NEW PROJ: AI Real Estate Agent

ai_agent_tutorials/ai_real_estate_agent/ai_real_estate_agent.py

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+from typing import Dict, List
+from pydantic import BaseModel, Field
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+from firecrawl import FirecrawlApp
+
+class PropertyData(BaseModel):
+    """Schema for property data extraction"""
+    building_name: str = Field(description="Name of the building/property", alias="Building_name")
+    property_type: str = Field(description="Type of property (commercial, residential, etc)", alias="Property_type")
+    location_address: str = Field(description="Complete address of the property")
+    price: str = Field(description="Price of the property", alias="Price")
+    description: str = Field(description="Detailed description of the property", alias="Description")
+
+class MarketNewsData(BaseModel):
+    """Schema for market news extraction"""
+    title: str = Field(description="Title of the article/news")
+    content: str = Field(description="Main content of the article")
+    date: str = Field(description="Publication date")
+    source: str = Field(description="Source of the article")
+
+class FirecrawlResponse(BaseModel):
+    """Schema for Firecrawl API response"""
+    success: bool
+    data: Dict
+    status: str
+    expiresAt: str
+
+class PriceFindingAgent:
+    """Agent responsible for finding properties and providing recommendations"""
+    
+    def __init__(self, firecrawl_api_key: str):
+        self.agent = Agent(
+            model=OpenAIChat(id="o3-mini"),
+            markdown=True,
+            description="I am a real estate expert who helps find and analyze properties based on user preferences."
+        )
+        self.firecrawl = FirecrawlApp(api_key=firecrawl_api_key)
+
+    def find_properties(
+        self, 
+        city: str,
+        max_price: float,
+        property_category: str = "Residential",
+        property_type: str = "Flat"
+    ) -> str:
+        """Find and analyze properties based on user preferences"""
+        formatted_location = city.lower()
+        
+        # First, extract properties using Firecrawl
+        urls = [
+            f"https://www.99acres.com/property-in-{formatted_location}-ffid/*",
+            f"https://housing.com/in/buy/{formatted_location}/{formatted_location}",
+            f"https://www.squareyards.com/sale/property-for-sale-in-{formatted_location}/*",
+            f"https://www.nobroker.in/*",
+            f"https://www.nobroker.in/property/sale/{city}/{formatted_location}",
+            f"https://www.magicbricks.com/*"
+        ]
+        
+        property_type_prompt = "Flats" if property_type == "Flat" else "Individual Houses"
+        
+        raw_response = self.firecrawl.extract(
+            urls=urls,
+            params={
+                'prompt': f"""Extract {property_category} {property_type_prompt} from {city} that cost less than {max_price} crores.
+                
+                Requirements:
+                - Property Category: {property_category} properties only
+                - Property Type: {property_type_prompt} only
+                - Location: {city}
+                - Maximum Price: {max_price} crores
+                - Include complete property details with exact location
+                """,
+                'schema': PropertyData.model_json_schema()
+            }
+        )
+        
+        # Process the properties data
+        properties = []
+        if isinstance(raw_response, dict):
+            response = FirecrawlResponse(**raw_response)
+            properties = [response.data]
+        elif isinstance(raw_response, list):
+            responses = [FirecrawlResponse(**item) for item in raw_response]
+            properties = [resp.data for resp in responses]
+
+        # Now use the agent to analyze and provide recommendations
+        properties_context = "\n".join([
+            f"Property: {p['Building_name']}\nPrice: {p['Price']}\nLocation: {p['location_address']}\nType: {p['Property_type']}\nDescription: {p['Description']}"
+            for p in properties
+        ])
+        
+        analysis = self.agent.run(
+            f"""As a real estate expert, analyze these properties and provide detailed recommendations:
+
+            Properties Found:
+            {properties_context}
+
+            Please provide:
+            1. A summary of available properties
+            2. Best value properties and why
+            3. Location-specific advantages
+            4. Price comparison with market rates
+            5. Specific recommendations based on the {property_category} {property_type} requirement
+            6. Any red flags or concerns to consider
+            7. Negotiation tips for the best properties
+
+            Format your response in a clear, structured way that helps the user make an informed decision.
+            """
+        )
+        
+        return analysis
+
+class MarketAnalysisAgent:
+    """Agent responsible for analyzing market trends and conditions"""
+    
+    def __init__(self, firecrawl_api_key: str):
+        self.agent = Agent(
+            model=OpenAIChat(id="o3-mini"),
+            markdown=True,
+            description="I am a real estate market analyst who provides insights on market trends and conditions."
+        )
+        self.firecrawl = FirecrawlApp(api_key=firecrawl_api_key)
+
+    def analyze_market(self, city: str) -> str:
+        """Analyze market conditions using news and market reports"""
+        # Extract market information from news and analysis sites
+        urls = [
+            "https://www.moneycontrol.com/real-estate-property/*",
+            "https://economictimes.indiatimes.com/wealth/real-estate/*",
+            "https://housing.com/news/*",
+            f"https://www.99acres.com/articles/real-estate-market-{city.lower()}*"
+        ]
+        
+        raw_response = self.firecrawl.extract(
+            urls=urls,
+            params={
+                'prompt': f"""Extract recent real estate market information and trends for {city}.
+                Focus on:
+                - Market trends
+                - Price movements
+                - Development projects
+                - Infrastructure updates
+                - Investment potential
+                Only extract articles from the last 6 months.
+                """,
+                'schema': MarketNewsData.model_json_schema()
+            }
+        )
+        
+        # Process the market data
+        market_data = []
+        if isinstance(raw_response, dict):
+            response = FirecrawlResponse(**raw_response)
+            market_data = [response.data]
+        elif isinstance(raw_response, list):
+            responses = [FirecrawlResponse(**item) for item in raw_response]
+            market_data = [resp.data for resp in responses]
+
+        # Analyze the market data
+        market_context = "\n".join([
+            f"Title: {article['title']}\nDate: {article['date']}\nContent: {article['content']}\nSource: {article['source']}"
+            for article in market_data
+        ])
+        
+        analysis = self.agent.run(
+            f"""As a real estate market analyst, provide a comprehensive market analysis for {city}:
+
+            Market Data:
+            {market_context}
+
+            Please provide:
+            1. Current market overview
+            2. Price trends and predictions
+            3. Development and infrastructure updates
+            4. Investment opportunities and risks
+            5. Regulatory changes affecting the market
+            6. Future outlook
+
+            Format your response as a detailed market report with clear sections and actionable insights.
+            """
+        )
+        
+        return analysis
+
+def main():
+    """Main function to demonstrate the agents"""
+    firecrawl_api_key = "YOUR_FIRECRAWL_API_KEY"
+    
+    try:
+        # Initialize agents
+        property_agent = PriceFindingAgent(firecrawl_api_key)
+        market_agent = MarketAnalysisAgent(firecrawl_api_key)
+        
+        # Get property recommendations
+        print("=== Property Analysis ===")
+        property_analysis = property_agent.find_properties(
+            city="Hyderabad",
+            max_price=5.0,
+            property_category="Residential",
+            property_type="Flat"
+        )
+        print(property_analysis)
+        
+        # Get market analysis
+        print("\n=== Market Analysis ===")
+        market_analysis = market_agent.analyze_market("Hyderabad")
+        print(market_analysis)
+            
+    except Exception as e:
+        print(f"An error occurred: {str(e)}")
+
+if __name__ == "__main__":
+    main()