app.py

import os
from datetime import datetime
import ee
import json
import geemap
import numpy as np
import geemap.foliumap as gee_folium
import leafmap.foliumap as leaf_folium
import streamlit as st
import pandas as pd
import geopandas as gpd
from shapely.ops import transform
from functools import reduce
import plotly.express as px
import branca.colormap as cm
import folium

st.set_page_config(layout="wide")
m = st.markdown(
    """
<style>
div.stButton > button:first-child {
    background-color: #006400;
    color:#ffffff;
}
</style>""",
    unsafe_allow_html=True,
)

# Logo
st.write(
    f"""
    <div style="display: flex; justify-content: space-between; align-items: center;">
        <img src="https://huggingface.co/spaces/SustainabilityLabIITGN/NDVI_PERG/resolve/main/Final_IITGN-Logo-symmetric-Color.png"  style="width: 10%; margin-right: auto;">
        <img src="https://huggingface.co/spaces/SustainabilityLabIITGN/NDVI_PERG/resolve/main/IFS.jpg" style="width: 10%; margin-left: auto;">
    </div>
    """,
    unsafe_allow_html=True,
)


# Title
# make title in center
st.markdown(
    f"""
    <h1 style="text-align: center;">Vrinda (वृन्दा): Interactive Vegetation Index Analyzer</h1>
    """,
    unsafe_allow_html=True,
)

############################################
# Hyperparameters
############################################
st.write("<h2><div style='text-align: center;'>User Inputs</div></h2>", unsafe_allow_html=True)

st.write("Select the vegetation indices to calculate:")
all_veg_indices = ["NDVI", "EVI", "EVI2"]
formulas = {
    "NDVI": r"$\frac{NIR - Red}{NIR + Red}$",
    "EVI": r"$G \times \frac{NIR - Red}{NIR + C1 \times Red - C2 \times Blue + L}$",
    "EVI2": r"$G \times \frac{NIR - Red}{NIR + L + C \times Red}$",
}
defaults = [True, False, False]
veg_indices = []
for veg_index, default in zip(all_veg_indices, defaults):
    if st.checkbox(f"{veg_index} = {formulas[veg_index]}", value=default):
        veg_indices.append(veg_index)

with st.expander("EVI/EVI2 Parameters"):
    st.write("Select the parameters for the EVI/EVI2 calculation (default is as per EVI's Wikipedia page)")
    cols = st.columns(5)
    evi_vars = {}
    for col, name, default in zip(cols, ["G", "C1", "C2", "L", "C"], [2.5, 6, 7.5, 1, 2.4]):
        value = col.number_input(f"{name}", value=default)
        evi_vars[name] = value


############################################
# Functions
############################################
def daterange_str_to_dates(daterange_str):
    start_date, end_date = daterange_str.split("-")
    start_date = pd.to_datetime(start_date)
    end_date = pd.to_datetime(end_date)
    return start_date, end_date


def daterange_dates_to_str(start_date, end_date):
    return f"{start_date.strftime('%Y/%m/%d')}-{end_date.strftime('%Y/%m/%d')}"


def daterange_str_to_year(daterange_str):
    start_date, _ = daterange_str.split("-")
    year = pd.to_datetime(start_date).year
    return year


def shape_3d_to_2d(shape):
    if shape.has_z:
        return transform(lambda x, y, z: (x, y), shape)
    else:
        return shape


def preprocess_gdf(gdf):
    gdf = gdf.to_crs(epsg=7761)  # epsg for Gujarat
    gdf["geometry"] = gdf["geometry"].apply(shape_3d_to_2d)
    gdf["geometry"] = gdf.buffer(0)  # Fixes some invalid geometries
    return gdf


def check_valid_geometry(geometry_gdf):
    geometry = geometry_gdf.geometry.item()
    if geometry.type != "Polygon":
        st.error(f"Selected geometry is of type '{geometry.type}'. Please provide a 'Polygon' geometry.")
        st.stop()


def add_geometry_to_maps(map_list, opacity=0.0):
    for m in map_list:
        m.add_gdf(
            buffer_geometry_gdf,
            layer_name="Geometry Buffer",
            style_function=lambda x: {"color": "red", "fillOpacity": opacity, "fillColor": "red"},
        )
        m.add_gdf(
            geometry_gdf,
            layer_name="Geometry",
            style_function=lambda x: {"color": "blue", "fillOpacity": opacity, "fillColor": "blue"},
        )


