addition of market analyst
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Madhu
parent
b42b879761
commit
a211286993
2 changed files with 101 additions and 46 deletions
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@ -12,6 +12,21 @@ class PropertyData(BaseModel):
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price: str = Field(description="Price of the property", alias="Price")
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description: str = Field(description="Detailed description of the property", alias="Description")
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class PropertiesResponse(BaseModel):
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"""Schema for multiple properties response"""
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properties: List[PropertyData] = Field(description="List of property details")
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class LocationData(BaseModel):
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"""Schema for location price trends"""
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location: str
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price_per_sqft: float
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percent_increase: float
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rental_yield: float
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class LocationsResponse(BaseModel):
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"""Schema for multiple locations response"""
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locations: List[LocationData] = Field(description="List of location data points")
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class MarketNewsData(BaseModel):
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"""Schema for market news extraction"""
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title: str = Field(description="Title of the article/news")
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@ -49,9 +64,9 @@ class PropertyFindingAgent:
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# First, extract properties using Firecrawl
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urls = [
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f"https://www.squareyards.com/sale/property-for-sale-in-{formatted_location}/*",
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f"https://www.99acres.com/property-in-{formatted_location}-ffid/*",
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f"https://housing.com/in/buy/{formatted_location}/{formatted_location}",
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f"https://www.squareyards.com/sale/property-for-sale-in-{formatted_location}/*",
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f"https://www.nobroker.in/property/sale/{city}/{formatted_location}",
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f"https://www.magicbricks.com/*"
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]
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@ -61,7 +76,7 @@ class PropertyFindingAgent:
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raw_response = self.firecrawl.extract(
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urls=urls,
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params={
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'prompt': f"""Extract at least 2-3 different {property_category} {property_type_prompt} from {city} that cost less than {max_price} crores.
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'prompt': f"""Extract at least 3-6 different {property_category} {property_type_prompt} from {city} that cost less than {max_price} crores.
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Requirements:
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- Property Category: {property_category} properties only
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@ -69,38 +84,47 @@ class PropertyFindingAgent:
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- Location: {city}
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- Maximum Price: {max_price} crores
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- Include complete property details with exact location
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- IMPORTANT: Return at least 2 different property listings
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- IMPORTANT: Return data for at least 3 different properties
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- Format as a list of properties with their respective details
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""",
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'schema': PropertyData.model_json_schema()
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} )
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print(raw_response)
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'schema': PropertiesResponse.model_json_schema()
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}
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)
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print("Raw Property Response:", raw_response)
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# Process the properties data
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properties = []
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if isinstance(raw_response, dict):
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response = FirecrawlResponse(**raw_response)
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properties = [response.data]
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elif isinstance(raw_response, list):
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responses = [FirecrawlResponse(**item) for item in raw_response]
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properties = [resp.data for resp in responses]
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print(properties)
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if isinstance(raw_response, dict) and raw_response.get('success'):
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properties = raw_response['data'].get('properties', [])
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else:
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properties = []
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print("Processed Properties:", properties)
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# Now use the agent to analyze and provide recommendations
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properties_context = "\n".join([
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f"Property: {p['Building_name']}\nPrice: {p['Price']}\nLocation: {p['location_address']}\nType: {p['Property_type']}\nDescription: {p['Description']}"
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for p in properties
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])
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# Get location price trends
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price_trends = self.get_location_trends(city)
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analysis = self.agent.run(
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f"""As a real estate expert, analyze these properties and provide detailed recommendations:
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f"""As a real estate expert, analyze these properties and market trends:
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Properties Found:
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{properties_context}
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Location Price Trends:
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{price_trends}
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Please provide:
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1. A summary of available properties
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2. Best value properties and why
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3. Location-specific advantages
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4. Price comparison with market rates
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5. Specific recommendations based on the {property_category} {property_type} requirement
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2. Best value properties based on current market rates
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3. Location-specific advantages and price trends
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4. Specific recommendations based on the {property_category} {property_type} requirement
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5. Investment potential based on price trends
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6. Any red flags or concerns to consider
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7. Negotiation tips for the best properties
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@ -110,6 +134,63 @@ class PropertyFindingAgent:
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return analysis
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def get_location_trends(self, city: str) -> str:
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"""Get price trends for different localities in the city"""
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raw_response = self.firecrawl.extract([
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f"https://www.99acres.com/property-rates-and-price-trends-in-{city.lower()}-prffid/*"
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], {
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'prompt': """Extract price trends data for ALL major localities in the city.
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IMPORTANT:
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- Return data for at least 5-10 different localities
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- Include both premium and affordable areas
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- Do not skip any locality mentioned in the source
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- Format as a list of locations with their respective data
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""",
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'schema': LocationsResponse.model_json_schema(),
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})
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if isinstance(raw_response, dict) and raw_response.get('success'):
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locations = raw_response['data'].get('locations', [])
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# Use agent to analyze the trends
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analysis = self.agent.run(
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f"""As a real estate expert, analyze these location price trends for {city}:
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{locations}
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Please provide:
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1. A bullet-point summary of the price trends for each location
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2. Identify the top 3 locations with:
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- Highest price appreciation
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- Best rental yields
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- Best value for money
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3. Investment recommendations:
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- Best locations for long-term investment
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- Best locations for rental income
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- Areas showing emerging potential
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4. Specific advice for investors based on these trends
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Format the response as follows:
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📊 LOCATION TRENDS SUMMARY
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• [Bullet points for each location]
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🏆 TOP PERFORMING AREAS
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• [Bullet points for best areas]
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💡 INVESTMENT INSIGHTS
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• [Bullet points with investment advice]
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🎯 RECOMMENDATIONS
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• [Bullet points with specific recommendations]
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"""
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)
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return analysis.content
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return "No price trends data available"
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class MarketAnalysisAgent:
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"""Agent responsible for analyzing market trends and conditions"""
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@ -126,7 +207,7 @@ class MarketAnalysisAgent:
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# Extract market information from news and analysis sites
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urls = [
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"https://www.moneycontrol.com/real-estate-property/*",
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"https://economictimes.indiatimes.com/wealth/real-estate/*",
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f"https://www.99acres.com/property-rates-and-price-trends-in-{city.lower()}/*",
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"https://housing.com/news/*",
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f"https://www.99acres.com/articles/real-estate-market-{city.lower()}*"
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]
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@ -1,26 +0,0 @@
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from ai_real_estate_agent import PropertyFindingAgent
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def test_property_agent():
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# Initialize the agent with your Firecrawl and OpenAI API keys
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agent = PropertyFindingAgent(
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firecrawl_api_key="", # Replace with your Firecrawl key
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openai_api_key=""
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)
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try:
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# Test property search
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results = agent.find_properties(
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city="Visakhapatnam",
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max_price=4.0,
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property_category="Residential",
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property_type="Individual House"
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)
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print("\n=== Property Search Results ===")
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print(results.content)
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except Exception as e:
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print(f"Error during testing: {str(e)}")
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if __name__ == "__main__":
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test_property_agent()
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