Fix GitHub vulnerability

Procfile

@@ -1 +0,0 @@
-web: sh setup.sh && streamlit run Home.py

README.md

@@ -1,11 +1,11 @@
 ---
-title: Timelapse
+title: Streamlit
 emoji: 🔥
 colorFrom: indigo
 colorTo: green
 sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
+sdk_version: 1.34.0
+app_file: Home.py
 pinned: false
 license: mit
 ---

app-bk.py

@@ -1,49 +0,0 @@
-import streamlit as st
-from multiapp import MultiApp
-from apps import (
-    basemaps,
-    census,
-    cesium,
-    deck,
-    device_loc,
-    gee,
-    gee_datasets,
-    heatmap,
-    home,
-    housing,
-    # hurricane,
-    plotly_maps,
-    raster,
-    timelapse,
-    vector,
-    wms,
-    xy,
-)
-
-st.set_page_config(layout="wide")
-
-
-apps = MultiApp()
-
-# Add all your application here
-
-apps.add_app("Home", home.app)
-apps.add_app("Create Timelapse", timelapse.app)
-# apps.add_app("Hurricane Mapping", hurricane.app)
-apps.add_app("U.S. Real Estate Data", housing.app)
-apps.add_app("U.S. Census Data", census.app)
-apps.add_app("Visualize Raster Data", raster.app)
-apps.add_app("Visualize Vector Data", vector.app)
-apps.add_app("Search Basemaps", basemaps.app)
-apps.add_app("Pydeck Gallery", deck.app)
-apps.add_app("Heatmaps", heatmap.app)
-apps.add_app("Add Points from XY", xy.app)
-apps.add_app("Add Web Map Service (WMS)", wms.app)
-apps.add_app("Google Earth Engine (GEE)", gee.app)
-apps.add_app("Awesome GEE Community Datasets", gee_datasets.app)
-apps.add_app("Geolocation", device_loc.app)
-apps.add_app("Cesium 3D Map", cesium.app)
-apps.add_app("Plotly", plotly_maps.app)
-
-# The main app
-apps.run()

app.css

@@ -1,4 +0,0 @@
-.flex
-{
-    overflow:auto;
-}

app.py

@@ -1,48 +0,0 @@
-import streamlit as st
-import leafmap.foliumap as leafmap
-
-st.set_page_config(layout="wide")
-
-st.sidebar.info(
-    """
-    - Web App URL: <https://streamlit.gishub.org>
-    - GitHub repository: <https://github.com/giswqs/streamlit-geospatial>
-    """
-)
-
-st.sidebar.title("Contact")
-st.sidebar.info(
-    """
-    Qiusheng Wu at [wetlands.io](https://wetlands.io) | [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
-    """
-)
-
-st.title("Streamlit for Geospatial Applications")
-
-st.markdown(
-    """
-    This multi-page web app demonstrates various interactive web apps created using [streamlit](https://streamlit.io) and open-source mapping libraries, 
-    such as [leafmap](https://leafmap.org), [geemap](https://geemap.org), [pydeck](https://deckgl.readthedocs.io), and [kepler.gl](https://docs.kepler.gl/docs/keplergl-jupyter).
-    This is an open-source project and you are very welcome to contribute your comments, questions, resources, and apps as [issues](https://github.com/giswqs/streamlit-geospatial/issues) or 
-    [pull requests](https://github.com/giswqs/streamlit-geospatial/pulls) to the [GitHub repository](https://github.com/giswqs/streamlit-geospatial).
-
-    """
-)
-
-st.info("Click on the left sidebar menu to navigate to the different apps.")
-
-st.subheader("Timelapse of Satellite Imagery")
-st.markdown(
-    """
-    The following timelapse animations were created using the Timelapse web app. Click `Timelapse` on the left sidebar menu to create your own timelapse for any location around the globe.
-"""
-)
-
-row1_col1, row1_col2 = st.columns(2)
-with row1_col1:
-    st.image("https://github.com/giswqs/data/raw/main/timelapse/spain.gif")
-    st.image("https://github.com/giswqs/data/raw/main/timelapse/las_vegas.gif")
-
-with row1_col2:
-    st.image("https://github.com/giswqs/data/raw/main/timelapse/goes.gif")
-    st.image("https://github.com/giswqs/data/raw/main/timelapse/fire.gif")

apps/basemaps.py

@@ -1,44 +0,0 @@
-import streamlit as st
-import leafmap.foliumap as leafmap
-
-
-def app():
-    st.title("Searching Basemaps")
-    st.markdown(
-        """
-    This app is a demonstration of searching and loading basemaps from [xyzservices](https://github.com/geopandas/xyzservices) and [Quick Map Services (QMS)](https://github.com/nextgis/quickmapservices). Selecting from 1000+ basemaps with a few clicks.  
-    """
-    )
-
-    with st.expander("See demo"):
-        st.image("https://i.imgur.com/0SkUhZh.gif")
-
-    row1_col1, row1_col2 = st.columns([3, 1])
-    width = 800
-    height = 600
-    tiles = None
-
-    with row1_col2:
-
-        checkbox = st.checkbox("Search Quick Map Services (QMS)")
-        keyword = st.text_input("Enter a keyword to search and press Enter:")
-        empty = st.empty()
-
-        if keyword:
-            options = leafmap.search_xyz_services(keyword=keyword)
-            if checkbox:
-                qms = leafmap.search_qms(keyword=keyword)
-                if qms is not None:
-                    options = options + qms
-
-            tiles = empty.multiselect(
-                "Select XYZ tiles to add to the map:", options)
-
-        with row1_col1:
-            m = leafmap.Map()
-
-            if tiles is not None:
-                for tile in tiles:
-                    m.add_xyz_service(tile)
-
-            m.to_streamlit(width, height)

apps/census.py

@@ -1,35 +0,0 @@
-import streamlit as st
-import leafmap.foliumap as leafmap
-
-
-def app():
-    st.title("Using U.S. Census Data")
-    st.markdown(
-        """
-    This app is a demonstration of using the [U.S. Census Bureau](https://www.census.gov/) TIGERweb Web Map Service (WMS). A complete list of WMS layers can be found [here](https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_wms.html). 
-    """
-    )
-
-    if "first_index" not in st.session_state:
-        st.session_state["first_index"] = 60
-    else:
-        st.session_state["first_index"] = 0
-
-    row1_col1, row1_col2 = st.columns([3, 1])
-    width = 800
-    height = 600
-
-    census_dict = leafmap.get_census_dict()
-    with row1_col2:
-
-        wms = st.selectbox("Select a WMS", list(census_dict.keys()), index=11)
-        layer = st.selectbox(
-            "Select a layer",
-            census_dict[wms]["layers"],
-            index=st.session_state["first_index"],
-        )
-
-        with row1_col1:
-            m = leafmap.Map()
-            m.add_census_data(wms, layer)
-            m.to_streamlit(width, height)

apps/cesium.py

@@ -1,8 +0,0 @@
-import leafmap
-import streamlit as st
-
-
-def app():
-    st.title("Cesium 3D Map")
-    html = "data/html/sfo_buildings.html"
-    leafmap.cesium_to_streamlit(html, height=800)

apps/deck.py

@@ -1,178 +0,0 @@
-import os
-import streamlit as st
-import pydeck as pdk
-import pandas as pd
-
-
-def globe_view():
-
-    """
-    GlobeView
-    =========
-
-    Over 33,000 power plants of the world plotted by their production capacity (given by height)
-    and fuel type (green if renewable) on an experimental deck.gl GlobeView.
-    """
-
-    COUNTRIES = "https://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_50m_admin_0_scale_rank.geojson"
-    POWER_PLANTS = "https://raw.githubusercontent.com/ajduberstein/geo_datasets/master/global_power_plant_database.csv"
-
-    df = pd.read_csv(POWER_PLANTS)
-
-    def is_green(fuel_type):
-        """Return a green RGB value if a facility uses a renewable fuel type"""
-        if fuel_type.lower() in (
-            "nuclear",
-            "water",
-            "wind",
-            "hydro",
-            "biomass",
-            "solar",
-            "geothermal",
-        ):
-            return [10, 230, 120]
-        return [230, 158, 10]
-
-    df["color"] = df["primary_fuel"].apply(is_green)
-
-    view_state = pdk.ViewState(latitude=51.47, longitude=0.45, zoom=2, min_zoom=2)
-
-    # Set height and width variables
-    view = pdk.View(type="_GlobeView", controller=True, width=1000, height=700)
-
-    layers = [
-        pdk.Layer(
-            "GeoJsonLayer",
-            id="base-map",
-            data=COUNTRIES,
-            stroked=False,
-            filled=True,
-            get_fill_color=[200, 200, 200],
-        ),
-        pdk.Layer(
-            "ColumnLayer",
-            id="power-plant",
-            data=df,
-            get_elevation="capacity_mw",
-            get_position=["longitude", "latitude"],
-            elevation_scale=100,
-            pickable=True,
-            auto_highlight=True,
-            radius=20000,
-            get_fill_color="color",
-        ),
-    ]
-
-    r = pdk.Deck(
-        views=[view],
-        initial_view_state=view_state,
-        tooltip={"text": "{name}, {primary_fuel} plant, {country}"},
-        layers=layers,
-        # Note that this must be set for the globe to be opaque
-        parameters={"cull": True},
-    )
-
-    return r
-
-
-def geojson_layer():
-
-    """
-    GeoJsonLayer
-    ===========
-
-    Property values in Vancouver, Canada, adapted from the deck.gl example pages. Input data is in a GeoJSON format.
-    """
-
-    DATA_URL = "https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/geojson/vancouver-blocks.json"
-    LAND_COVER = [
-        [[-123.0, 49.196], [-123.0, 49.324], [-123.306, 49.324], [-123.306, 49.196]]
-    ]
-
-    INITIAL_VIEW_STATE = pdk.ViewState(
-        latitude=49.254, longitude=-123.13, zoom=11, max_zoom=16, pitch=45, bearing=0
-    )
-
-    polygon = pdk.Layer(
-        "PolygonLayer",
-        LAND_COVER,
-        stroked=False,
-        # processes the data as a flat longitude-latitude pair
-        get_polygon="-",
-        get_fill_color=[0, 0, 0, 20],
-    )
-
-    geojson = pdk.Layer(
-        "GeoJsonLayer",
-        DATA_URL,
-        opacity=0.8,
-        stroked=False,
-        filled=True,
-        extruded=True,
-        wireframe=True,
-        get_elevation="properties.valuePerSqm / 20",
-        get_fill_color="[255, 255, properties.growth * 255]",
-        get_line_color=[255, 255, 255],
-    )
-
-    r = pdk.Deck(layers=[polygon, geojson], initial_view_state=INITIAL_VIEW_STATE)
-    return r
-
-
-def terrain():
-
-    """
-    TerrainLayer
-    ===========
-
-    Extruded terrain using AWS Open Data Terrain Tiles and Mapbox Satellite imagery
-    """
-
-    # Import Mapbox API Key from environment
-    MAPBOX_API_KEY = os.environ["MAPBOX_API_KEY"]
-
-    # AWS Open Data Terrain Tiles
-    TERRAIN_IMAGE = (
-        "https://s3.amazonaws.com/elevation-tiles-prod/terrarium/{z}/{x}/{y}.png"
-    )
-
-    # Define how to parse elevation tiles
-    ELEVATION_DECODER = {
-        "rScaler": 256,
-        "gScaler": 1,
-        "bScaler": 1 / 256,
-        "offset": -32768,
-    }
-
-    SURFACE_IMAGE = f"https://api.mapbox.com/v4/mapbox.satellite/{{z}}/{{x}}/{{y}}@2x.png?access_token={MAPBOX_API_KEY}"
-
-    terrain_layer = pdk.Layer(
-        "TerrainLayer",
-        elevation_decoder=ELEVATION_DECODER,
-        texture=SURFACE_IMAGE,
-        elevation_data=TERRAIN_IMAGE,
-    )
-
-    view_state = pdk.ViewState(
-        latitude=46.24, longitude=-122.18, zoom=11.5, bearing=140, pitch=60
-    )
-
-    r = pdk.Deck(terrain_layer, initial_view_state=view_state)
-    return r
-
-
-def app():
-
-    st.title("Pydeck Gallery")
-
-    options = ["GeoJsonLayer", "GlobeView", "TerrainLayer"]
-
-    option = st.selectbox("Select a pydeck layer type", options)
-
-    if option == "GeoJsonLayer":
-        st.header("Property values in Vancouver, Canada")
-        st.pydeck_chart(geojson_layer())
-    # elif option == "GlobeView":
-    #     st.pydeck_chart(globe_view())
-    elif option == "TerrainLayer":
-        st.pydeck_chart(terrain())

apps/device_loc.py

@@ -1,43 +0,0 @@
-import streamlit as st
-from bokeh.models.widgets import Button
-from bokeh.models import CustomJS
-from streamlit_bokeh_events import streamlit_bokeh_events
-import leafmap.foliumap as leafmap
-
-
-def app():
-
-    loc_button = Button(label="Get Device Location", max_width=150)
-    loc_button.js_on_event(
-        "button_click",
-        CustomJS(
-            code="""
-        navigator.geolocation.getCurrentPosition(
-            (loc) => {
-                document.dispatchEvent(new CustomEvent("GET_LOCATION", {detail: {lat: loc.coords.latitude, lon: loc.coords.longitude}}))
-            }
-        )
-        """
-        ),
-    )
-    result = streamlit_bokeh_events(
-        loc_button,
-        events="GET_LOCATION",
-        key="get_location",
-        refresh_on_update=False,
-        override_height=75,
-        debounce_time=0,
-    )
-
-    if result:
-        if "GET_LOCATION" in result:
-            loc = result.get("GET_LOCATION")
-            lat = loc.get("lat")
-            lon = loc.get("lon")
-            st.write(f"Lat, Lon: {lat}, {lon}")
-
-            m = leafmap.Map(center=(lat, lon), zoom=16)
-            m.add_basemap("ROADMAP")
-            popup = f"lat, lon: {lat}, {lon}"
-            m.add_marker(location=(lat, lon), popup=popup)
-            m.to_streamlit()