def get_dem_slope_maps(buffer_ee_geometry):
    # Create the map for DEM
    dem_map = gee_folium.Map(controls={'scale':'bottomleft'})
    dem_map.add_tile_layer(
        wayback_mapping[latest_date], name=f"Esri Wayback - {latest_date.replace('-', '/')}", attribution="Esri"
    )

    dem_layer = ee.Image("USGS/SRTMGL1_003")
    # Set the target resolution to 10 meters
    target_resolution = 10
    dem_layer = (
        dem_layer.resample("bilinear").reproject(crs="EPSG:4326", scale=target_resolution).clip(buffer_ee_geometry)
    )

    # Generate contour lines using elevation thresholds
    terrain = ee.Algorithms.Terrain(dem_layer)
    contour_interval = 1
    contours = (
        terrain.select("elevation").subtract(terrain.select("elevation").mod(contour_interval)).rename("contours")
    )

    # Calculate the minimum and maximum values
    stats = contours.reduceRegion(reducer=ee.Reducer.minMax(), scale=10, maxPixels=1e13)
    max_value = stats.get("contours_max").getInfo()
    min_value = stats.get("contours_min").getInfo()
    vis_params = {"min": min_value, "max": max_value, "palette": ["blue", "green", "yellow", "red"]}
    dem_map.addLayer(contours, vis_params, "Contours")
    # Create a colormap
    cmap = cm.LinearColormap(colors=vis_params["palette"], vmin=vis_params["min"], vmax=vis_params["max"])
    tick_size = int((max_value-min_value)/4)
    dem_map.add_legend(title="Elevation (m)",
                                legend_dict={'{}-{} m'.format(min_value, min_value+tick_size): '#0000FF',
                                             '{}-{} m'.format(min_value+tick_size, min_value+2*tick_size): '#00FF00',
                                             '{}-{} m'.format(min_value+2*tick_size, min_value+3*tick_size): '#FFFF00',
                                             '{}-{} m'.format(min_value+3*tick_size, max_value): '#FF0000'},
                                position='bottomright')

    # Create the map for Slope
    slope_map = gee_folium.Map(controls={'scale':'bottomleft'})
    slope_map.add_tile_layer(
        wayback_mapping[latest_date], name=f"Esri Wayback - {latest_date.replace('-', '/')}", attribution="Esri"
    )

    # Calculate slope from the DEM
    slope_layer = (
        ee.Terrain.slope(
            ee.Image("USGS/SRTMGL1_003").resample("bilinear").reproject(crs="EPSG:4326", scale=target_resolution)
        )
        .clip(buffer_ee_geometry)
        .rename("slope")
    )
    # Calculate the minimum and maximum values
    stats = slope_layer.reduceRegion(reducer=ee.Reducer.minMax(), scale=10, maxPixels=1e13)
    max_value = int(stats.get("slope_max").getInfo())
    min_value = int(stats.get("slope_min").getInfo())
    vis_params = {"min": min_value, "max": max_value, "palette": ["blue", "green", "yellow", "red"]}
    slope_map.addLayer(slope_layer, vis_params, "Slope Layer")
    # Create a colormap
    colormap = cm.LinearColormap(colors=vis_params["palette"], vmin=vis_params["min"], vmax=vis_params["max"])
    tick_size=int((max_value-min_value)/4)
    slope_map.add_legend(title="Slope (degrees)",
                                legend_dict={'{}-{} deg'.format(min_value, min_value+tick_size): '#0000FF',
                                             '{}-{} deg'.format(min_value+tick_size, min_value+2*tick_size): '#00FF00',
                                             '{}-{} deg'.format(min_value+2*tick_size, min_value+3*tick_size): '#FFFF00',
                                             '{}-{} deg'.format(min_value+3*tick_size, max_value): '#FF0000'},
                                position='bottomright')
    return dem_map, slope_map


def add_indices(image, nir_band, red_band, blue_band):
    # Add negative cloud
    neg_cloud = image.select("MSK_CLDPRB").multiply(-1).rename("Neg_MSK_CLDPRB")
    nir = image.select(nir_band).divide(10000)
    red = image.select(red_band).divide(10000)
    blue = image.select(blue_band).divide(10000)
    numerator = nir.subtract(red)
    ndvi = (numerator).divide(nir.add(red)).rename("NDVI").clamp(-1, 1)
    # EVI formula taken from: https://en.wikipedia.org/wiki/Enhanced_vegetation_index

