Update app.py

app.py

@@ -3,12 +3,27 @@ import geopandas as gpd
 import pandas as pd
 import os
 import matplotlib.pyplot as plt
+from arcgis.features import FeatureLayer
 from shapely.geometry import shape
 from shapely.ops import unary_union
 #from datasets import load_dataset
 
 #ds = load_dataset('psalama/NYC_sensitive_sites', data_files=data_files)
 
+
+def get_gdf_from_feature_layer(url):
+    # Access the ArcGIS feature layer
+    feature_layer = FeatureLayer(url)
+    
+    # Use the query() method to get all features where 'Borough' is 'MN'
+    sdf = feature_layer.query(where="Borough='MN'", out_sr=4326, as_df=True)
+    
+    # Convert the SpatialDataFrame to a GeoDataFrame
+    gdf = gpd.GeoDataFrame(sdf, geometry=gpd.points_from_xy(sdf['SHAPE.x'], sdf['SHAPE.y']))
+    
+    return gdf
+
+
 def process_buildings(input_gdf, sensitive_sites_gdf, default_building_height_m, multiplier_factor):
     # List to store all intersected sensitive sites
     intersected_sites = []
@@ -136,8 +151,11 @@ def ss_intersect(geojson1, ss_geoselect, multiplier_factor, default_building_hei
     # Calculate the maximum extent
     extent = get_max_extent(input_gdf, buffers_gdf)
 
+    lots_url = "https://services5.arcgis.com/GfwWNkhOj9bNBqoJ/arcgis/rest/services/MAPPLUTO/FeatureServer/0" # Access MapPLUTO # Eventually should be a checkbox
+    lots_gdf = get_gdf_from_feature_layer(lots_url)
+
     # Create and save the plot
-    create_plot('output_image.png', extent, buffer_union_gdf, intersected_sites_gdf, input_gdf)
+    create_plot('output_image.png', extent, lots_gdf, buffer_union_gdf, intersected_sites_gdf, input_gdf)
             
     # Return the image
     return 'output_image.png', "building_buffers.geojson", "buffer_union.geojson", intersection_desc