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

# Schema for product pricing data
class ProductPrice(BaseModel):
    name: str = Field(description="Name of the product")
    price: Decimal = Field(description="Current price")
    original_price: Optional[Decimal] = Field(description="Original price if on sale")
    currency: str = Field(description="Currency code (e.g., USD)")
    seller: str = Field(description="Seller/retailer name")
    availability: str = Field(description="Product availability status")
    updated_at: str = Field(description="Last update timestamp")

# Schema for price monitoring results
class PriceMonitorResult(BaseModel):
    products: List[ProductPrice] = Field(description="List of product prices")
    total_products: int = Field(description="Total number of products monitored")
    source_url: str = Field(description="URL of the monitored page")

client = Client()

# Monitor competitor prices
response = client.extract(
    url="https://competitor-store.com/category/products",
    prompt="Extract pricing information for all products including name, current price, original price if available, and availability status",
    output_schema=PriceMonitorResult
)

# Process and analyze the data
for product in response.products:
    if product.original_price and product.original_price > product.price:
        discount = ((product.original_price - product.price) / product.original_price) * 100
        print(f"Product: {product.name}")
        print(f"Current Price: {product.price} {product.currency}")
        print(f"Original Price: {product.original_price} {product.currency}")
        print(f"Discount: {discount:.1f}%")
        print(f"Availability: {product.availability}\n")

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

# Schema for market trend data
class TrendData(BaseModel):
    topic: str = Field(description="Trend topic or keyword")
    mentions: int = Field(description="Number of mentions")
    sentiment: float = Field(description="Sentiment score (-1 to 1)")
    sources: List[str] = Field(description="Source URLs")
    date: str = Field(description="Date of analysis")
    key_insights: Optional[List[str]] = Field(description="Key insights about the trend")

# Schema for trend analysis results
class TrendAnalysisResult(BaseModel):
    trends: List[TrendData] = Field(description="List of analyzed trends")
    total_sources: int = Field(description="Total number of sources analyzed")
    analysis_date: str = Field(description="Date of the analysis")

client = Client()

# Search and analyze market trends
response = client.search(
    query="Analyze market trends and sentiment in the electric vehicle industry. Focus on pricing trends, consumer preferences, and technological advancements.",
    num_results=10,  # Number of sources to analyze
    output_schema=TrendAnalysisResult
)

# Process and visualize trends
print(f"Analysis Date: {response.analysis_date}")
print(f"Sources Analyzed: {response.total_sources}\n")

for trend in response.trends:
    print(f"Topic: {trend.topic}")
    print(f"Mentions: {trend.mentions}")
    print(f"Sentiment: {trend.sentiment:+.2f}")
    if trend.key_insights:
        print("Key Insights:")
        for insight in trend.key_insights:
            print(f"- {insight}")
    print(f"Sources: {len(trend.sources)}\n")