apps/gee.py

@@ -1,123 +0,0 @@
-import ee
-import streamlit as st
-import geemap.foliumap as geemap
-
-
-def nlcd():
-
-    st.header("National Land Cover Database (NLCD)")
-
-    row1_col1, row1_col2 = st.columns([3, 1])
-    width = 950
-    height = 600
-
-    Map = geemap.Map()
-
-    # Select the seven NLCD epoches after 2000.
-    years = ["2001", "2004", "2006", "2008", "2011", "2013", "2016"]
-
-    # Get an NLCD image by year.
-    def getNLCD(year):
-        # Import the NLCD collection.
-        dataset = ee.ImageCollection("USGS/NLCD_RELEASES/2016_REL")
-
-        # Filter the collection by year.
-        nlcd = dataset.filter(ee.Filter.eq("system:index", year)).first()
-
-        # Select the land cover band.
-        landcover = nlcd.select("landcover")
-        return landcover
-
-    with row1_col2:
-        selected_year = st.multiselect("Select a year", years)
-        add_legend = st.checkbox("Show legend")
-
-    if selected_year:
-        for year in selected_year:
-            Map.addLayer(getNLCD(year), {}, "NLCD " + year)
-
-        if add_legend:
-            Map.add_legend(
-                legend_title="NLCD Land Cover Classification", builtin_legend="NLCD"
-            )
-        with row1_col1:
-            Map.to_streamlit(width=width, height=height)
-
-    else:
-        with row1_col1:
-            Map.to_streamlit(width=width, height=height)
-
-
-def search_data():
-
-    st.header("Search Earth Engine Data Catalog")
-
-    Map = geemap.Map()
-
-    if "ee_assets" not in st.session_state:
-        st.session_state["ee_assets"] = None
-    if "asset_titles" not in st.session_state:
-        st.session_state["asset_titles"] = None
-
-    col1, col2 = st.columns([2, 1])
-
-    dataset = None
-    with col2:
-        keyword = st.text_input("Enter a keyword to search (e.g., elevation)", "")
-        if keyword:
-            ee_assets = geemap.search_ee_data(keyword)
-            asset_titles = [x["title"] for x in ee_assets]
-            dataset = st.selectbox("Select a dataset", asset_titles)
-            if len(ee_assets) > 0:
-                st.session_state["ee_assets"] = ee_assets
-                st.session_state["asset_titles"] = asset_titles
-
-            if dataset is not None:
-                with st.expander("Show dataset details", True):
-                    index = asset_titles.index(dataset)
-                    html = geemap.ee_data_html(st.session_state["ee_assets"][index])
-                    st.markdown(html, True)
-
-                ee_id = ee_assets[index]["ee_id_snippet"]
-                uid = ee_assets[index]["uid"]
-                st.markdown(f"""**Earth Engine Snippet:** `{ee_id}`""")
-
-                vis_params = st.text_input(
-                    "Enter visualization parameters as a dictionary", {}
-                )
-                layer_name = st.text_input("Enter a layer name", uid)
-                button = st.button("Add dataset to map")
-                if button:
-                    vis = {}
-                    try:
-                        if vis_params.strip() == "":
-                            # st.error("Please enter visualization parameters")
-                            vis_params = "{}"
-                        vis = eval(vis_params)
-                        if not isinstance(vis, dict):
-                            st.error("Visualization parameters must be a dictionary")
-                        try:
-                            Map.addLayer(eval(ee_id), vis, layer_name)
-                        except Exception as e:
-                            st.error(f"Error adding layer: {e}")
-                    except Exception as e:
-                        st.error(f"Invalid visualization parameters: {e}")
-
-            with col1:
-                Map.to_streamlit()
-        else:
-            with col1:
-                Map.to_streamlit()
-
-
-def app():
-    st.title("Google Earth Engine Applications")
-
-    apps = ["National Land Cover Database (NLCD)", "Search Earth Engine Data Catalog"]
-
-    selected_app = st.selectbox("Select an app", apps)
-
-    if selected_app == "National Land Cover Database (NLCD)":
-        nlcd()
-    elif selected_app == "Search Earth Engine Data Catalog":
-        search_data()

apps/gee_datasets.py

@@ -1,186 +0,0 @@
-import ee
-import streamlit as st
-import geemap.foliumap as geemap
-
-WIDTH = 1060
-HEIGHT = 600
-
-
-def function():
-    st.write("Not implemented yet.")
-    Map = geemap.Map()
-    Map.to_streamlit(WIDTH, HEIGHT)
-
-
-def lulc_mrb_floodplain():
-
-    Map = geemap.Map()
-
-    State_boundaries = ee.FeatureCollection('users/giswqs/MRB/State_Boundaries')
-    State_style = State_boundaries.style(
-        **{'color': '808080', 'width': 1, 'fillColor': '00000000'}
-    )
-
-    MRB_boundary = ee.FeatureCollection('users/giswqs/MRB/MRB_Boundary')
-    MRB_style = MRB_boundary.style(
-        **{'color': '000000', 'width': 2, 'fillColor': '00000000'}
-    )
-
-    floodplain = ee.Image('users/giswqs/MRB/USGS_Floodplain')
-
-    class_values = [34, 38, 46, 50, 62]
-    class_palette = ['c500ff', '00ffc5', '00a9e6', '73004d', '004d73']
-
-    img_1950 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1950')
-    img_1950 = img_1950.set('b1_class_values', class_values)
-    img_1950 = img_1950.set('b1_class_palette', class_palette)
-
-    img_1960 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1960')
-    img_1960 = img_1960.set('b1_class_values', class_values)
-    img_1960 = img_1960.set('b1_class_palette', class_palette)
-
-    img_1970 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1970')
-    img_1970 = img_1970.set('b1_class_values', class_values)
-    img_1970 = img_1970.set('b1_class_palette', class_palette)
-
-    img_1980 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1980')
-    img_1980 = img_1980.set('b1_class_values', class_values)
-    img_1980 = img_1980.set('b1_class_palette', class_palette)
-
-    img_1990 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1990')
-    img_1990 = img_1990.set('b1_class_values', class_values)
-    img_1990 = img_1990.set('b1_class_palette', class_palette)
-
-    img_2000 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_2000')
-    img_2000 = img_2000.set('b1_class_values', class_values)
-    img_2000 = img_2000.set('b1_class_palette', class_palette)
-
-    Map.addLayer(floodplain, {'palette': ['cccccc']}, 'Floodplain', True, 0.5)
-    Map.addLayer(img_2000, {}, 'Major Transitions 1941-2000')
-    Map.addLayer(img_1990, {}, 'Major Transitions 1941-1990')
-    Map.addLayer(img_1980, {}, 'Major Transitions 1941-1980')
-    Map.addLayer(img_1970, {}, 'Major Transitions 1941-1970')
-    Map.addLayer(img_1960, {}, 'Major Transitions 1941-1960')
-    Map.addLayer(img_1950, {}, 'Major Transitions 1941-1950')
-
-    Map.addLayer(State_style, {}, 'State Boundaries')
-    Map.addLayer(MRB_style, {}, 'MRB Boundary')
-
-    Map.to_streamlit(WIDTH, HEIGHT)
-
-
-def global_mangrove_watch():
-    """https://samapriya.github.io/awesome-gee-community-datasets/projects/mangrove/"""
-    Map = geemap.Map()
-    gmw2007 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2007_v2")
-    gmw2008 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2008_v2")
-    gmw2009 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2009_v2")
-    gmw2010 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2010_v2")
-    gmw2015 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2015_v2")
-    gmw2016 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2016_v2")
-    gmw1996 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_1996_v2")
-
-    Map.addLayer(
-        ee.Image().paint(gmw1996, 0, 3),
-        {"palette": ["228B22"]},
-        'Global Mangrove Watch 1996',
-    )
-    Map.addLayer(
-        ee.Image().paint(gmw2007, 0, 3),
-        {"palette": ["228B22"]},
-        'Global Mangrove Watch 2007',
-    )
-    Map.addLayer(
-        ee.Image().paint(gmw2008, 0, 3),
-        {"palette": ["228B22"]},
-        'Global Mangrove Watch 2008',
-    )
-    Map.addLayer(
-        ee.Image().paint(gmw2009, 0, 3),
-        {"palette": ["228B22"]},
-        'Global Mangrove Watch 2009',
-    )
-    Map.addLayer(
-        ee.Image().paint(gmw2010, 0, 3),
-        {"palette": ["228B22"]},
-        'Global Mangrove Watch 2010',
-    )
-    Map.addLayer(
-        ee.Image().paint(gmw2015, 0, 3),
-        {"palette": ["228B22"]},
-        'Global Mangrove Watch 2015',
-    )
-    Map.addLayer(
-        ee.Image().paint(gmw2016, 0, 3),
-        {"palette": ["228B22"]},
-        'Global Mangrove Watch 2015',
-    )
-
-    Map.to_streamlit(WIDTH, HEIGHT)
-
-
-def app():
-
-    st.title("Awesome GEE Community Datasets")
-
-    st.markdown(
-        """
-    
-    This app is for exploring the [Awesome GEE Community Datasets](https://samapriya.github.io/awesome-gee-community-datasets). Work in progress.
-    
-    """
-    )
-
-    datasets = {
-        "Population & Socioeconomic": {
-            "High Resolution Settlement Layer": "function()",
-            "World Settlement Footprint (2015)": "function()",
-            "Gridded Population of the World": "function()",
-            "geoBoundaries Global Database": "function()",
-            "West Africa Coastal Vulnerability Mapping": "function()",
-            "Relative Wealth Index (RWI)": "function()",
-            "Social Connectedness Index (SCI)": "function()",
-            "Native Land (Indigenous Land Maps)": "function()",
-        },
-        "Geophysical, Biological & Biogeochemical": {
-            "Geomorpho90m Geomorphometric Layers": "function()",
-        },
-        "Land Use and Land Cover": {
-            "Global Mangrove Watch": "global_mangrove_watch()",
-            "Mississippi River Basin Floodplain Land Use Change (1941-2000)": "lulc_mrb_floodplain()",
-        },
-        "Hydrology": {
-            "Global Shoreline Dataset": "function()",
-        },
-        "Agriculture, Vegetation and Forestry": {
-            "Landfire Mosaics LF v2.0.0": "function()",
-        },
-        "Global Utilities, Assets and Amenities Layers": {
-            "Global Power": "function()",
-        },
-        "EarthEnv Biodiversity ecosystems & climate Layers": {
-            "Global Consensus Landcover": "function()",
-        },
-        "Weather and Climate Layers": {
-            "Global Reference Evapotranspiration Layers": "function()",
-        },
-        "Global Events Layers": {
-            "Global Fire Atlas (2003-2016)": "function()",
-        },
-    }
-
-    row1_col1, row1_col2, _ = st.columns([1.2, 1.8, 1])
-
-    with row1_col1:
-        category = st.selectbox("Select a category", datasets.keys(), index=2)
-    with row1_col2:
-        dataset = st.selectbox("Select a dataset", datasets[category].keys())
-
-    Map = geemap.Map()
-
-    if dataset:
-        eval(datasets[category][dataset])
-
-    else:
-        Map = geemap.Map()
-        Map.to_streamlit(WIDTH, HEIGHT)

apps/heatmap.py

@@ -1,19 +0,0 @@
-import streamlit as st
-import leafmap.foliumap as leafmap
-
-
-def app():
-
-    st.title('Heatmaps')
-
-    filepath = "https://raw.githubusercontent.com/giswqs/leafmap/master/examples/data/us_cities.csv"
-    m = leafmap.Map(tiles="stamentoner")
-    m.add_heatmap(
-        filepath,
-        latitude="latitude",
-        longitude="longitude",
-        value="pop_max",
-        name="Heat map",
-        radius=20,
-    )
-    m.to_streamlit(width=700, height=500)

apps/home.py

@@ -1,34 +0,0 @@
-import streamlit as st
-import leafmap.foliumap as leafmap
-
-
-def app():
-    st.title("Streamlit for Geospatial Applications")
-
-    st.markdown(
-        """
-        This multi-page web app demonstrates various interactive web apps created using [streamlit](https://streamlit.io) and open-source mapping libraries, 
-        such as [leafmap](https://leafmap.org), [geemap](https://geemap.org), [pydeck](https://deckgl.readthedocs.io), and [kepler.gl](https://docs.kepler.gl/docs/keplergl-jupyter).
-        This is an open-source project and you are very welcome to contribute your comments, questions, resources, and apps as [issues](https://github.com/giswqs/streamlit-geospatial/issues) or 
-        [pull requests](https://github.com/giswqs/streamlit-geospatial/pulls) to the [GitHub repository](https://github.com/giswqs/streamlit-geospatial).
-
-        """
-    )
-
-    st.info("Click on the left sidebar menu to navigate to the different apps.")
-
-    st.subheader("Timelapse of Satellite Imagery")
-    st.markdown(
-        """
-        The following timelapse animations were created using the Timelapse web app. Click `Create Timelapse` on the left sidebar menu to create your own timelapse for any location around the globe.
-    """
-    )
-
-    row1_col1, row1_col2 = st.columns(2)
-    with row1_col1:
-        st.image("https://github.com/giswqs/data/raw/main/timelapse/spain.gif")
-        st.image("https://github.com/giswqs/data/raw/main/timelapse/las_vegas.gif")
-
-    with row1_col2:
-        st.image("https://github.com/giswqs/data/raw/main/timelapse/goes.gif")
-        st.image("https://github.com/giswqs/data/raw/main/timelapse/fire.gif")

