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Madhu 2025-02-13 22:11:05 +05:30
parent 5fb0f11a12
commit c645234165
1 changed files with 7 additions and 14 deletions
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21
ai_agent_tutorials/ai_real_estate_agent/ai_real_estate_agent.py
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@ -60,7 +60,7 @@ class PropertyFindingAgent:
f"https://www.squareyards.com/sale/property-for-sale-in-{formatted_location}/*", f"https://www.squareyards.com/sale/property-for-sale-in-{formatted_location}/*",
f"https://www.99acres.com/property-in-{formatted_location}-ffid/*", f"https://www.99acres.com/property-in-{formatted_location}-ffid/*",
f"https://housing.com/in/buy/{formatted_location}/{formatted_location}", f"https://housing.com/in/buy/{formatted_location}/{formatted_location}",
f"https://www.nobroker.in/property/sale/{city}/{formatted_location}", # f"https://www.nobroker.in/property/sale/{city}/{formatted_location}",
] ]
property_type_prompt = "Flats" if property_type == "Flat" else "Individual Houses" property_type_prompt = "Flats" if property_type == "Flat" else "Individual Houses"
@ -92,24 +92,15 @@ class PropertyFindingAgent:
print("Processed Properties:", properties) print("Processed Properties:", properties)
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
])
price_trends = self.get_location_trends(city)
analysis = self.agent.run( analysis = self.agent.run(
f"""As a real estate expert, analyze these properties and market trends: f"""As a real estate expert, analyze these properties and market trends:
Properties Found: Properties Found in json format:
{properties_context} {properties}
Location Price Trends:
{price_trends}
Please provide: Please provide:
1. A summary of available properties 1. Bullet Point Information for each property that are available based on the user's requirements.
2. Best value properties based on current market rates 2. Best value properties based on current market rates
3. Location-specific advantages and price trends 3. Location-specific advantages and price trends
4. Specific recommendations based on the {property_category} {property_type} requirement 4. Specific recommendations based on the {property_category} {property_type} requirement
@ -285,6 +276,8 @@ def main():
st.success("✅ Property search completed!") st.success("✅ Property search completed!")
st.subheader("🏘️ Property Recommendations") st.subheader("🏘️ Property Recommendations")
with st.expander("🏘️ Property Recommendations"):
st.write(property)
st.markdown(property_results) st.markdown(property_results)
st.divider() st.divider()
@ -294,7 +287,7 @@ def main():
st.success("✅ Location analysis completed!") st.success("✅ Location analysis completed!")
with st.expander("📈 Location Trends Analysis"): with st.expander("📈 Location Trends Analysis of the city"):
st.markdown(location_trends) st.markdown(location_trends)
except Exception as e: except Exception as e: