Update pages/market_rent_estimation.py

pages/market_rent_estimation.py

@@ -21,8 +21,8 @@ def main():
     st.set_page_config(initial_sidebar_state="collapsed", layout="wide")
     tab1, tab2 = st.tabs(["Comps", "Combined estimation"])#, "Market distributions", "Rexy"])
 
-    distances_matrix = np.load('Miami_matrix.npy')
-    df_properties = pd.read_csv("Miami_data_new.csv", encoding='utf-8')
+    distances_matrix = np.load('all_data_matrix.npy')
+    df_properties = pd.read_csv("all_data.csv", encoding='utf-8')
 
 
     # update the values of date and price of the historical lease to today date and nan value for price
@@ -40,7 +40,8 @@ def main():
 
     #apply all filters 
     # 
-    mask = (df_properties['rented_sf'] >= lease_min_size) & (df_properties['rented_sf'] <= lease_max_size) & (df_properties['building_sf'] >= min_size) & (df_properties['building_sf'] <= max_size) & (df_properties['months_since'] <= st.session_state['months_back'])
+    mask = (df_properties['rented_sf'] >= lease_min_size) & (df_properties['rented_sf'] <= lease_max_size) & (df_properties['building_sf'] >= min_size) & (df_properties['building_sf'] <= max_size) 
+        & (df_properties['months_since'] <= st.session_state['months_back']) & (df_properties['market_costar'] <= st.session_state['market_val'])
     df_properties_filtered = df_properties[mask]
     
     # create distances matrix for remaining rows
@@ -150,9 +151,9 @@ def main():
             
             #add poligons on map
             gdf = gpd.read_file('costar_sm_polygons.geojson')
-            gdf_Atlanta = gdf[gdf.full_submarket.str.contains("Miami")]
-            folium.GeoJson(data=gdf_Atlanta.geometry.to_json(), name='geojson').add_to(m)
-            for _, row in gdf_Atlanta.iterrows():
+            gdf_current_market = gdf[gdf.full_submarket.str.contains(st.session_state['market_val'])]
+            folium.GeoJson(data=gdf_current_market.geometry.to_json(), name='geojson').add_to(m)
+            for _, row in gdf_current_market.iterrows():
                 # Convert the row's geometry to GeoJSON
                 geojson = folium.GeoJson(data=row['geometry'].__geo_interface__,
                                             style_function=lambda feature: {