apps/housing.py

@@ -1,457 +0,0 @@
-import datetime
-import os
-import pathlib
-import requests
-import zipfile
-import pandas as pd
-import pydeck as pdk
-import geopandas as gpd
-import streamlit as st
-import leafmap.colormaps as cm
-from leafmap.common import hex_to_rgb
-
-
-STREAMLIT_STATIC_PATH = pathlib.Path(st.__path__[0]) / "static"
-# We create a downloads directory within the streamlit static asset directory
-# and we write output files to it
-DOWNLOADS_PATH = STREAMLIT_STATIC_PATH / "downloads"
-if not DOWNLOADS_PATH.is_dir():
-    DOWNLOADS_PATH.mkdir()
-
-# Data source: https://www.realtor.com/research/data/
-# link_prefix = "https://econdata.s3-us-west-2.amazonaws.com/Reports/"
-link_prefix = "https://raw.githubusercontent.com/giswqs/data/main/housing/"
-
-data_links = {
-    "weekly": {
-        "national": link_prefix + "Core/listing_weekly_core_aggregate_by_country.csv",
-        "metro": link_prefix + "Core/listing_weekly_core_aggregate_by_metro.csv",
-    },
-    "monthly_current": {
-        "national": link_prefix + "Core/RDC_Inventory_Core_Metrics_Country.csv",
-        "state": link_prefix + "Core/RDC_Inventory_Core_Metrics_State.csv",
-        "metro": link_prefix + "Core/RDC_Inventory_Core_Metrics_Metro.csv",
-        "county": link_prefix + "Core/RDC_Inventory_Core_Metrics_County.csv",
-        "zip": link_prefix + "Core/RDC_Inventory_Core_Metrics_Zip.csv",
-    },
-    "monthly_historical": {
-        "national": link_prefix + "Core/RDC_Inventory_Core_Metrics_Country_History.csv",
-        "state": link_prefix + "Core/RDC_Inventory_Core_Metrics_State_History.csv",
-        "metro": link_prefix + "Core/RDC_Inventory_Core_Metrics_Metro_History.csv",
-        "county": link_prefix + "Core/RDC_Inventory_Core_Metrics_County_History.csv",
-        "zip": link_prefix + "Core/RDC_Inventory_Core_Metrics_Zip_History.csv",
-    },
-    "hotness": {
-        "metro": link_prefix
-        + "Hotness/RDC_Inventory_Hotness_Metrics_Metro_History.csv",
-        "county": link_prefix
-        + "Hotness/RDC_Inventory_Hotness_Metrics_County_History.csv",
-        "zip": link_prefix + "Hotness/RDC_Inventory_Hotness_Metrics_Zip_History.csv",
-    },
-}
-
-
-def get_data_columns(df, category, frequency="monthly"):
-    if frequency == "monthly":
-        if category.lower() == "county":
-            del_cols = ["month_date_yyyymm", "county_fips", "county_name"]
-        elif category.lower() == "state":
-            del_cols = ["month_date_yyyymm", "state", "state_id"]
-        elif category.lower() == "national":
-            del_cols = ["month_date_yyyymm", "country"]
-        elif category.lower() == "metro":
-            del_cols = ["month_date_yyyymm", "cbsa_code", "cbsa_title", "HouseholdRank"]
-        elif category.lower() == "zip":
-            del_cols = ["month_date_yyyymm", "postal_code", "zip_name", "flag"]
-    elif frequency == "weekly":
-        if category.lower() == "national":
-            del_cols = ["week_end_date", "geo_country"]
-        elif category.lower() == "metro":
-            del_cols = ["week_end_date", "cbsa_code", "cbsa_title", "hh_rank"]
-
-    cols = df.columns.values.tolist()
-
-    for col in cols:
-        if col.strip() in del_cols:
-            cols.remove(col)
-    if category.lower() == "metro":
-        return cols[2:]
-    else:
-        return cols[1:]
-
-
-@st.cache(allow_output_mutation=True)
-def get_inventory_data(url):
-    df = pd.read_csv(url)
-    url = url.lower()
-    if "county" in url:
-        df["county_fips"] = df["county_fips"].map(str)
-        df["county_fips"] = df["county_fips"].str.zfill(5)
-    elif "state" in url:
-        df["STUSPS"] = df["state_id"].str.upper()
-    elif "metro" in url:
-        df["cbsa_code"] = df["cbsa_code"].map(str)
-    elif "zip" in url:
-        df["postal_code"] = df["postal_code"].map(str)
-        df["postal_code"] = df["postal_code"].str.zfill(5)
-
-    if "listing_weekly_core_aggregate_by_country" in url:
-        columns = get_data_columns(df, "national", "weekly")
-        for column in columns:
-            if column != "median_days_on_market_by_day_yy":
-                df[column] = df[column].str.rstrip("%").astype(float) / 100
-    if "listing_weekly_core_aggregate_by_metro" in url:
-        columns = get_data_columns(df, "metro", "weekly")
-        for column in columns:
-            if column != "median_days_on_market_by_day_yy":
-                df[column] = df[column].str.rstrip("%").astype(float) / 100
-        df["cbsa_code"] = df["cbsa_code"].str[:5]
-    return df
-
-
-def filter_weekly_inventory(df, week):
-    df = df[df["week_end_date"] == week]
-    return df
-
-
-def get_start_end_year(df):
-    start_year = int(str(df["month_date_yyyymm"].min())[:4])
-    end_year = int(str(df["month_date_yyyymm"].max())[:4])
-    return start_year, end_year
-
-
-def get_periods(df):
-    return [str(d) for d in list(set(df["month_date_yyyymm"].tolist()))]
-
-
-@st.cache(allow_output_mutation=True)
-def get_geom_data(category):
-
-    prefix = (
-        "https://raw.githubusercontent.com/giswqs/streamlit-geospatial/master/data/"
-    )
-    links = {
-        "national": prefix + "us_nation.geojson",
-        "state": prefix + "us_states.geojson",
-        "county": prefix + "us_counties.geojson",
-        "metro": prefix + "us_metro_areas.geojson",
-        "zip": "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_zcta510_500k.zip",
-    }
-
-    if category.lower() == "zip":
-        r = requests.get(links[category])
-        out_zip = os.path.join(DOWNLOADS_PATH, "cb_2018_us_zcta510_500k.zip")
-        with open(out_zip, "wb") as code:
-            code.write(r.content)
-        zip_ref = zipfile.ZipFile(out_zip, "r")
-        zip_ref.extractall(DOWNLOADS_PATH)
-        gdf = gpd.read_file(out_zip.replace("zip", "shp"))
-    else:
-        gdf = gpd.read_file(links[category])
-    return gdf
-
-
-def join_attributes(gdf, df, category):
-
-    new_gdf = None
-    if category == "county":
-        new_gdf = gdf.merge(df, left_on="GEOID", right_on="county_fips", how="outer")
-    elif category == "state":
-        new_gdf = gdf.merge(df, left_on="STUSPS", right_on="STUSPS", how="outer")
-    elif category == "national":
-        if "geo_country" in df.columns.values.tolist():
-            df["country"] = None
-            df.loc[0, "country"] = "United States"
-        new_gdf = gdf.merge(df, left_on="NAME", right_on="country", how="outer")
-    elif category == "metro":
-        new_gdf = gdf.merge(df, left_on="CBSAFP", right_on="cbsa_code", how="outer")
-    elif category == "zip":
-        new_gdf = gdf.merge(df, left_on="GEOID10", right_on="postal_code", how="outer")
-    return new_gdf
-
-
-def select_non_null(gdf, col_name):
-    new_gdf = gdf[~gdf[col_name].isna()]
-    return new_gdf
-
-
-def select_null(gdf, col_name):
-    new_gdf = gdf[gdf[col_name].isna()]
-    return new_gdf
-
-
-def get_data_dict(name):
-    in_csv = os.path.join(os.getcwd(), "data/realtor_data_dict.csv")
-    df = pd.read_csv(in_csv)
-    label = list(df[df["Name"] == name]["Label"])[0]
-    desc = list(df[df["Name"] == name]["Description"])[0]
-    return label, desc
-
-
-def get_weeks(df):
-    seq = list(set(df[~df["week_end_date"].isnull()]["week_end_date"].tolist()))
-    weeks = [
-        datetime.date(int(d.split("/")[2]), int(d.split("/")[0]), int(d.split("/")[1]))
-        for d in seq
-    ]
-    weeks.sort()
-    return weeks
-
-
-def get_saturday(in_date):
-    idx = (in_date.weekday() + 1) % 7
-    sat = in_date + datetime.timedelta(6 - idx)
-    return sat
-
-
-def app():
-
-    st.title("U.S. Real Estate Data and Market Trends")
-    st.markdown(
-        """**Introduction:** This interactive dashboard is designed for visualizing U.S. real estate data and market trends at multiple levels (i.e., national,
-         state, county, and metro). The data sources include [Real Estate Data](https://www.realtor.com/research/data) from realtor.com and 
-         [Cartographic Boundary Files](https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html) from U.S. Census Bureau.
-         Several open-source packages are used to process the data and generate the visualizations, e.g., [streamlit](https://streamlit.io),
-          [geopandas](https://geopandas.org), [leafmap](https://leafmap.org), and [pydeck](https://deckgl.readthedocs.io).
-    """
-    )
-
-    with st.expander("See a demo"):
-        st.image("https://i.imgur.com/Z3dk6Tr.gif")
-
-    row1_col1, row1_col2, row1_col3, row1_col4, row1_col5 = st.columns(
-        [0.6, 0.8, 0.6, 1.4, 2]
-    )
-    with row1_col1:
-        frequency = st.selectbox("Monthly/weekly data", ["Monthly", "Weekly"])
-    with row1_col2:
-        types = ["Current month data", "Historical data"]
-        if frequency == "Weekly":
-            types.remove("Current month data")
-        cur_hist = st.selectbox(
-            "Current/historical data",
-            types,
-        )
-    with row1_col3:
-        if frequency == "Monthly":
-            scale = st.selectbox(
-                "Scale", ["National", "State", "Metro", "County"], index=3
-            )
-        else:
-            scale = st.selectbox("Scale", ["National", "Metro"], index=1)
-
-    gdf = get_geom_data(scale.lower())
-
-    if frequency == "Weekly":
-        inventory_df = get_inventory_data(data_links["weekly"][scale.lower()])
-        weeks = get_weeks(inventory_df)
-        with row1_col1:
-            selected_date = st.date_input("Select a date", value=weeks[-1])
-            saturday = get_saturday(selected_date)
-            selected_period = saturday.strftime("%-m/%-d/%Y")
-            if saturday not in weeks:
-                st.error(
-                    "The selected date is not available in the data. Please select a date between {} and {}".format(
-                        weeks[0], weeks[-1]
-                    )
-                )
-                selected_period = weeks[-1].strftime("%-m/%-d/%Y")
-        inventory_df = get_inventory_data(data_links["weekly"][scale.lower()])
-        inventory_df = filter_weekly_inventory(inventory_df, selected_period)
-
-    if frequency == "Monthly":
-        if cur_hist == "Current month data":
-            inventory_df = get_inventory_data(
-                data_links["monthly_current"][scale.lower()]
-            )
-            selected_period = get_periods(inventory_df)[0]
-        else:
-            with row1_col2:
-                inventory_df = get_inventory_data(
-                    data_links["monthly_historical"][scale.lower()]
-                )
-                start_year, end_year = get_start_end_year(inventory_df)
-                periods = get_periods(inventory_df)
-                with st.expander("Select year and month", True):
-                    selected_year = st.slider(
-                        "Year",
-                        start_year,
-                        end_year,
-                        value=start_year,
-                        step=1,
-                    )
-                    selected_month = st.slider(
-                        "Month",
-                        min_value=1,
-                        max_value=12,
-                        value=int(periods[0][-2:]),
-                        step=1,
-                    )
-                selected_period = str(selected_year) + str(selected_month).zfill(2)
-                if selected_period not in periods:
-                    st.error("Data not available for selected year and month")
-                    selected_period = periods[0]
-                inventory_df = inventory_df[
-                    inventory_df["month_date_yyyymm"] == int(selected_period)
-                ]
-
-    data_cols = get_data_columns(inventory_df, scale.lower(), frequency.lower())
-
-    with row1_col4:
-        selected_col = st.selectbox("Attribute", data_cols)
-    with row1_col5:
-        show_desc = st.checkbox("Show attribute description")
-        if show_desc:
-            try:
-                label, desc = get_data_dict(selected_col.strip())
-                markdown = f"""
-                **{label}**: {desc}
-                """
-                st.markdown(markdown)
-            except:
-                st.warning("No description available for selected attribute")
-
-    row2_col1, row2_col2, row2_col3, row2_col4, row2_col5, row2_col6 = st.columns(
-        [0.6, 0.68, 0.7, 0.7, 1.5, 0.8]
-    )
-
-    palettes = cm.list_colormaps()
-    with row2_col1:
-        palette = st.selectbox("Color palette", palettes, index=palettes.index("Blues"))
-    with row2_col2:
-        n_colors = st.slider("Number of colors", min_value=2, max_value=20, value=8)
-    with row2_col3:
-        show_nodata = st.checkbox("Show nodata areas", value=True)
-    with row2_col4:
-        show_3d = st.checkbox("Show 3D view", value=False)
-    with row2_col5:
-        if show_3d:
-            elev_scale = st.slider(
-                "Elevation scale", min_value=1, max_value=1000000, value=1, step=10
-            )
-            with row2_col6:
-                st.info("Press Ctrl and move the left mouse button.")
-        else:
-            elev_scale = 1
-
-    gdf = join_attributes(gdf, inventory_df, scale.lower())
-    gdf_null = select_null(gdf, selected_col)
-    gdf = select_non_null(gdf, selected_col)
-    gdf = gdf.sort_values(by=selected_col, ascending=True)
-
-    colors = cm.get_palette(palette, n_colors)
-    colors = [hex_to_rgb(c) for c in colors]
-
-    for i, ind in enumerate(gdf.index):
-        index = int(i / (len(gdf) / len(colors)))
-        if index >= len(colors):
-            index = len(colors) - 1
-        gdf.loc[ind, "R"] = colors[index][0]
-        gdf.loc[ind, "G"] = colors[index][1]
-        gdf.loc[ind, "B"] = colors[index][2]
-
-    initial_view_state = pdk.ViewState(
-        latitude=40, longitude=-100, zoom=3, max_zoom=16, pitch=0, bearing=0
-    )
-
-    min_value = gdf[selected_col].min()
-    max_value = gdf[selected_col].max()
-    color = "color"
-    # color_exp = f"[({selected_col}-{min_value})/({max_value}-{min_value})*255, 0, 0]"
-    color_exp = f"[R, G, B]"
-
-    geojson = pdk.Layer(
-        "GeoJsonLayer",
-        gdf,
-        pickable=True,
-        opacity=0.5,
-        stroked=True,
-        filled=True,
-        extruded=show_3d,
-        wireframe=True,
-        get_elevation=f"{selected_col}",
-        elevation_scale=elev_scale,
-        # get_fill_color="color",
-        get_fill_color=color_exp,
-        get_line_color=[0, 0, 0],
-        get_line_width=2,
-        line_width_min_pixels=1,
-    )
-
-    geojson_null = pdk.Layer(
-        "GeoJsonLayer",
-        gdf_null,
-        pickable=True,
-        opacity=0.2,
-        stroked=True,
-        filled=True,
-        extruded=False,
-        wireframe=True,
-        # get_elevation="properties.ALAND/100000",
-        # get_fill_color="color",
-        get_fill_color=[200, 200, 200],
-        get_line_color=[0, 0, 0],
-        get_line_width=2,
-        line_width_min_pixels=1,
-    )
-
-    # tooltip = {"text": "Name: {NAME}"}
-
-    # tooltip_value = f"<b>Value:</b> {median_listing_price}""
-    tooltip = {
-        "html": "<b>Name:</b> {NAME}<br><b>Value:</b> {"
-        + selected_col
-        + "}<br><b>Date:</b> "
-        + selected_period
-        + "",
-        "style": {"backgroundColor": "steelblue", "color": "white"},
-    }
-
-    layers = [geojson]
-    if show_nodata:
-        layers.append(geojson_null)
-
-    r = pdk.Deck(
-        layers=layers,
-        initial_view_state=initial_view_state,
-        map_style="light",
-        tooltip=tooltip,
-    )
-
-    row3_col1, row3_col2 = st.columns([6, 1])
-
-    with row3_col1:
-        st.pydeck_chart(r)
-    with row3_col2:
-        st.write(
-            cm.create_colormap(
-                palette,
-                label=selected_col.replace("_", " ").title(),
-                width=0.2,
-                height=3,
-                orientation="vertical",
-                vmin=min_value,
-                vmax=max_value,
-                font_size=10,
-            )
-        )
-    row4_col1, row4_col2, row4_col3 = st.columns([1, 2, 3])
-    with row4_col1:
-        show_data = st.checkbox("Show raw data")
-    with row4_col2:
-        show_cols = st.multiselect("Select columns", data_cols)
-    with row4_col3:
-        show_colormaps = st.checkbox("Preview all color palettes")
-        if show_colormaps:
-            st.write(cm.plot_colormaps(return_fig=True))
-    if show_data:
-        if scale == "National":
-            st.dataframe(gdf[["NAME", "GEOID"] + show_cols])
-        elif scale == "State":
-            st.dataframe(gdf[["NAME", "STUSPS"] + show_cols])
-        elif scale == "County":
-            st.dataframe(gdf[["NAME", "STATEFP", "COUNTYFP"] + show_cols])
-        elif scale == "Metro":
-            st.dataframe(gdf[["NAME", "CBSAFP"] + show_cols])
-        elif scale == "Zip":
-            st.dataframe(gdf[["GEOID10"] + show_cols])

