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = client.smartscraper(
                website_url=url,
                user_prompt=prompt,
                total_pages=3
            )
            return response
        except APIError as e:
            if attempt < max_retries - 1:
                print(f"Attempt {attempt + 1} failed: {e}")
                time.sleep(2 ** attempt)  # Exponential backoff
            else:
                raise e

# Amazon product scraping
response = client.smartscraper(
    website_url="https://amazon.com/s?k=smartphones",
    user_prompt="""
    Extract all smartphone products from this search results page.
    For each product include: name, price, rating, reviews count, 
    availability, and prime eligibility.
    """,
    output_schema=ProductCatalog,
    total_pages=5,
    number_of_scrolls=3
)

# Twitter/X feed scraping
response = client.smartscraper(
    website_url="https://twitter.com/search?q=AI",
    user_prompt="""
    Extract all tweets from this search results page.
    For each tweet include: author, content, timestamp, 
    likes, retweets, and replies count.
    """,
    total_pages=1,
    number_of_scrolls=15  # More scrolls for social media
)

# News website scraping
response = client.smartscraper(
    website_url="https://reuters.com/technology",
    user_prompt="""
    Extract all technology news articles from this page.
    For each article include: headline, summary, author, 
    publication date, and category.
    """,
    output_schema=NewsFeed,
    total_pages=4
)

import asyncio
from scrapegraph_py import AsyncClient

async def scrape_multiple_sites():
    client = AsyncClient(api_key="your-api-key")
    
    urls = [
        "https://site1.com/products",
        "https://site2.com/products",
        "https://site3.com/products"
    ]
    
    tasks = []
    for url in urls:
        task = client.smartscraper(
            website_url=url,
            user_prompt="Extract products",
            total_pages=3
        )
        tasks.append(task)
    
    results = await asyncio.gather(*tasks)
    return results

def process_in_batches(urls, batch_size=3):
    results = []
    
    for i in range(0, len(urls), batch_size):
        batch = urls[i:i + batch_size]
        
        # Process batch
        batch_results = []
        for url in batch:
            response = client.smartscraper(
                website_url=url,
                user_prompt="Extract data",
                total_pages=2
            )
            batch_results.append(response)
        
        results.extend(batch_results)
        
        # Delay between batches
        time.sleep(5)
    
    return results