from pydanticimport BaseModel, Fieldclass ArticleData(BaseModel):    title:str = Field(description="The article title")    author:str = Field(description="The author's name")    publish_date:str = Field(description="Article publication date")    content:str = Field(description="Main article content")    category:str = Field(description="Article category")response= client.smartscraper(    website_url="https://example.com/blog/article",    user_prompt="Extract the article information",    output_schema=ArticleData)print(f"Title:{response.title}")print(f"Author:{response.author}")print(f"Published:{response.publish_date}")

from typingimport Listfrom pydanticimport BaseModel, Fieldclass Employee(BaseModel):    name:str = Field(description="Employee's full name")    position:str = Field(description="Job title")    department:str = Field(description="Department name")    email:str = Field(description="Email address")class Office(BaseModel):    location:str = Field(description="Office location/city")    address:str = Field(description="Full address")    phone:str = Field(description="Contact number")class CompanyData(BaseModel):    name:str = Field(description="Company name")    description:str = Field(description="Company description")    industry:str = Field(description="Industry sector")    founded_year:int = Field(description="Year company was founded")    employees: List[Employee]= Field(description="List of key employees")    offices: List[Office]= Field(description="Company office locations")    website:str = Field(description="Company website URL")# Extract comprehensive company informationresponse= client.smartscraper(    website_url="https://example.com/about",    user_prompt="Extract detailed company information including employees and offices",    output_schema=CompanyData)# Access nested dataprint(f"Company:{response.name}")print("\nKey Employees:")for employeein response.employees:    print(f"-{employee.name} ({employee.position})")print("\nOffice Locations:")for officein response.offices:    print(f"-{office.location}:{office.address}")

from scrapegraph_pyimport Clientfrom pydanticimport BaseModel, Fieldclass ProductInfo(BaseModel):    name:str = Field(description="Product name")    price:str = Field(description="Product price")    description:str = Field(description="Product description")    availability:str = Field(description="Product availability status")client= Client(api_key="your-api-key")# Enable enhanced JavaScript rendering for a React-based e-commerce siteresponse= client.smartscraper(    website_url="https://example-react-store.com/products/123",    user_prompt="Extract product details including name, price, description, and availability",    output_schema=ProductInfo,    render_heavy_js=True  # Enable for React/Vue/Angular sites)print(f"Product:{response['result']['name']}")print(f"Price:{response['result']['price']}")print(f"Available:{response['result']['availability']}")

from pydanticimport BaseModel, Fieldfrom typingimport Listclass ProductInfo(BaseModel):    name:str = Field(description="Product name")    description:str = Field(description="Product description")    price:str = Field(description="Product price")    features: List[str]= Field(description="List of key features")    availability:str = Field(description="Availability information")response= client.searchscraper(    user_prompt="Find information about iPhone 15 Pro",    output_schema=ProductInfo)print(f"Product:{response.name}")print(f"Price:{response.price}")print("\nFeatures:")for featurein response.features:    print(f"-{feature}")

from typingimport Listfrom pydanticimport BaseModel, Fieldclass MarketPlayer(BaseModel):    name:str = Field(description="Company name")    market_share:str = Field(description="Market share percentage")    key_products: List[str]= Field(description="Main products in market")    strengths: List[str]= Field(description="Company's market strengths")class MarketTrend(BaseModel):    name:str = Field(description="Trend name")    description:str = Field(description="Trend description")    impact:str = Field(description="Expected market impact")    timeframe:str = Field(description="Trend timeframe")class MarketAnalysis(BaseModel):    market_size:str = Field(description="Total market size")    growth_rate:str = Field(description="Annual growth rate")    key_players: List[MarketPlayer]= Field(description="Major market players")    trends: List[MarketTrend]= Field(description="Market trends")    challenges: List[str]= Field(description="Industry challenges")    opportunities: List[str]= Field(description="Market opportunities")# Perform comprehensive market researchresponse= client.searchscraper(    user_prompt="Analyze the current AI chip market landscape",    output_schema=MarketAnalysis)# Access structured market dataprint(f"Market Size:{response.market_size}")print(f"Growth Rate:{response.growth_rate}")print("\nKey Players:")for playerin response.key_players:    print(f"\n{player.name}")    print(f"Market Share:{player.market_share}")    print("Key Products:")    for productin player.key_products:        print(f"-{product}")print("\nMarket Trends:")for trendin response.trends:    print(f"\n{trend.name}")    print(f"Impact:{trend.impact}")    print(f"Timeframe:{trend.timeframe}")

from scrapegraph_pyimport Clientclient= Client(api_key="your-api-key")# Enable markdown mode for cost-effective content gatheringresponse= client.searchscraper(    user_prompt="Latest developments in artificial intelligence",    num_results=3,    extraction_mode=False  # Enable markdown mode (2 credits per page vs 10 credits))# Access the raw markdown contentmarkdown_content= response['markdown_content']reference_urls= response['reference_urls']print(f"Markdown content length:{len(markdown_content)} characters")print(f"Reference URLs:{len(reference_urls)}")# Process the markdown contentprint("Content preview:", markdown_content[:500]+ "...")# Save to file for analysiswith open('ai_research_content.md','w',encoding='utf-8')as f:    f.write(markdown_content)print("Content saved to ai_research_content.md")