    denominator = nir.add(red.multiply(evi_vars["C1"])).subtract(blue.multiply(evi_vars["C2"])).add(evi_vars["L"])
    evi = numerator.divide(denominator).multiply(evi_vars["G"]).rename("EVI").clamp(-1, 1)
    evi2 = (
        numerator.divide(nir.add(evi_vars["L"]).add(red.multiply(evi_vars["C"])))
        .multiply(evi_vars["G"])
        .rename("EVI2")
        .clamp(-1, 1)
    )
    return image.addBands([neg_cloud, ndvi, evi, evi2])


def process_date(daterange, satellite, veg_indices):
    start_date, end_date = daterange
    daterange_str = daterange_dates_to_str(start_date, end_date)
    prefix = f"Processing {satellite} - {daterange_str}"
    try:
        attrs = satellites[satellite]
        collection = attrs["collection"]
        collection = collection.filterBounds(buffer_ee_geometry)
        collection = collection.filterDate(start_date, end_date)

        bucket = {}
        for veg_index in veg_indices:
            mosaic_veg_index = collection.qualityMosaic(veg_index)
            fc = geemap.zonal_stats(
                mosaic_veg_index, ee_feature_collection, scale=attrs["scale"], return_fc=True
            ).getInfo()
            mean_veg_index = fc["features"][0]["properties"][veg_index]
            bucket[veg_index] = mean_veg_index
            fc = geemap.zonal_stats(
                mosaic_veg_index, buffer_ee_feature_collection, scale=attrs["scale"], return_fc=True
            ).getInfo()
            buffer_mean_veg_index = fc["features"][0]["properties"][veg_index]
            bucket[f"{veg_index}_buffer"] = buffer_mean_veg_index
            bucket[f"{veg_index}_ratio"] = mean_veg_index / buffer_mean_veg_index
            bucket[f"mosaic_{veg_index}"] = mosaic_veg_index

        # Get median mosaic
        bucket["mosaic_visual_max_ndvi"] = collection.qualityMosaic("NDVI")
        bucket["mosaic_visual_median"] = collection.median()
        bucket["image_visual_least_cloud"] = collection.sort("CLOUDY_PIXEL_PERCENTAGE").first()

        if satellite == "COPERNICUS/S2_SR_HARMONIZED":
            cloud_mask_probability = fc["features"][0]["properties"]["MSK_CLDPRB"] / 100
        else:
            cloud_mask_probability = None
        bucket["Cloud (0 to 1)"] = cloud_mask_probability
        result_df.loc[daterange_str, list(bucket.keys())] = list(bucket.values())
        count = collection.size().getInfo()
        suffix = f" - Processed {count} images"
        write_info(f"{prefix}{suffix}")
    except Exception as e:
        print(e)
        suffix = f" - Imagery not available"
        write_info(f"{prefix}{suffix}")


def write_info(info):
    st.write(f"<span style='color:#006400;'>{info}</span>", unsafe_allow_html=True)


############################################
# One time setup
############################################


def one_time_setup():
    credentials_path = os.path.expanduser("~/.config/earthengine/credentials")
    if os.path.exists(credentials_path):
        pass  # Earth Engine credentials already exist
    elif "EE" in os.environ:  # write the credentials to the file
        ee_credentials = os.environ.get("EE")
        os.makedirs(os.path.dirname(credentials_path), exist_ok=True)
        with open(credentials_path, "w") as f:
            f.write(ee_credentials)
    else:
        raise ValueError(
            f"Earth Engine credentials not found at {credentials_path} or in the environment variable 'EE'"
        )

    ee.Initialize()

    satellites = {
        "COPERNICUS/S2_SR_HARMONIZED": {
            "scale": 10,
            "collection": ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
            .select(
                ["B2", "B4", "B8", "MSK_CLDPRB", "TCI_R", "TCI_G", "TCI_B"],
                ["Blue", "Red", "NIR", "MSK_CLDPRB", "R", "G", "B"],
            )
            .map(lambda image: add_indices(image, nir_band="NIR", red_band="Red", blue_band="Blue")),
        },
    }
    st.session_state.satellites = satellites
    with open("wayback_imagery.json") as f:
        st.session_state.wayback_mapping = json.load(f)


if "one_time_setup_done" not in st.session_state:
    one_time_setup()
    st.session_state.one_time_setup_done = True

satellites = st.session_state.satellites
wayback_mapping = st.session_state.wayback_mapping