apps/hurricane.py

@@ -1,52 +0,0 @@
-import streamlit as st
-import tropycal.tracks as tracks
-
-
-@st.cache(allow_output_mutation=True)
-def read_data(basin='north_atlantic', source='hurdat', include_btk=False):
-    return tracks.TrackDataset(basin=basin, source=source, include_btk=include_btk)
-
-
-def app():
-
-    st.title("Hurricane Mapping")
-
-    row1_col1, row1_col2 = st.columns([3, 1])
-
-    with row1_col1:
-        empty = st.empty()
-        empty.image("https://i.imgur.com/Ec7qsR0.png")
-
-    with row1_col2:
-
-        checkbox = st.checkbox("Select from a list of hurricanes", value=False)
-        if checkbox:
-            if st.session_state.get('hurricane') is None:
-                st.session_state['hurricane'] = read_data()
-
-            years = st.slider(
-                'Select a year', min_value=1950, max_value=2022, value=(2000, 2010)
-            )
-            storms = st.session_state['hurricane'].filter_storms(year_range=years)
-            selected = st.selectbox('Select a storm', storms)
-            storm = st.session_state['hurricane'].get_storm(selected)
-            ax = storm.plot()
-            fig = ax.get_figure()
-            empty.pyplot(fig)
-        else:
-
-            name = st.text_input("Or enter a storm Name", "michael")
-            if name:
-                if st.session_state.get('hurricane') is None:
-                    st.session_state['hurricane'] = read_data()
-                basin = st.session_state['hurricane']
-                years = basin.search_name(name)
-                if len(years) > 0:
-                    year = st.selectbox("Select a year", years)
-                    storm = basin.get_storm((name, year))
-                    ax = storm.plot()
-                    fig = ax.get_figure()
-                    empty.pyplot(fig)
-                else:
-                    empty.text("No storms found")
-                    st.write("No storms found")

apps/plotly_maps.py

@@ -1,17 +0,0 @@
-import streamlit as st
-import leafmap.plotlymap as leafmap
-
-
-def app():
-
-    st.title("Plotly Maps")
-    m = leafmap.Map(basemap="street", height=650)
-    m.add_mapbox_layer(style="streets")
-
-    basemaps = list(leafmap.basemaps.keys())
-    basemap = st.selectbox(
-        "Select a basemap", basemaps, basemaps.index("Stamen.Terrain")
-    )
-    m.add_basemap(basemap)
-
-    st.plotly_chart(m, use_container_width=True)

apps/raster.py

@@ -1,77 +0,0 @@
-import os
-import leafmap.foliumap as leafmap
-import streamlit as st
-import palettable
-
-
-@st.cache(allow_output_mutation=True)
-def load_cog_list():
-    print(os.getcwd())
-    in_txt = os.path.join(os.getcwd(), "data/cog_files.txt")
-    with open(in_txt) as f:
-        return [line.strip() for line in f.readlines()[1:]]
-
-
-@st.cache(allow_output_mutation=True)
-def get_palettes():
-    palettes = dir(palettable.matplotlib)[:-16]
-    return ["matplotlib." + p for p in palettes]
-
-
-def app():
-
-    st.title("Visualize Raster Datasets")
-    st.markdown(
-        """
-    An interactive web app for visualizing local raster datasets and Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org)). The app was built using [streamlit](https://streamlit.io), [leafmap](https://leafmap.org), and [localtileserver](https://github.com/banesullivan/localtileserver).
-
-
-    """
-    )
-
-    row1_col1, row1_col2 = st.columns([2, 1])
-
-    with row1_col1:
-        cog_list = load_cog_list()
-        cog = st.selectbox("Select a sample Cloud Opitmized GeoTIFF (COG)", cog_list)
-
-    with row1_col2:
-        empty = st.empty()
-
-        url = empty.text_input(
-            "Enter a HTTP URL to a Cloud Optimized GeoTIFF (COG)",
-            cog,
-        )
-
-        data = st.file_uploader("Upload a raster dataset", type=["tif", "img"])
-
-        if data:
-            url = empty.text_input(
-                "Enter a URL to a Cloud Optimized GeoTIFF (COG)",
-                "",
-            )
-
-        add_palette = st.checkbox("Add a color palette")
-        if add_palette:
-            palette = st.selectbox("Select a color palette", get_palettes())
-        else:
-            palette = None
-
-        submit = st.button("Submit")
-
-    m = leafmap.Map(latlon_control=False)
-
-    if submit:
-        if data or url:
-            try:
-                if data:
-                    file_path = leafmap.save_data(data)
-                    m.add_local_tile(file_path, palette=palette, debug=True)
-                elif url:
-                    m.add_remote_tile(url, palette=palette, debug=True)
-            except Exception as e:
-                with row1_col2:
-                    st.error("Work in progress. Try it again later.")
-
-    with row1_col1:
-        m.to_streamlit()

apps/rois.py

@@ -1,174 +0,0 @@
-""" A module for storing some sample ROIs for creating Landsat/GOES timelapse.
-"""
-
-from shapely.geometry import Polygon
-
-goes_rois = {
-    "Creek Fire, CA (2020-09-05)": {
-        "region": Polygon(
-            [
-                [-121.003418, 36.848857],
-                [-121.003418, 39.049052],
-                [-117.905273, 39.049052],
-                [-117.905273, 36.848857],
-                [-121.003418, 36.848857],
-            ]
-        ),
-        "start_time": "2020-09-05T15:00:00",
-        "end_time": "2020-09-06T02:00:00",
-    },
-    "Bomb Cyclone (2021-10-24)": {
-        "region": Polygon(
-            [
-                [-159.5954, 60.4088],
-                [-159.5954, 24.5178],
-                [-114.2438, 24.5178],
-                [-114.2438, 60.4088],
-            ]
-        ),
-        "start_time": "2021-10-24T14:00:00",
-        "end_time": "2021-10-25T01:00:00",
-    },
-    "Hunga Tonga Volcanic Eruption (2022-01-15)": {
-        "region": Polygon(
-            [
-                [-192.480469, -32.546813],
-                [-192.480469, -8.754795],
-                [-157.587891, -8.754795],
-                [-157.587891, -32.546813],
-                [-192.480469, -32.546813],
-            ]
-        ),
-        "start_time": "2022-01-15T03:00:00",
-        "end_time": "2022-01-15T07:00:00",
-    },
-    "Hunga Tonga Volcanic Eruption Closer Look (2022-01-15)": {
-        "region": Polygon(
-            [
-                [-178.901367, -22.958393],
-                [-178.901367, -17.85329],
-                [-171.452637, -17.85329],
-                [-171.452637, -22.958393],
-                [-178.901367, -22.958393],
-            ]
-        ),
-        "start_time": "2022-01-15T03:00:00",
-        "end_time": "2022-01-15T07:00:00",
-    },
-}
-
-
-landsat_rois = {
-    "Aral Sea": Polygon(
-        [
-            [57.667236, 43.834527],
-            [57.667236, 45.996962],
-            [61.12793, 45.996962],
-            [61.12793, 43.834527],
-            [57.667236, 43.834527],
-        ]
-    ),
-    "Dubai": Polygon(
-        [
-            [54.541626, 24.763044],
-            [54.541626, 25.427152],
-            [55.632019, 25.427152],
-            [55.632019, 24.763044],
-            [54.541626, 24.763044],
-        ]
-    ),
-    "Hong Kong International Airport": Polygon(
-        [
-            [113.825226, 22.198849],
-            [113.825226, 22.349758],
-            [114.085121, 22.349758],
-            [114.085121, 22.198849],
-            [113.825226, 22.198849],
-        ]
-    ),
-    "Las Vegas, NV": Polygon(
-        [
-            [-115.554199, 35.804449],
-            [-115.554199, 36.558188],
-            [-113.903503, 36.558188],
-            [-113.903503, 35.804449],
-            [-115.554199, 35.804449],
-        ]
-    ),
-    "Pucallpa, Peru": Polygon(
-        [
-            [-74.672699, -8.600032],
-            [-74.672699, -8.254983],
-            [-74.279938, -8.254983],
-            [-74.279938, -8.600032],
-        ]
-    ),
-    "Sierra Gorda, Chile": Polygon(
-        [
-            [-69.315491, -22.837104],
-            [-69.315491, -22.751488],
-            [-69.190006, -22.751488],
-            [-69.190006, -22.837104],
-            [-69.315491, -22.837104],
-        ]
-    ),
-}
-
-modis_rois = {
-    "World": Polygon(
-        [
-            [-171.210938, -57.136239],
-            [-171.210938, 79.997168],
-            [177.539063, 79.997168],
-            [177.539063, -57.136239],
-            [-171.210938, -57.136239],
-        ]
-    ),
-    "Africa": Polygon(
-        [
-            [-18.6983, 38.1446],
-            [-18.6983, -36.1630],
-            [52.2293, -36.1630],
-            [52.2293, 38.1446],
-        ]
-    ),
-    "USA": Polygon(
-        [
-            [-127.177734, 23.725012],
-            [-127.177734, 50.792047],
-            [-66.269531, 50.792047],
-            [-66.269531, 23.725012],
-            [-127.177734, 23.725012],
-        ]
-    ),
-}
-
-ocean_rois = {
-    "Gulf of Mexico": Polygon(
-        [
-            [-101.206055, 15.496032],
-            [-101.206055, 32.361403],
-            [-75.673828, 32.361403],
-            [-75.673828, 15.496032],
-            [-101.206055, 15.496032],
-        ]
-    ),
-    "North Atlantic Ocean": Polygon(
-        [
-            [-85.341797, 24.046464],
-            [-85.341797, 45.02695],
-            [-55.810547, 45.02695],
-            [-55.810547, 24.046464],
-            [-85.341797, 24.046464],
-        ]
-    ),
-    "World": Polygon(
-        [
-            [-171.210938, -57.136239],
-            [-171.210938, 79.997168],
-            [177.539063, 79.997168],
-            [177.539063, -57.136239],
-            [-171.210938, -57.136239],
-        ]
-    ),
-}

