Update pages/market_rent_estimation.py

pages/market_rent_estimation.py

@@ -18,12 +18,12 @@ def main():
     df_properties = pd.read_csv("atlanta_data_new.csv", encoding='utf-8')
     
     # user's filter for property size is a range in percentage around picked property 
-    min_size = (100 + st.session_state['min_property_lease_size_perc']) / 100 * st.session_state['building_sf']
-    max_size = (100 + st.session_state['max_property_lease_size_perc']) / 100 * st.session_state['building_sf']
+    min_size = (100 + st.session_state['min_property_size_perc']) / 100 * st.session_state['building_sf']
+    max_size = (100 + st.session_state['max_property_size_perc']) / 100 * st.session_state['building_sf']
 
     # # user's filter for lease size is a range in percentage around picked lease 
-    # lease_min_size = (100 + st.session_state['min_property_size_perc']) / 100 * st.session_state['rented_sf']
-    # lease_max_size = (100 + st.session_state['max_property_size_perc']) / 100 * st.session_state['rented_sf']
+    # lease_min_size = (100 + st.session_state['min_property_lease_size_perc']) / 100 * st.session_state['rented_sf']
+    # lease_max_size = (100 + st.session_state['max_property_lease_size_perc']) / 100 * st.session_state['rented_sf']
 
     #apply all filters 
     # (df_properties['rented_sf'] >= lease_min_size) & (df_properties['rented_sf'] <= lease_max_size) &