############################################
# App
############################################

# Input: Satellite Sources
st.write("Select the satellite sources:")
satellite_selected = {}
for satellite in satellites:
    satellite_selected[satellite] = st.checkbox(satellite, value=True, disabled=True)

# Date range input
max_year = datetime.now().year
jan_1 = pd.to_datetime(f"{max_year}/01/01", format="%Y/%m/%d")
dec_31 = pd.to_datetime(f"{max_year}/12/31", format="%Y/%m/%d")
nov_15 = pd.to_datetime(f"{max_year}/11/15", format="%Y/%m/%d")
dec_15 = pd.to_datetime(f"{max_year}/12/15", format="%Y/%m/%d")
input_daterange = st.date_input(
    "Date Range (Ignore year. App will compute indices for all possible years)", (nov_15, dec_15), jan_1, dec_31
)
min_year = int(st.number_input("Minimum Year", value=2019, min_value=2015, step=1))
max_year = int(st.number_input("Maximum Year", value=max_year, min_value=2015, step=1))

# Input: GeoJSON/KML file
file_url = st.query_params.get("file_url", None)
if file_url is None:
    file_url = st.file_uploader("Upload KML/GeoJSON file", type=["geojson", "kml", "shp"])

if file_url is None:
    st.stop()

if isinstance(file_url, str):
    if file_url.startswith("https://drive.google.com/file/d/"):
        ID = file_url.replace("https://drive.google.com/file/d/", "").split("/")[0]
        file_url = f"https://drive.google.com/uc?id={ID}"
    elif file_url.startswith("https://drive.google.com/open?id="):
        ID = file_url.replace("https://drive.google.com/open?id=", "")
        file_url = f"https://drive.google.com/uc?id={ID}"

buffer = st.number_input("Buffer (m)", value=50, min_value=0, step=1)

input_gdf = preprocess_gdf(gpd.read_file(file_url))
if len(input_gdf) != 1:
    st.error(f"Expecting only one geometry per KML, but found {len(input_gdf)} geometries. Please fix the KML or provide another KML.")
    st.stop()
# print(input_gdf.is_valid)
# print(gpd.read_file(file_url).is_valid)
# print(input_gdf.iloc[0].geometry.__geo_interface__)

# Input: Geometry
def format_fn(x):
    return input_gdf.drop(columns=["geometry"]).loc[x].to_dict()


# input_geometry_idx = st.selectbox("Select the geometry", input_gdf.index, format_func=format_fn)
geometry_gdf = input_gdf[input_gdf.index == 0]
check_valid_geometry(geometry_gdf)

outer_geometry_gdf = geometry_gdf.copy()
outer_geometry_gdf["geometry"] = outer_geometry_gdf["geometry"].buffer(buffer)
buffer_geometry_gdf = (
    outer_geometry_gdf.difference(geometry_gdf).reset_index().drop(columns="index")
)  # reset index forces GeoSeries to GeoDataFrame
buffer_geometry_gdf["Name"] = "Buffer"

# Derived Inputs
ee_geometry = ee.Geometry(geometry_gdf.to_crs(4326).geometry.item().__geo_interface__)
ee_feature_collection = ee.FeatureCollection(ee_geometry)
buffer_ee_geometry = ee.Geometry(buffer_geometry_gdf.to_crs(4326).geometry.item().__geo_interface__)
buffer_ee_feature_collection = ee.FeatureCollection(buffer_ee_geometry)
outer_ee_geometry = ee.Geometry(outer_geometry_gdf.to_crs(4326).geometry.item().__geo_interface__)
outer_ee_feature_collection = ee.FeatureCollection(outer_ee_geometry)

# visualize the geometry
m = leaf_folium.Map()
keys = list(wayback_mapping.keys())
latest_date = sorted(keys, key=lambda x: pd.to_datetime(x))[-1]
m.add_tile_layer(
    wayback_mapping[latest_date], name=f"Esri Wayback - {latest_date.replace('-', '/')}", attribution="Esri"
)
# m.add_layer(buffer_ee_feature_collection)
add_geometry_to_maps([m], opacity=0.3)
write_info(
    f"""
<div style="text-align: center;">
    Latest Esri Imagery - {latest_date.replace('-', '/')}
</div>
"""
)
m.to_streamlit()