apps/timelapse.py

@@ -1,1314 +0,0 @@
-import ee
-import os
-import datetime
-import fiona
-import geopandas as gpd
-import folium
-import streamlit as st
-import geemap.colormaps as cm
-import geemap.foliumap as geemap
-from datetime import date
-from .rois import *
-
-
-@st.cache(allow_output_mutation=True)
-def uploaded_file_to_gdf(data):
-    import tempfile
-    import os
-    import uuid
-
-    _, file_extension = os.path.splitext(data.name)
-    file_id = str(uuid.uuid4())
-    file_path = os.path.join(tempfile.gettempdir(), f"{file_id}{file_extension}")
-
-    with open(file_path, "wb") as file:
-        file.write(data.getbuffer())
-
-    if file_path.lower().endswith(".kml"):
-        fiona.drvsupport.supported_drivers["KML"] = "rw"
-        gdf = gpd.read_file(file_path, driver="KML")
-    else:
-        gdf = gpd.read_file(file_path)
-
-    return gdf
-
-
-def app():
-
-    today = date.today()
-
-    st.title("Create Timelapse")
-
-    st.markdown(
-        """
-        An interactive web app for creating [Landsat](https://developers.google.com/earth-engine/datasets/catalog/landsat)/[GOES](https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16) timelapse for any location around the globe. 
-        The app was built using [streamlit](https://streamlit.io), [geemap](https://geemap.org), and [Google Earth Engine](https://earthengine.google.com). For more info, check out my streamlit [blog post](https://blog.streamlit.io/creating-satellite-timelapse-with-streamlit-and-earth-engine). 
-    """
-    )
-
-    row1_col1, row1_col2 = st.columns([2, 1])
-
-    if st.session_state.get("zoom_level") is None:
-        st.session_state["zoom_level"] = 4
-
-    st.session_state["ee_asset_id"] = None
-    st.session_state["bands"] = None
-    st.session_state["palette"] = None
-    st.session_state["vis_params"] = None
-
-    with row1_col1:
-        m = geemap.Map(
-            basemap="HYBRID",
-            plugin_Draw=True,
-            Draw_export=True,
-            locate_control=True,
-            plugin_LatLngPopup=False,
-        )
-        m.add_basemap("ROADMAP")
-
-    with row1_col2:
-
-        keyword = st.text_input("Search for a location:", "")
-        if keyword:
-            locations = geemap.geocode(keyword)
-            if locations is not None and len(locations) > 0:
-                str_locations = [str(g)[1:-1] for g in locations]
-                location = st.selectbox("Select a location:", str_locations)
-                loc_index = str_locations.index(location)
-                selected_loc = locations[loc_index]
-                lat, lng = selected_loc.lat, selected_loc.lng
-                folium.Marker(location=[lat, lng], popup=location).add_to(m)
-                m.set_center(lng, lat, 12)
-                st.session_state["zoom_level"] = 12
-
-        collection = st.selectbox(
-            "Select a satellite image collection: ",
-            [
-                "Any Earth Engine ImageCollection",
-                "Landsat TM-ETM-OLI Surface Reflectance",
-                "Sentinel-2 MSI Surface Reflectance",
-                "Geostationary Operational Environmental Satellites (GOES)",
-                "MODIS Vegetation Indices (NDVI/EVI) 16-Day Global 1km",
-                "MODIS Gap filled Land Surface Temperature Daily",
-                "MODIS Ocean Color SMI",
-                "USDA National Agriculture Imagery Program (NAIP)",
-            ],
-            index=1,
-        )
-
-        if collection in [
-            "Landsat TM-ETM-OLI Surface Reflectance",
-            "Sentinel-2 MSI Surface Reflectance",
-        ]:
-            roi_options = ["Uploaded GeoJSON"] + list(landsat_rois.keys())
-
-        elif collection == "Geostationary Operational Environmental Satellites (GOES)":
-            roi_options = ["Uploaded GeoJSON"] + list(goes_rois.keys())
-
-        elif collection in [
-            "MODIS Vegetation Indices (NDVI/EVI) 16-Day Global 1km",
-            "MODIS Gap filled Land Surface Temperature Daily",
-        ]:
-            roi_options = ["Uploaded GeoJSON"] + list(modis_rois.keys())
-        elif collection == "MODIS Ocean Color SMI":
-            roi_options = ["Uploaded GeoJSON"] + list(ocean_rois.keys())
-        else:
-            roi_options = ["Uploaded GeoJSON"]
-
-        if collection == "Any Earth Engine ImageCollection":
-            keyword = st.text_input("Enter a keyword to search (e.g., MODIS):", "")
-            if keyword:
-
-                assets = geemap.search_ee_data(keyword)
-                ee_assets = []
-                for asset in assets:
-                    if asset["ee_id_snippet"].startswith("ee.ImageCollection"):
-                        ee_assets.append(asset)
-
-                asset_titles = [x["title"] for x in ee_assets]
-                dataset = st.selectbox("Select a dataset:", asset_titles)
-                if len(ee_assets) > 0:
-                    st.session_state["ee_assets"] = ee_assets
-                    st.session_state["asset_titles"] = asset_titles
-                    index = asset_titles.index(dataset)
-                    ee_id = ee_assets[index]["id"]
-                else:
-                    ee_id = ""
-
-                if dataset is not None:
-                    with st.expander("Show dataset details", False):
-                        index = asset_titles.index(dataset)
-                        html = geemap.ee_data_html(st.session_state["ee_assets"][index])
-                        st.markdown(html, True)
-            # elif collection == "MODIS Gap filled Land Surface Temperature Daily":
-            #     ee_id = ""
-            else:
-                ee_id = ""
-
-            asset_id = st.text_input("Enter an ee.ImageCollection asset ID:", ee_id)
-
-            if asset_id:
-                with st.expander("Customize band combination and color palette", True):
-                    try:
-                        col = ee.ImageCollection.load(asset_id)
-                        st.session_state["ee_asset_id"] = asset_id
-                    except:
-                        st.error("Invalid Earth Engine asset ID.")
-                        st.session_state["ee_asset_id"] = None
-                        return
-
-                    img_bands = col.first().bandNames().getInfo()
-                    if len(img_bands) >= 3:
-                        default_bands = img_bands[:3][::-1]
-                    else:
-                        default_bands = img_bands[:]
-                    bands = st.multiselect(
-                        "Select one or three bands (RGB):", img_bands, default_bands
-                    )
-                    st.session_state["bands"] = bands
-
-                    if len(bands) == 1:
-                        palette_options = st.selectbox(
-                            "Color palette",
-                            cm.list_colormaps(),
-                            index=2,
-                        )
-                        palette_values = cm.get_palette(palette_options, 15)
-                        palette = st.text_area(
-                            "Enter a custom palette:",
-                            palette_values,
-                        )
-                        st.write(
-                            cm.plot_colormap(cmap=palette_options, return_fig=True)
-                        )
-                        st.session_state["palette"] = eval(palette)
-
-                    if bands:
-                        vis_params = st.text_area(
-                            "Enter visualization parameters",
-                            "{'bands': ["
-                            + ", ".join([f"'{band}'" for band in bands])
-                            + "]}",
-                        )
-                    else:
-                        vis_params = st.text_area(
-                            "Enter visualization parameters",
-                            "{}",
-                        )
-                    try:
-                        st.session_state["vis_params"] = eval(vis_params)
-                        st.session_state["vis_params"]["palette"] = st.session_state[
-                            "palette"
-                        ]
-                    except Exception as e:
-                        st.session_state["vis_params"] = None
-                        st.error(
-                            f"Invalid visualization parameters. It must be a dictionary."
-                        )
-
-        elif collection == "MODIS Gap filled Land Surface Temperature Daily":
-            with st.expander("Show dataset details", False):
-                st.markdown(
-                    """
-                See the [Awesome GEE Community Datasets](https://samapriya.github.io/awesome-gee-community-datasets/projects/daily_lst/).
-                """
-                )
-
-            MODIS_options = ["Daytime (1:30 pm)", "Nighttime (1:30 am)"]
-            MODIS_option = st.selectbox("Select a MODIS dataset:", MODIS_options)
-            if MODIS_option == "Daytime (1:30 pm)":
-                st.session_state[
-                    "ee_asset_id"
-                ] = "projects/sat-io/open-datasets/gap-filled-lst/gf_day_1km"
-            else:
-                st.session_state[
-                    "ee_asset_id"
-                ] = "projects/sat-io/open-datasets/gap-filled-lst/gf_night_1km"
-
-            palette_options = st.selectbox(
-                "Color palette",
-                cm.list_colormaps(),
-                index=90,
-            )
-            palette_values = cm.get_palette(palette_options, 15)
-            palette = st.text_area(
-                "Enter a custom palette:",
-                palette_values,
-            )
-            st.write(cm.plot_colormap(cmap=palette_options, return_fig=True))
-            st.session_state["palette"] = eval(palette)
-        elif collection == "MODIS Ocean Color SMI":
-            with st.expander("Show dataset details", False):
-                st.markdown(
-                    """
-                See the [Earth Engine Data Catalog](https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI).
-                """
-                )
-
-            MODIS_options = ["Aqua", "Terra"]
-            MODIS_option = st.selectbox("Select a satellite:", MODIS_options)
-            st.session_state["ee_asset_id"] = MODIS_option
-            # if MODIS_option == "Daytime (1:30 pm)":
-            #     st.session_state[
-            #         "ee_asset_id"
-            #     ] = "projects/sat-io/open-datasets/gap-filled-lst/gf_day_1km"
-            # else:
-            #     st.session_state[
-            #         "ee_asset_id"
-            #     ] = "projects/sat-io/open-datasets/gap-filled-lst/gf_night_1km"
-
-            band_dict = {
-                "Chlorophyll a concentration": "chlor_a",
-                "Normalized fluorescence line height": "nflh",
-                "Particulate organic carbon": "poc",
-                "Sea surface temperature": "sst",
-                "Remote sensing reflectance at band 412nm": "Rrs_412",
-                "Remote sensing reflectance at band 443nm": "Rrs_443",
-                "Remote sensing reflectance at band 469nm": "Rrs_469",
-                "Remote sensing reflectance at band 488nm": "Rrs_488",
-                "Remote sensing reflectance at band 531nm": "Rrs_531",
-                "Remote sensing reflectance at band 547nm": "Rrs_547",
-                "Remote sensing reflectance at band 555nm": "Rrs_555",
-                "Remote sensing reflectance at band 645nm": "Rrs_645",
-                "Remote sensing reflectance at band 667nm": "Rrs_667",
-                "Remote sensing reflectance at band 678nm": "Rrs_678",
-            }
-
-            band_options = list(band_dict.keys())
-            band = st.selectbox(
-                "Select a band",
-                band_options,
-                band_options.index("Sea surface temperature"),
-            )
-            st.session_state["band"] = band_dict[band]
-
-            colors = cm.list_colormaps()
-            palette_options = st.selectbox(
-                "Color palette",
-                colors,
-                index=colors.index("coolwarm"),
-            )
-            palette_values = cm.get_palette(palette_options, 15)
-            palette = st.text_area(
-                "Enter a custom palette:",
-                palette_values,
-            )
-            st.write(cm.plot_colormap(cmap=palette_options, return_fig=True))
-            st.session_state["palette"] = eval(palette)
-
-        sample_roi = st.selectbox(
-            "Select a sample ROI or upload a GeoJSON file:",
-            roi_options,
-            index=0,
-        )
-
-        add_outline = st.checkbox(
-            "Overlay an administrative boundary on timelapse", False
-        )
-
-        if add_outline:
-
-            with st.expander("Customize administrative boundary", True):
-
-                overlay_options = {
-                    "User-defined": None,
-                    "Continents": "continents",
-                    "Countries": "countries",
-                    "US States": "us_states",
-                    "China": "china",
-                }
-
-                overlay = st.selectbox(
-                    "Select an administrative boundary:",
-                    list(overlay_options.keys()),
-                    index=2,
-                )
-
-                overlay_data = overlay_options[overlay]
-
-                if overlay_data is None:
-                    overlay_data = st.text_input(
-                        "Enter an HTTP URL to a GeoJSON file or an ee.FeatureCollection asset id:",
-                        "https://raw.githubusercontent.com/giswqs/geemap/master/examples/data/countries.geojson",
-                    )
-
-                overlay_color = st.color_picker(
-                    "Select a color for the administrative boundary:", "#000000"
-                )
-                overlay_width = st.slider(
-                    "Select a line width for the administrative boundary:", 1, 20, 1
-                )
-                overlay_opacity = st.slider(
-                    "Select an opacity for the administrative boundary:",
-                    0.0,
-                    1.0,
-                    1.0,
-                    0.05,
-                )
-        else:
-            overlay_data = None
-            overlay_color = "black"
-            overlay_width = 1
-            overlay_opacity = 1
-
-    with row1_col1:
-
-        with st.expander(
-            "Steps: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Expand this tab to see a demo 👉"
-        ):
-            video_empty = st.empty()
-
-        data = st.file_uploader(
-            "Upload a GeoJSON file to use as an ROI. Customize timelapse parameters and then click the Submit button 😇👇",
-            type=["geojson", "kml", "zip"],
-        )
-
-        crs = "epsg:4326"
-        if sample_roi == "Uploaded GeoJSON":
-            if data is None:
-                # st.info(
-                #     "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click Submit button"
-                # )
-                if collection in [
-                    "Geostationary Operational Environmental Satellites (GOES)",
-                    "USDA National Agriculture Imagery Program (NAIP)",
-                ] and (not keyword):
-                    m.set_center(-100, 40, 3)
-                # else:
-                #     m.set_center(4.20, 18.63, zoom=2)
-        else:
-            if collection in [
-                "Landsat TM-ETM-OLI Surface Reflectance",
-                "Sentinel-2 MSI Surface Reflectance",
-            ]:
-                gdf = gpd.GeoDataFrame(
-                    index=[0], crs=crs, geometry=[landsat_rois[sample_roi]]
-                )
-            elif (
-                collection
-                == "Geostationary Operational Environmental Satellites (GOES)"
-            ):
-                gdf = gpd.GeoDataFrame(
-                    index=[0], crs=crs, geometry=[goes_rois[sample_roi]["region"]]
-                )
-            elif collection == "MODIS Vegetation Indices (NDVI/EVI) 16-Day Global 1km":
-                gdf = gpd.GeoDataFrame(
-                    index=[0], crs=crs, geometry=[modis_rois[sample_roi]]
-                )
-
-        if sample_roi != "Uploaded GeoJSON":
-
-            if collection in [
-                "Landsat TM-ETM-OLI Surface Reflectance",
-                "Sentinel-2 MSI Surface Reflectance",
-            ]:
-                gdf = gpd.GeoDataFrame(
-                    index=[0], crs=crs, geometry=[landsat_rois[sample_roi]]
-                )
-            elif (
-                collection
-                == "Geostationary Operational Environmental Satellites (GOES)"
-            ):
-                gdf = gpd.GeoDataFrame(
-                    index=[0], crs=crs, geometry=[goes_rois[sample_roi]["region"]]
-                )
-            elif collection in [
-                "MODIS Vegetation Indices (NDVI/EVI) 16-Day Global 1km",
-                "MODIS Gap filled Land Surface Temperature Daily",
-            ]:
-                gdf = gpd.GeoDataFrame(
-                    index=[0], crs=crs, geometry=[modis_rois[sample_roi]]
-                )
-            elif collection == "MODIS Ocean Color SMI":
-                gdf = gpd.GeoDataFrame(
-                    index=[0], crs=crs, geometry=[ocean_rois[sample_roi]]
-                )
-            st.session_state["roi"] = geemap.gdf_to_ee(gdf, geodesic=False)
-            m.add_gdf(gdf, "ROI")
-
-        elif data:
-            gdf = uploaded_file_to_gdf(data)
-            st.session_state["roi"] = geemap.gdf_to_ee(gdf, geodesic=False)
-            m.add_gdf(gdf, "ROI")
-
-        m.to_streamlit(height=600)
-
-    with row1_col2:
-
-        if collection in [
-            "Landsat TM-ETM-OLI Surface Reflectance",
-            "Sentinel-2 MSI Surface Reflectance",
-        ]:
-
-            if collection == "Landsat TM-ETM-OLI Surface Reflectance":
-                sensor_start_year = 1984
-                timelapse_title = "Landsat Timelapse"
-                timelapse_speed = 5
-            elif collection == "Sentinel-2 MSI Surface Reflectance":
-                sensor_start_year = 2015
-                timelapse_title = "Sentinel-2 Timelapse"
-                timelapse_speed = 5
-            video_empty.video("https://youtu.be/VVRK_-dEjR4")
-
-            with st.form("submit_landsat_form"):
-
-                roi = None
-                if st.session_state.get("roi") is not None:
-                    roi = st.session_state.get("roi")
-                out_gif = geemap.temp_file_path(".gif")
-
-                title = st.text_input(
-                    "Enter a title to show on the timelapse: ", timelapse_title
-                )
-                RGB = st.selectbox(
-                    "Select an RGB band combination:",
-                    [
-                        "Red/Green/Blue",
-                        "NIR/Red/Green",
-                        "SWIR2/SWIR1/NIR",
-                        "NIR/SWIR1/Red",
-                        "SWIR2/NIR/Red",
-                        "SWIR2/SWIR1/Red",
-                        "SWIR1/NIR/Blue",
-                        "NIR/SWIR1/Blue",
-                        "SWIR2/NIR/Green",
-                        "SWIR1/NIR/Red",
-                        "SWIR2/NIR/SWIR1",
-                        "SWIR1/NIR/SWIR2",
-                    ],
-                    index=9,
-                )
-
-                frequency = st.selectbox(
-                    "Select a temporal frequency:",
-                    ["year", "quarter", "month"],
-                    index=0,
-                )
-
-                with st.expander("Customize timelapse"):
-
-                    speed = st.slider("Frames per second:", 1, 30, timelapse_speed)
-                    dimensions = st.slider(
