Update app.py

app.py

@@ -1,27 +1,124 @@
 import gradio as gr
 import geopandas as gpd
 from shapely.geometry import shape
+#from datasets import load_dataset
 
-def predict(geojson1, geojson2, value):
+#ds = load_dataset('psalama/NYC_sensitive_sites', data_files=data_files)
+
+def process_buildings(input_gdf, sensitive_sites_gdf, default_building_height_m, multiplier_factor):
+    # List to store all intersected sensitive sites
+    intersected_sites = []
+    
+    # List to store all buffers
+    buffers = []
+    
+    # Iterate over each building in the input file
+    for idx, building in input_gdf.iterrows():
+        building_name = building.get('building_name', 'Unnamed building')
+        
+        # If the 'building_height' field exists and its value is not null or zero for this building,
+        # use it as the building height. Otherwise, use the default building height provided by the user.
+        if 'building_height' in building and pd.notnull(building['building_height']) and building['building_height'] != 0:
+            building_height_m = building['building_height'] * 0.3048
+        else:
+            building_height_m = default_building_height_m
+
+        buffer_distance_m = building_height_m * multiplier_factor
+
+        # Convert building's geometry to EPSG:3857 for accurate meter-based distance measurement
+        building_geometry = gpd.GeoSeries([building['geometry']], crs="EPSG:4326")
+        building_geometry_m = building_geometry.to_crs("EPSG:3857")
+
+        # Create a buffer around the building and convert it to a GeoDataFrame
+        building_buffer = building_geometry_m.buffer(buffer_distance_m)
+        building_buffer_gdf = gpd.GeoDataFrame(geometry=building_buffer, crs="EPSG:3857")
+        building_buffer_gdf = building_buffer_gdf.to_crs("EPSG:4326")
+
+        # Convert back to feet for storing and printing, rounding to the nearest foot
+        building_height_ft = round(building_height_m / 0.3048)
+        buffer_distance_ft = round(buffer_distance_m / 0.3048)
+
+        # Assign additional attributes
+        building_buffer_gdf['building_name'] = building_name
+        building_buffer_gdf['building_height'] = building_height_ft
+        building_buffer_gdf['buffer_distance'] = buffer_distance_ft
+
+        buffers.append(building_buffer_gdf)
+
+        # Check if the buffer intersects with any sensitive sites
+        intersects = gpd.overlay(building_buffer_gdf, sensitive_sites_gdf, how='intersection')
+
+        if not intersects.empty:
+            print(f"Building {idx} ({building_name}), height: {building_height_ft}, buffer distance: {buffer_distance_ft} is in the vicinity of a sensitive site.")
+            intersected_sites.append(intersects)
+        else:
+            print(f"Building {idx} ({building_name}), height: {building_height_ft}, buffer distance: {buffer_distance_ft} is not in the vicinity of any sensitive sites.")
+
+    return buffers, intersected_sites
+
+
+def ss_intersect(geojson1, ss_geoselect, multiplier_factor, default_building_height): #function should be renamed!
     # Read the GeoJSON files
-    gdf1 = gpd.read_file(geojson1.name)
-    gdf2 = gpd.read_file(geojson2.name)
+    input_gdf = gpd.read_file(geojson1.name)
+
+    # Check that CRS is EPSG:4326
+    if input_gdf.crs.to_epsg() != 4326 or sensitive_sites_gdf.crs.to_epsg() != 4326:
+        raise ValueError("Input GeoJSON files must be in CRS EPSG:4326")
+    
+    if ss_geoselect==0:
+        sensitive_sites_gdf = gpd.read_file("sensitive_sites/NYC_Parks_Properties.geojson")
+    else:
+        sensitive_sites_gdf = gpd.read_file("sensitive_sites/NYC_Parks_Zones.geojson")
+
+    default_building_height_m = default_building_height * 0.3048
+
+    buffers, intersected_sites = process_buildings(input_gdf, sensitive_sites_gdf, default_building_height_m, multiplier_factor)
 
-    # Use the model to make a prediction
-    #output_image = model.predict(gdf1, gdf2, value)
+    # Concatenate all buffer GeoDataFrames and save as a GeoJSON file
+    buffers_gdf = pd.concat(buffers, ignore_index=True)
+    buffers_gdf = buffers_gdf.to_crs("EPSG:4326")
+    buffers_gdf.to_file("building_buffers.geojson", driver='GeoJSON')
 
+    # Concatenate all intersected sensitive sites and save as a GeoJSON file
+    if intersected_sites:
+        intersected_sites_gdf = pd.concat(intersected_sites, ignore_index=True)
+        intersected_sites_gdf = intersected_sites_gdf.to_crs("EPSG:4326")
+        intersected_sites_gdf.to_file("intersected_sensitive_sites.geojson", driver='GeoJSON')
+    else:
+        print("No buildings are in the vicinity of any sensitive sites.")
+
+    # Perform the union operation if there is more than one buffer
+    if len(buffers) > 1:
+        # Perform a unary union on the geometry column of the GeoDataFrame
+        buffer_union = unary_union(buffers_gdf['geometry'])
+
+        # Create a new GeoDataFrame from the union result
+        buffer_union_gdf = gpd.GeoDataFrame(geometry=[buffer_union], crs="EPSG:4326")
+
+        # Save the union GeoDataFrame as a GeoJSON file
+        buffer_union_gdf.to_file("buffer_union.geojson", driver='GeoJSON')
+
+            
     # Return the image
-    return output_image
+    return output_image, building_vicinity
+
 
 iface = gr.Interface(
-    fn=predict, 
+    fn=ss_intersect, 
     inputs=[
-        gr.inputs.File(label="GeoJSON Input 1"), 
-        gr.inputs.File(label="GeoJSON Input 2"), #This should be optional
+        gr.inputs.File(label="Building Footprints GeoJSON"),
+        gr.Radio(["Parks Properties", "Park Zones"], label="Which Sensitive Sites?", info="From NYC DPR", type="index"),
+        #gr.inputs.File(label="Sensitive Sites GeoJSON"), #Replaced by radio button above
         gr.inputs.Slider(minimum=0.0, maximum=10.0, default=4.3, label="Building Height Multiplier"),
         gr.inputs.Number(default=200, label="Default Building Height"), #Can I make this optional?
     ], 
-    outputs=gr.outputs.Image(type="pil"),
+    outputs=[
+        gr.outputs.File(label="Intersecting Buildings"),
+        gr.outputs.Textbox(label="Building and Sensitive Site Vicinities"),
+    ]
+    examples=[
+        ["files/building4test.geojson", "Parks Properties", 4.3, 200],
+    ],
     title="Shadow Proximity",
     description="Upload proposed building footprints in a GeoJSON file and select a numeric value to get the building proximity prediction.",
 )