app.py

import gradio as gr
import geopandas as gpd
import pandas as pd
import os
from shapely.ops import unary_union
#from datasets import load_dataset

#ds = load_dataset('psalama/NYC_sensitive_sites', data_files=data_files)

# Import functions from modules in data.py and plot.py
from data import get_gdf_from_feature_layer, process_buildings, get_max_extent
from plot import create_plot


def ss_intersect(geojson1, ss_geoselect, multiplier_factor, default_building_height): 
    # Read the GeoJSON files
    input_gdf = gpd.read_file(geojson1.name)

    # Check that CRS is EPSG:4326
    if input_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, intersection_desc = process_buildings(input_gdf, sensitive_sites_gdf, default_building_height_m, multiplier_factor)

    # 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")
    else: #if there aren't any intersections, return an empty geojson
        intersected_sites_gdf = gpd.read_file("files/No_intersecting_buildings.geojson")
        print("No buildings are in the vicinity of any sensitive sites.")
    
    intersected_sites_gdf.to_file("intersected_sensitive_sites.geojson", driver='GeoJSON')

    # 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')


    # 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 - which is the output image
    create_plot('output_image.png', extent, lots_gdf, sensitive_sites_gdf, buffer_union_gdf, intersected_sites_gdf, input_gdf)
            
    # Return the image, geojson files, and text description
    return 'output_image.png', "building_buffers.geojson", "buffer_union.geojson", intersection_desc


iface = gr.Interface(
    fn=ss_intersect, 
    inputs=[
        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.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", label="Result Image"),
        gr.outputs.File(label="Building Buffers"),
        gr.outputs.File(label="Union of Building Buffers"),
        gr.outputs.Textbox(label="Building intersection descriptions"),
    ],
    examples=[
        ["files/building4test.geojson", "Parks Properties", 4.3, 200],
        ["files/building4test.geojson", "Park Zones", 4.3, 900],
    ],
    title="Shadow Proximity",
    description="Upload proposed building footprints in a GeoJSON file and select a numeric value to get the building proximity prediction.",
)

iface.launch()