-                        "Maximum dimensions (Width*Height) in pixels", 768, 2000, 768
-                    )
-                    progress_bar_color = st.color_picker(
-                        "Progress bar color:", "#0000ff"
-                    )
-                    years = st.slider(
-                        "Start and end year:",
-                        sensor_start_year,
-                        today.year,
-                        (sensor_start_year, today.year),
-                    )
-                    months = st.slider("Start and end month:", 1, 12, (1, 12))
-                    font_size = st.slider("Font size:", 10, 50, 30)
-                    font_color = st.color_picker("Font color:", "#ffffff")
-                    apply_fmask = st.checkbox(
-                        "Apply fmask (remove clouds, shadows, snow)", True
-                    )
-                    font_type = st.selectbox(
-                        "Select the font type for the title:",
-                        ["arial.ttf", "alibaba.otf"],
-                        index=0,
-                    )
-                    fading = st.slider(
-                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
-                    )
-                    mp4 = st.checkbox("Save timelapse as MP4", True)
-
-                empty_text = st.empty()
-                empty_image = st.empty()
-                empty_fire_image = st.empty()
-                empty_video = st.container()
-                submitted = st.form_submit_button("Submit")
-                if submitted:
-
-                    if sample_roi == "Uploaded GeoJSON" and data is None:
-                        empty_text.warning(
-                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
-                        )
-                    else:
-
-                        empty_text.text("Computing... Please wait...")
-
-                        start_year = years[0]
-                        end_year = years[1]
-                        start_date = str(months[0]).zfill(2) + "-01"
-                        end_date = str(months[1]).zfill(2) + "-30"
-                        bands = RGB.split("/")
-
-                        try:
-                            if collection == "Landsat TM-ETM-OLI Surface Reflectance":
-                                out_gif = geemap.landsat_timelapse(
-                                    roi=roi,
-                                    out_gif=out_gif,
-                                    start_year=start_year,
-                                    end_year=end_year,
-                                    start_date=start_date,
-                                    end_date=end_date,
-                                    bands=bands,
-                                    apply_fmask=apply_fmask,
-                                    frames_per_second=speed,
-                                    dimensions=dimensions,
-                                    overlay_data=overlay_data,
-                                    overlay_color=overlay_color,
-                                    overlay_width=overlay_width,
-                                    overlay_opacity=overlay_opacity,
-                                    frequency=frequency,
-                                    date_format=None,
-                                    title=title,
-                                    title_xy=("2%", "90%"),
-                                    add_text=True,
-                                    text_xy=("2%", "2%"),
-                                    text_sequence=None,
-                                    font_type=font_type,
-                                    font_size=font_size,
-                                    font_color=font_color,
-                                    add_progress_bar=True,
-                                    progress_bar_color=progress_bar_color,
-                                    progress_bar_height=5,
-                                    loop=0,
-                                    mp4=mp4,
-                                    fading=fading,
-                                )
-                            elif collection == "Sentinel-2 MSI Surface Reflectance":
-                                out_gif = geemap.sentinel2_timelapse(
-                                    roi=roi,
-                                    out_gif=out_gif,
-                                    start_year=start_year,
-                                    end_year=end_year,
-                                    start_date=start_date,
-                                    end_date=end_date,
-                                    bands=bands,
-                                    apply_fmask=apply_fmask,
-                                    frames_per_second=speed,
-                                    dimensions=dimensions,
-                                    overlay_data=overlay_data,
-                                    overlay_color=overlay_color,
-                                    overlay_width=overlay_width,
-                                    overlay_opacity=overlay_opacity,
-                                    frequency=frequency,
-                                    date_format=None,
-                                    title=title,
-                                    title_xy=("2%", "90%"),
-                                    add_text=True,
-                                    text_xy=("2%", "2%"),
-                                    text_sequence=None,
-                                    font_type=font_type,
-                                    font_size=font_size,
-                                    font_color=font_color,
-                                    add_progress_bar=True,
-                                    progress_bar_color=progress_bar_color,
-                                    progress_bar_height=5,
-                                    loop=0,
-                                    mp4=mp4,
-                                    fading=fading,
-                                )
-                        except:
-                            empty_text.error(
-                                "An error occurred while computing the timelapse. Your probably requested too much data. Try reducing the ROI or timespan."
-                            )
-                            st.stop()
-
-                        if out_gif is not None and os.path.exists(out_gif):
-
-                            empty_text.text(
-                                "Right click the GIF to save it to your computer👇"
-                            )
-                            empty_image.image(out_gif)
-
-                            out_mp4 = out_gif.replace(".gif", ".mp4")
-                            if mp4 and os.path.exists(out_mp4):
-                                with empty_video:
-                                    st.text(
-                                        "Right click the MP4 to save it to your computer👇"
-                                    )
-                                    st.video(out_gif.replace(".gif", ".mp4"))
-
-                        else:
-                            empty_text.error(
-                                "Something went wrong. You probably requested too much data. Try reducing the ROI or timespan."
-                            )
-
-        elif collection == "Geostationary Operational Environmental Satellites (GOES)":
-
-            video_empty.video("https://youtu.be/16fA2QORG4A")
-
-            with st.form("submit_goes_form"):
-
-                roi = None
-                if st.session_state.get("roi") is not None:
-                    roi = st.session_state.get("roi")
-                out_gif = geemap.temp_file_path(".gif")
-
-                satellite = st.selectbox("Select a satellite:", ["GOES-17", "GOES-16"])
-                earliest_date = datetime.date(2017, 7, 10)
-                latest_date = datetime.date.today()
-
-                if sample_roi == "Uploaded GeoJSON":
-                    roi_start_date = today - datetime.timedelta(days=2)
-                    roi_end_date = today - datetime.timedelta(days=1)
-                    roi_start_time = datetime.time(14, 00)
-                    roi_end_time = datetime.time(1, 00)
-                else:
-                    roi_start = goes_rois[sample_roi]["start_time"]
-                    roi_end = goes_rois[sample_roi]["end_time"]
-                    roi_start_date = datetime.datetime.strptime(
-                        roi_start[:10], "%Y-%m-%d"
-                    )
-                    roi_end_date = datetime.datetime.strptime(roi_end[:10], "%Y-%m-%d")
-                    roi_start_time = datetime.time(
-                        int(roi_start[11:13]), int(roi_start[14:16])
-                    )
-                    roi_end_time = datetime.time(
-                        int(roi_end[11:13]), int(roi_end[14:16])
-                    )
-
-                start_date = st.date_input("Select the start date:", roi_start_date)
-                end_date = st.date_input("Select the end date:", roi_end_date)
-
-                with st.expander("Customize timelapse"):
-
-                    add_fire = st.checkbox("Add Fire/Hotspot Characterization", False)
-
-                    scan_type = st.selectbox(
-                        "Select a scan type:", ["Full Disk", "CONUS", "Mesoscale"]
-                    )
-
-                    start_time = st.time_input(
-                        "Select the start time of the start date:", roi_start_time
-                    )
-
-                    end_time = st.time_input(
-                        "Select the end time of the end date:", roi_end_time
-                    )
-
-                    start = (
-                        start_date.strftime("%Y-%m-%d")
-                        + "T"
-                        + start_time.strftime("%H:%M:%S")
-                    )
-                    end = (
-                        end_date.strftime("%Y-%m-%d")
-                        + "T"
-                        + end_time.strftime("%H:%M:%S")
-                    )
-
-                    speed = st.slider("Frames per second:", 1, 30, 5)
-                    add_progress_bar = st.checkbox("Add a progress bar", True)
-                    progress_bar_color = st.color_picker(
-                        "Progress bar color:", "#0000ff"
-                    )
-                    font_size = st.slider("Font size:", 10, 50, 20)
-                    font_color = st.color_picker("Font color:", "#ffffff")
-                    fading = st.slider(
-                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
-                    )
-                    mp4 = st.checkbox("Save timelapse as MP4", True)
-
-                empty_text = st.empty()
-                empty_image = st.empty()
-                empty_video = st.container()
-                empty_fire_text = st.empty()
-                empty_fire_image = st.empty()
-
-                submitted = st.form_submit_button("Submit")
-                if submitted:
-                    if sample_roi == "Uploaded GeoJSON" and data is None:
-                        empty_text.warning(
-                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
-                        )
-                    else:
-                        empty_text.text("Computing... Please wait...")
-
-                        geemap.goes_timelapse(
-                            out_gif,
-                            start_date=start,
-                            end_date=end,
-                            data=satellite,
-                            scan=scan_type.replace(" ", "_").lower(),
-                            region=roi,
-                            dimensions=768,
-                            framesPerSecond=speed,
-                            date_format="YYYY-MM-dd HH:mm",
-                            xy=("3%", "3%"),
-                            text_sequence=None,
-                            font_type="arial.ttf",
-                            font_size=font_size,
-                            font_color=font_color,
-                            add_progress_bar=add_progress_bar,
-                            progress_bar_color=progress_bar_color,
-                            progress_bar_height=5,
-                            loop=0,
-                            overlay_data=overlay_data,
-                            overlay_color=overlay_color,
-                            overlay_width=overlay_width,
-                            overlay_opacity=overlay_opacity,
-                            mp4=mp4,
-                            fading=fading,
-                        )
-
-                        if out_gif is not None and os.path.exists(out_gif):
-                            empty_text.text(
-                                "Right click the GIF to save it to your computer👇"
-                            )
-                            empty_image.image(out_gif)
-
-                            out_mp4 = out_gif.replace(".gif", ".mp4")
-                            if mp4 and os.path.exists(out_mp4):
-                                with empty_video:
-                                    st.text(
-                                        "Right click the MP4 to save it to your computer👇"
-                                    )
-                                    st.video(out_gif.replace(".gif", ".mp4"))
-
-                            if add_fire:
-                                out_fire_gif = geemap.temp_file_path(".gif")
-                                empty_fire_text.text(
-                                    "Delineating Fire Hotspot... Please wait..."
-                                )
-                                geemap.goes_fire_timelapse(
-                                    out_fire_gif,
-                                    start_date=start,
-                                    end_date=end,
-                                    data=satellite,
-                                    scan=scan_type.replace(" ", "_").lower(),
-                                    region=roi,
-                                    dimensions=768,
-                                    framesPerSecond=speed,
-                                    date_format="YYYY-MM-dd HH:mm",
-                                    xy=("3%", "3%"),
-                                    text_sequence=None,
-                                    font_type="arial.ttf",
-                                    font_size=font_size,
-                                    font_color=font_color,
-                                    add_progress_bar=add_progress_bar,
-                                    progress_bar_color=progress_bar_color,
-                                    progress_bar_height=5,
-                                    loop=0,
-                                )
-                                if os.path.exists(out_fire_gif):
-                                    empty_fire_image.image(out_fire_gif)
-                        else:
-                            empty_text.text(
-                                "Something went wrong, either the ROI is too big or there are no data available for the specified date range. Please try a smaller ROI or different date range."
-                            )
-
-        elif collection == "MODIS Vegetation Indices (NDVI/EVI) 16-Day Global 1km":
-
-            video_empty.video("https://youtu.be/16fA2QORG4A")
-
-            satellite = st.selectbox("Select a satellite:", ["Terra", "Aqua"])
-            band = st.selectbox("Select a band:", ["NDVI", "EVI"])
-
-            with st.form("submit_modis_form"):
-
-                roi = None
-                if st.session_state.get("roi") is not None:
-                    roi = st.session_state.get("roi")
-                out_gif = geemap.temp_file_path(".gif")
-
-                with st.expander("Customize timelapse"):
-
-                    start = st.date_input(
-                        "Select a start date:", datetime.date(2000, 2, 8)
-                    )
-                    end = st.date_input("Select an end date:", datetime.date.today())
-
-                    start_date = start.strftime("%Y-%m-%d")
-                    end_date = end.strftime("%Y-%m-%d")
-
-                    speed = st.slider("Frames per second:", 1, 30, 5)
-                    add_progress_bar = st.checkbox("Add a progress bar", True)
-                    progress_bar_color = st.color_picker(
-                        "Progress bar color:", "#0000ff"
-                    )
-                    font_size = st.slider("Font size:", 10, 50, 20)
-                    font_color = st.color_picker("Font color:", "#ffffff")
-
-                    font_type = st.selectbox(
-                        "Select the font type for the title:",
-                        ["arial.ttf", "alibaba.otf"],
-                        index=0,
-                    )
-                    fading = st.slider(
-                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
-                    )
-                    mp4 = st.checkbox("Save timelapse as MP4", True)
-
-                empty_text = st.empty()
-                empty_image = st.empty()
-                empty_video = st.container()
-
-                submitted = st.form_submit_button("Submit")
-                if submitted:
-                    if sample_roi == "Uploaded GeoJSON" and data is None:
-                        empty_text.warning(
-                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
-                        )
-                    else:
-
-                        empty_text.text("Computing... Please wait...")
-
-                        geemap.modis_ndvi_timelapse(
-                            out_gif,
-                            satellite,
-                            band,
-                            start_date,
-                            end_date,
-                            roi,
-                            768,
-                            speed,
-                            overlay_data=overlay_data,
-                            overlay_color=overlay_color,
-                            overlay_width=overlay_width,
-                            overlay_opacity=overlay_opacity,
-                            mp4=mp4,
-                            fading=fading,
-                        )
-
-                        geemap.reduce_gif_size(out_gif)
-
-                        empty_text.text(
-                            "Right click the GIF to save it to your computer👇"
-                        )
-                        empty_image.image(out_gif)
-
-                        out_mp4 = out_gif.replace(".gif", ".mp4")
-                        if mp4 and os.path.exists(out_mp4):
-                            with empty_video:
-                                st.text(
-                                    "Right click the MP4 to save it to your computer👇"
-                                )
-                                st.video(out_gif.replace(".gif", ".mp4"))
-
-        elif collection == "Any Earth Engine ImageCollection":
-
-            with st.form("submit_ts_form"):
-                with st.expander("Customize timelapse"):
-
-                    title = st.text_input(
-                        "Enter a title to show on the timelapse: ", "Timelapse"
-                    )
-                    start_date = st.date_input(
-                        "Select the start date:", datetime.date(2020, 1, 1)
-                    )
-                    end_date = st.date_input(
-                        "Select the end date:", datetime.date.today()
-                    )
-                    frequency = st.selectbox(
-                        "Select a temporal frequency:",
-                        ["year", "quarter", "month", "day", "hour", "minute", "second"],
-                        index=0,
-                    )
-                    reducer = st.selectbox(
-                        "Select a reducer for aggregating data:",