# Generate stats
stats_df = pd.DataFrame(
    {
        "Area (m^2)": geometry_gdf.area.item(),
        "Perimeter (m)": geometry_gdf.length.item(),
        "Points": json.loads(geometry_gdf.to_crs(4326).to_json())["features"][0]["geometry"]["coordinates"],
    }
)
st.write("<h3><div style='text-align: center;'>Geometry Metrics</div></h3>", unsafe_allow_html=True)
st.markdown(
    f"""| Metric | Value |
| --- | --- |
| Area (m^2) | {stats_df['Area (m^2)'].item():.2f} m^2 = {stats_df['Area (m^2)'].item()/10000:.2f} ha |
| Perimeter (m) | {stats_df['Perimeter (m)'].item():.2f} m |
| Points | {stats_df['Points'][0]} |
"""
)

stats_csv = stats_df.to_csv(index=False)
st.download_button("Download Geometry Metrics", stats_csv, "geometry_metrics.csv", "text/csv", use_container_width=True)

# Submit
submit = st.button("Calculate Vegetation Indices", use_container_width=True)

st.write("<h2><div style='text-align: center;'>Results</div></h2>", unsafe_allow_html=True)

if submit:
    if not any(satellite_selected.values()):
        st.error("Please select at least one satellite source")
        st.stop()

    # Create range
    start_day = input_daterange[0].day
    start_month = input_daterange[0].month
    end_day = input_daterange[1].day
    end_month = input_daterange[1].month

    dates = []
    for year in range(min_year, max_year + 1):
        start_date = pd.to_datetime(f"{year}-{start_month:02d}-{start_day:02d}")
        end_date = pd.to_datetime(f"{year}-{end_month:02d}-{end_day:02d}")
        dates.append((start_date, end_date))

    result_df = pd.DataFrame()
    for satellite, attrs in satellites.items():
        if not satellite_selected[satellite]:
            continue

        with st.spinner(f"Processing {satellite} ..."):
            progress_bar = st.progress(0)
            for i, daterange in enumerate(dates):
                process_date(daterange, satellite, veg_indices)
                progress_bar.progress((i + 1) / len(dates))

    st.session_state.result = result_df

print("Printing result...")
if "result" in st.session_state:
    result_df = st.session_state.result
    print(result_df.columns)

    # drop rows with all NaN values
    result_df = result_df.dropna(how="all")
    # drop columns with all NaN values
    result_df = result_df.dropna(axis=1, how="all")
    print(result_df.columns)
    print(result_df.head(2))

    # df.reset_index(inplace=True)
    # df.index = pd.to_datetime(df["index"], format="%Y-%m")
    for column in result_df.columns:
        result_df[column] = pd.to_numeric(result_df[column], errors="ignore")

    df_numeric = result_df.select_dtypes(include=["float64"])
    st.write(df_numeric)

    df_numeric_csv = df_numeric.to_csv(index=True)
    st.download_button(
        "Download Time Series Data", df_numeric_csv, "vegetation_indices.csv", "text/csv", use_container_width=True
    )

    df_numeric.index = [daterange_str_to_year(daterange) for daterange in df_numeric.index]
    for veg_index in veg_indices:
        fig = px.line(df_numeric, y=[veg_index, f"{veg_index}_buffer"], markers=True)
        fig.update_layout(xaxis=dict(tickvals=df_numeric.index, ticktext=df_numeric.index))
        st.plotly_chart(fig)

    st.write(
        "<h3><div style='text-align: center;'>Visual Comparison between Two Years</div></h3>", unsafe_allow_html=True
    )
    cols = st.columns(2)

    with cols[0]:
        year_1 = st.selectbox("Year 1", result_df.index, index=0, format_func=lambda x: daterange_str_to_year(x))
    with cols[1]:
        year_2 = st.selectbox(
            "Year 2", result_df.index, index=len(result_df.index) - 1, format_func=lambda x: daterange_str_to_year(x)
        )

    vis_params = {"min": 0, "max": 1, "palette": ["white", "green"]}  # Example visualisation for Sentinel-2