-                        ["median", "mean", "min", "max", "sum", "variance", "stdDev"],
-                        index=0,
-                    )
-                    data_format = st.selectbox(
-                        "Select a date format to show on the timelapse:",
-                        [
-                            "YYYY-MM-dd",
-                            "YYYY",
-                            "YYMM-MM",
-                            "YYYY-MM-dd HH:mm",
-                            "YYYY-MM-dd HH:mm:ss",
-                            "HH:mm",
-                            "HH:mm:ss",
-                            "w",
-                            "M",
-                            "d",
-                            "D",
-                        ],
-                        index=0,
-                    )
-
-                    speed = st.slider("Frames per second:", 1, 30, 5)
-                    add_progress_bar = st.checkbox("Add a progress bar", True)
-                    progress_bar_color = st.color_picker(
-                        "Progress bar color:", "#0000ff"
-                    )
-                    font_size = st.slider("Font size:", 10, 50, 30)
-                    font_color = st.color_picker("Font color:", "#ffffff")
-                    font_type = st.selectbox(
-                        "Select the font type for the title:",
-                        ["arial.ttf", "alibaba.otf"],
-                        index=0,
-                    )
-                    fading = st.slider(
-                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
-                    )
-                    mp4 = st.checkbox("Save timelapse as MP4", True)
-
-                empty_text = st.empty()
-                empty_image = st.empty()
-                empty_video = st.container()
-                empty_fire_image = st.empty()
-
-                roi = None
-                if st.session_state.get("roi") is not None:
-                    roi = st.session_state.get("roi")
-                out_gif = geemap.temp_file_path(".gif")
-
-                submitted = st.form_submit_button("Submit")
-                if submitted:
-
-                    if sample_roi == "Uploaded GeoJSON" and data is None:
-                        empty_text.warning(
-                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
-                        )
-                    else:
-
-                        empty_text.text("Computing... Please wait...")
-                        try:
-                            geemap.create_timelapse(
-                                st.session_state.get("ee_asset_id"),
-                                start_date=start_date.strftime("%Y-%m-%d"),
-                                end_date=end_date.strftime("%Y-%m-%d"),
-                                region=roi,
-                                frequency=frequency,
-                                reducer=reducer,
-                                date_format=data_format,
-                                out_gif=out_gif,
-                                bands=st.session_state.get("bands"),
-                                palette=st.session_state.get("palette"),
-                                vis_params=st.session_state.get("vis_params"),
-                                dimensions=768,
-                                frames_per_second=speed,
-                                crs="EPSG:3857",
-                                overlay_data=overlay_data,
-                                overlay_color=overlay_color,
-                                overlay_width=overlay_width,
-                                overlay_opacity=overlay_opacity,
-                                title=title,
-                                title_xy=("2%", "90%"),
-                                add_text=True,
-                                text_xy=("2%", "2%"),
-                                text_sequence=None,
-                                font_type=font_type,
-                                font_size=font_size,
-                                font_color=font_color,
-                                add_progress_bar=add_progress_bar,
-                                progress_bar_color=progress_bar_color,
-                                progress_bar_height=5,
-                                loop=0,
-                                mp4=mp4,
-                                fading=fading,
-                            )
-                        except:
-                            empty_text.error(
-                                "An error occurred while computing the timelapse. You probably requested too much data. Try reducing the ROI or timespan."
-                            )
-
-                        empty_text.text(
-                            "Right click the GIF to save it to your computer👇"
-                        )
-                        empty_image.image(out_gif)
-
-                        out_mp4 = out_gif.replace(".gif", ".mp4")
-                        if mp4 and os.path.exists(out_mp4):
-                            with empty_video:
-                                st.text(
-                                    "Right click the MP4 to save it to your computer👇"
-                                )
-                                st.video(out_gif.replace(".gif", ".mp4"))
-
-        elif collection in [
-            "MODIS Gap filled Land Surface Temperature Daily",
-            "MODIS Ocean Color SMI",
-        ]:
-
-            with st.form("submit_ts_form"):
-                with st.expander("Customize timelapse"):
-
-                    title = st.text_input(
-                        "Enter a title to show on the timelapse: ",
-                        "Surface Temperature",
-                    )
-                    start_date = st.date_input(
-                        "Select the start date:", datetime.date(2018, 1, 1)
-                    )
-                    end_date = st.date_input(
-                        "Select the end date:", datetime.date(2020, 12, 31)
-                    )
-                    frequency = st.selectbox(
-                        "Select a temporal frequency:",
-                        ["year", "quarter", "month", "week", "day"],
-                        index=2,
-                    )
-                    reducer = st.selectbox(
-                        "Select a reducer for aggregating data:",
-                        ["median", "mean", "min", "max", "sum", "variance", "stdDev"],
-                        index=0,
-                    )
-
-                    vis_params = st.text_area(
-                        "Enter visualization parameters",
-                        "",
-                        help="Enter a string in the format of a dictionary, such as '{'min': 23, 'max': 32}'",
-                    )
-
-                    speed = st.slider("Frames per second:", 1, 30, 5)
-                    add_progress_bar = st.checkbox("Add a progress bar", True)
-                    progress_bar_color = st.color_picker(
-                        "Progress bar color:", "#0000ff"
-                    )
-                    font_size = st.slider("Font size:", 10, 50, 30)
-                    font_color = st.color_picker("Font color:", "#ffffff")
-                    font_type = st.selectbox(
-                        "Select the font type for the title:",
-                        ["arial.ttf", "alibaba.otf"],
-                        index=0,
-                    )
-                    add_colorbar = st.checkbox("Add a colorbar", True)
-                    colorbar_label = st.text_input(
-                        "Enter the colorbar label:", "Surface Temperature (°C)"
-                    )
-                    fading = st.slider(
-                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
-                    )
-                    mp4 = st.checkbox("Save timelapse as MP4", True)
-
-                empty_text = st.empty()
-                empty_image = st.empty()
-                empty_video = st.container()
-
-                roi = None
-                if st.session_state.get("roi") is not None:
-                    roi = st.session_state.get("roi")
-                out_gif = geemap.temp_file_path(".gif")
-
-                submitted = st.form_submit_button("Submit")
-                if submitted:
-
-                    if sample_roi == "Uploaded GeoJSON" and data is None:
-                        empty_text.warning(
-                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
-                        )
-                    else:
-
-                        empty_text.text("Computing... Please wait...")
-                        try:
-                            if (
-                                collection
-                                == "MODIS Gap filled Land Surface Temperature Daily"
-                            ):
-                                out_gif = geemap.create_timelapse(
-                                    st.session_state.get("ee_asset_id"),
-                                    start_date=start_date.strftime("%Y-%m-%d"),
-                                    end_date=end_date.strftime("%Y-%m-%d"),
-                                    region=roi,
-                                    bands=None,
-                                    frequency=frequency,
-                                    reducer=reducer,
-                                    date_format=None,
-                                    out_gif=out_gif,
-                                    palette=st.session_state.get("palette"),
-                                    vis_params=None,
-                                    dimensions=768,
-                                    frames_per_second=speed,
-                                    crs="EPSG:3857",
-                                    overlay_data=overlay_data,
-                                    overlay_color=overlay_color,
-                                    overlay_width=overlay_width,
-                                    overlay_opacity=overlay_opacity,
-                                    title=title,
-                                    title_xy=("2%", "90%"),
-                                    add_text=True,
-                                    text_xy=("2%", "2%"),
-                                    text_sequence=None,
-                                    font_type=font_type,
-                                    font_size=font_size,
-                                    font_color=font_color,
-                                    add_progress_bar=add_progress_bar,
-                                    progress_bar_color=progress_bar_color,
-                                    progress_bar_height=5,
-                                    add_colorbar=add_colorbar,
-                                    colorbar_label=colorbar_label,
-                                    loop=0,
-                                    mp4=mp4,
-                                    fading=fading,
-                                )
-                            elif collection == "MODIS Ocean Color SMI":
-                                if vis_params.startswith("{") and vis_params.endswith(
-                                    "}"
-                                ):
-                                    vis_params = eval(vis_params)
-                                else:
-                                    vis_params = None
-                                out_gif = geemap.modis_ocean_color_timelapse(
-                                    st.session_state.get("ee_asset_id"),
-                                    start_date=start_date.strftime("%Y-%m-%d"),
-                                    end_date=end_date.strftime("%Y-%m-%d"),
-                                    region=roi,
-                                    bands=st.session_state["band"],
-                                    frequency=frequency,
-                                    reducer=reducer,
-                                    date_format=None,
-                                    out_gif=out_gif,
-                                    palette=st.session_state.get("palette"),
-                                    vis_params=vis_params,
-                                    dimensions=768,
-                                    frames_per_second=speed,
-                                    crs="EPSG:3857",
-                                    overlay_data=overlay_data,
-                                    overlay_color=overlay_color,
-                                    overlay_width=overlay_width,
-                                    overlay_opacity=overlay_opacity,
-                                    title=title,
-                                    title_xy=("2%", "90%"),
-                                    add_text=True,
-                                    text_xy=("2%", "2%"),
-                                    text_sequence=None,
-                                    font_type=font_type,
-                                    font_size=font_size,
-                                    font_color=font_color,
-                                    add_progress_bar=add_progress_bar,
-                                    progress_bar_color=progress_bar_color,
-                                    progress_bar_height=5,
-                                    add_colorbar=add_colorbar,
-                                    colorbar_label=colorbar_label,
-                                    loop=0,
-                                    mp4=mp4,
-                                    fading=fading,
-                                )
-                        except:
-                            empty_text.error(
-                                "Something went wrong. You probably requested too much data. Try reducing the ROI or timespan."
-                            )
-
-                        if out_gif is not None and os.path.exists(out_gif):
-
-                            geemap.reduce_gif_size(out_gif)
-
-                            empty_text.text(
-                                "Right click the GIF to save it to your computer👇"
-                            )
-                            empty_image.image(out_gif)
-
-                            out_mp4 = out_gif.replace(".gif", ".mp4")
-                            if mp4 and os.path.exists(out_mp4):
-                                with empty_video:
-                                    st.text(
-                                        "Right click the MP4 to save it to your computer👇"
-                                    )
-                                    st.video(out_gif.replace(".gif", ".mp4"))
-
-                        else:
-                            st.error(
-                                "Something went wrong. You probably requested too much data. Try reducing the ROI or timespan."
-                            )
-
-        elif collection == "USDA National Agriculture Imagery Program (NAIP)":
-
-            with st.form("submit_naip_form"):
-                with st.expander("Customize timelapse"):
-
-                    title = st.text_input(
-                        "Enter a title to show on the timelapse: ", "NAIP Timelapse"
-                    )
-
-                    years = st.slider(
-                        "Start and end year:",
-                        2003,
-                        today.year,
-                        (2003, today.year),
-                    )
-
-                    bands = st.selectbox(
-                        "Select a band combination:", ["N/R/G", "R/G/B"], index=0
-                    )
-
-                    speed = st.slider("Frames per second:", 1, 30, 3)
-                    add_progress_bar = st.checkbox("Add a progress bar", True)
-                    progress_bar_color = st.color_picker(
-                        "Progress bar color:", "#0000ff"
-                    )
-                    font_size = st.slider("Font size:", 10, 50, 30)
-                    font_color = st.color_picker("Font color:", "#ffffff")
-                    font_type = st.selectbox(
-                        "Select the font type for the title:",
-                        ["arial.ttf", "alibaba.otf"],
-                        index=0,
-                    )
-                    fading = st.slider(
-                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
-                    )
-                    mp4 = st.checkbox("Save timelapse as MP4", True)
-
-                empty_text = st.empty()
-                empty_image = st.empty()
-                empty_video = st.container()
-                empty_fire_image = st.empty()
-
-                roi = None
-                if st.session_state.get("roi") is not None:
-                    roi = st.session_state.get("roi")
-                out_gif = geemap.temp_file_path(".gif")
-
-                submitted = st.form_submit_button("Submit")
-                if submitted:
-
-                    if sample_roi == "Uploaded GeoJSON" and data is None:
-                        empty_text.warning(
-                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
-                        )
-                    else:
-
-                        empty_text.text("Computing... Please wait...")
-                        try:
-                            geemap.naip_timelapse(
-                                roi,
-                                years[0],
-                                years[1],
-                                out_gif,
-                                bands=bands.split("/"),
-                                palette=st.session_state.get("palette"),
-                                vis_params=None,
-                                dimensions=768,
-                                frames_per_second=speed,
-                                crs="EPSG:3857",
-                                overlay_data=overlay_data,
-                                overlay_color=overlay_color,
-                                overlay_width=overlay_width,
-                                overlay_opacity=overlay_opacity,
-                                title=title,
-                                title_xy=("2%", "90%"),
-                                add_text=True,
-                                text_xy=("2%", "2%"),
-                                text_sequence=None,
-                                font_type=font_type,
-                                font_size=font_size,
-                                font_color=font_color,
-                                add_progress_bar=add_progress_bar,
-                                progress_bar_color=progress_bar_color,
-                                progress_bar_height=5,
-                                loop=0,
-                                mp4=mp4,
-                                fading=fading,
-                            )
-                        except:
-                            empty_text.error(
-                                "Something went wrong. You either requested too much data or the ROI is outside the U.S."
-                            )
-
-                        if out_gif is not None and os.path.exists(out_gif):
-
-                            empty_text.text(
-                                "Right click the GIF to save it to your computer👇"
-                            )
-                            empty_image.image(out_gif)
-
-                            out_mp4 = out_gif.replace(".gif", ".mp4")
-                            if mp4 and os.path.exists(out_mp4):
-                                with empty_video:
-                                    st.text(
-                                        "Right click the MP4 to save it to your computer👇"
-                                    )
-                                    st.video(out_gif.replace(".gif", ".mp4"))
-
-                        else:
-                            st.error(
-                                "Something went wrong. You either requested too much data or the ROI is outside the U.S."
-                            )