    # Create a colormap and name it as NDVI
    colormap = cm.LinearColormap(colors=vis_params["palette"], vmin=vis_params["min"], vmax=vis_params["max"])

    for veg_index in veg_indices:
        st.write(f"<h3><div style='text-align: center;'>{veg_index}</div></h3>", unsafe_allow_html=True)
        cols = st.columns(2)
        for col, daterange_str in zip(cols, [year_1, year_2]):
            mosaic = result_df.loc[daterange_str, f"mosaic_{veg_index}"]
            with col:
                m = gee_folium.Map()
                veg_index_layer = gee_folium.ee_tile_layer(mosaic, {"bands": [veg_index], "min": 0, "max": 1})

                if satellite == "COPERNICUS/S2_SR_HARMONIZED":
                    min_all = 0
                    max_all = 255
                else:
                    raise ValueError(f"Unknown satellite: {satellite}")

                if veg_index=='NDVI':
                    m.add_legend(title="NDVI Class/Value",
                                legend_dict={'<0:Waterbody': '#0000FF',
                                             '0-0.1: Open': '#FF0000',
                                             '0.1-0.2: Highly Degraded':'#FFFF00',
                                             '0.2-0.3: Degraded': '#FFA500',
                                             '0.3-0.4: Moderately Degraded': '#00FE00',
                                             '0.4-0.5: Dense': '#00A400',
                                             '>0.5: Very Dense': '#006D00',
                                            },
                                position='bottomright')
                
                m.add_layer(mosaic.select(veg_index), vis_params)
                # add colorbar
                # m.add_colorbar(colors=["#000000", "#00FF00"], vmin=0.0, vmax=1.0)
                add_geometry_to_maps([m])
                m.add_child(colormap)
                m.to_streamlit()

    for name, key in zip(
        ["RGB (Least Cloud Tile Crop)", "RGB (Max NDVI Mosaic)", "RGB (Median Mosaic)"],
        ["image_visual_least_cloud", "mosaic_visual_max_ndvi", "mosaic_visual_median"],
    ):
        st.write(f"<h3><div style='text-align: center;'>{name}</div></h3>", unsafe_allow_html=True)
        cols = st.columns(2)
        for col, daterange_str in zip(cols, [year_1, year_2]):
            start_date, end_date = daterange_str_to_dates(daterange_str)
            mid_date = start_date + (end_date - start_date) / 2
            esri_date = min(wayback_mapping.keys(), key=lambda x: abs(pd.to_datetime(x) - mid_date))
            with col:
                m = gee_folium.Map()
                visual_mosaic = result_df.loc[daterange_str, key]
                # visual_layer = gee_folium.ee_tile_layer(mosaic, {"bands": ["R", "G", "B"], "min": min_all, "max": max_all})

                m.add_layer(visual_mosaic.select(["R", "G", "B"]))
                add_geometry_to_maps([m])
                m.to_streamlit()

    st.write("<h3><div style='text-align: center;'>Esri RGB Imagery</div></h3>", unsafe_allow_html=True)
    cols = st.columns(2)
    for col, daterange_str in zip(cols, [year_1, year_2]):
        start_date, end_date = daterange_str_to_dates(daterange_str)
        mid_date = start_date + (end_date - start_date) / 2
        esri_date = min(wayback_mapping.keys(), key=lambda x: abs(pd.to_datetime(x) - mid_date))
        with col:
            m = leaf_folium.Map()
            m.add_tile_layer(wayback_mapping[esri_date], name=f"Esri Wayback Imagery - {esri_date}", attribution="Esri")
            add_geometry_to_maps([m])
            write_info(
                f"""
            <div style="text-align: center;">
                Esri Imagery - {esri_date.replace('-', '/')}
            </div>
            """
            )
            m.to_streamlit()

    st.write(
        "<h3><div style='text-align: center;'>DEM and Slope from SRTM at 30m resolution</div></h3>",
        unsafe_allow_html=True,
    )
    cols = st.columns(2)
    dem_map, slope_map = get_dem_slope_maps(ee.Geometry(geometry_gdf.to_crs(4326).geometry.item().__geo_interface__))
    for col, param_map, title in zip(cols, [dem_map, slope_map], ["DEM Map", "Slope Map"]):
        with col:
            param_map.add_gdf(
                geometry_gdf,
                layer_name="Geometry",
                style_function=lambda x: {"color": "blue", "fillOpacity": 0.0, "fillColor": "blue"},
            )
            write_info(f"""<div style="text-align: center;">{title}</div>""")
            param_map.addLayerControl()
            
            param_map.to_streamlit()