apps/vector.py

@@ -1,98 +0,0 @@
-import os
-import fiona
-import geopandas as gpd
-import streamlit as st
-
-
-def save_uploaded_file(file_content, file_name):
-    """
-    Save the uploaded file to a temporary directory
-    """
-    import tempfile
-    import os
-    import uuid
-
-    _, file_extension = os.path.splitext(file_name)
-    file_id = str(uuid.uuid4())
-    file_path = os.path.join(tempfile.gettempdir(), f"{file_id}{file_extension}")
-
-    with open(file_path, "wb") as file:
-        file.write(file_content.getbuffer())
-
-    return file_path
-
-
-def app():
-
-    st.title("Upload Vector Data")
-
-    row1_col1, row1_col2 = st.columns([2, 1])
-    width = 950
-    height = 600
-
-    with row1_col2:
-
-        backend = st.selectbox(
-            "Select a plotting backend", ["folium", "kepler.gl", "pydeck"], index=2
-        )
-
-        if backend == "folium":
-            import leafmap.foliumap as leafmap
-        elif backend == "kepler.gl":
-            import leafmap.kepler as leafmap
-        elif backend == "pydeck":
-            import leafmap.deck as leafmap
-
-        url = st.text_input(
-            "Enter a URL to a vector dataset",
-            "https://github.com/giswqs/streamlit-geospatial/raw/master/data/us_states.geojson",
-        )
-
-        data = st.file_uploader(
-            "Upload a vector dataset", type=["geojson", "kml", "zip", "tab"]
-        )
-
-        container = st.container()
-
-        if data or url:
-            if data:
-                file_path = save_uploaded_file(data, data.name)
-                layer_name = os.path.splitext(data.name)[0]
-            elif url:
-                file_path = url
-                layer_name = url.split("/")[-1].split(".")[0]
-
-            with row1_col1:
-                if file_path.lower().endswith(".kml"):
-                    fiona.drvsupport.supported_drivers["KML"] = "rw"
-                    gdf = gpd.read_file(file_path, driver="KML")
-                else:
-                    gdf = gpd.read_file(file_path)
-                lon, lat = leafmap.gdf_centroid(gdf)
-                if backend == "pydeck":
-
-                    column_names = gdf.columns.values.tolist()
-                    random_column = None
-                    with container:
-                        random_color = st.checkbox("Apply random colors", True)
-                        if random_color:
-                            random_column = st.selectbox(
-                                "Select a column to apply random colors", column_names
-                            )
-
-                    m = leafmap.Map(center=(lat, lon))
-                    m.add_gdf(gdf, random_color_column=random_column)
-                    st.pydeck_chart(m)
-
-                else:
-                    m = leafmap.Map(center=(lat, lon), draw_export=True)
-                    m.add_gdf(gdf, layer_name=layer_name)
-                    # m.add_vector(file_path, layer_name=layer_name)
-                    if backend == "folium":
-                        m.zoom_to_gdf(gdf)
-                    m.to_streamlit(width=width, height=height)
-
-        else:
-            with row1_col1:
-                m = leafmap.Map()
-                st.pydeck_chart(m)

apps/wms.py

@@ -1,68 +0,0 @@
-import ast
-import streamlit as st
-import leafmap.foliumap as leafmap
-
-
-@st.cache(allow_output_mutation=True)
-def get_layers(url):
-    options = leafmap.get_wms_layers(url)
-    return options
-
-
-def app():
-    st.title("Add Web Map Service (WMS)")
-    st.markdown(
-        """
-    This app is a demonstration of loading Web Map Service (WMS) layers. Simply enter the URL of the WMS service 
-    in the text box below and press Enter to retrieve the layers. Go to https://apps.nationalmap.gov/services to find 
-    some WMS URLs if needed.
-    """
-    )
-
-    row1_col1, row1_col2 = st.columns([3, 1.3])
-    width = 800
-    height = 600
-    layers = None
-
-    with row1_col2:
-
-        esa_landcover = "https://services.terrascope.be/wms/v2"
-        url = st.text_input(
-            "Enter a WMS URL:", value="https://services.terrascope.be/wms/v2"
-        )
-        empty = st.empty()
-
-        if url:
-            options = get_layers(url)
-
-            default = None
-            if url == esa_landcover:
-                default = "WORLDCOVER_2020_MAP"
-            layers = empty.multiselect(
-                "Select WMS layers to add to the map:", options, default=default
-            )
-            add_legend = st.checkbox("Add a legend to the map", value=True)
-            if default == "WORLDCOVER_2020_MAP":
-                legend = str(leafmap.builtin_legends["ESA_WorldCover"])
-            else:
-                legend = ""
-            if add_legend:
-                legend_text = st.text_area(
-                    "Enter a legend as a dictionary {label: color}",
-                    value=legend,
-                    height=200,
-                )
-
-        with row1_col1:
-            m = leafmap.Map(center=(36.3, 0), zoom=2)
-
-            if layers is not None:
-                for layer in layers:
-                    m.add_wms_layer(
-                        url, layers=layer, name=layer, attribution=" ", transparent=True
-                    )
-            if add_legend and legend_text:
-                legend_dict = ast.literal_eval(legend_text)
-                m.add_legend(legend_dict=legend_dict)
-
-            m.to_streamlit(width, height)

apps/xy.py

@@ -1,65 +0,0 @@
-import leafmap.foliumap as leafmap
-import pandas as pd
-import streamlit as st
-
-
-def app():
-
-    st.title("Add Points from XY")
-
-    sample_url = "https://raw.githubusercontent.com/giswqs/leafmap/master/examples/data/world_cities.csv"
-    url = st.text_input("Enter URL:", sample_url)
-    m = leafmap.Map(locate_control=True, plugin_LatLngPopup=False)
-
-    if url:
-
-        try:
-            df = pd.read_csv(url)
-
-            columns = df.columns.values.tolist()
-            row1_col1, row1_col2, row1_col3, row1_col4, row1_col5 = st.columns(
-                [1, 1, 3, 1, 1]
-            )
-
-            lon_index = 0
-            lat_index = 0
-
-            for col in columns:
-                if col.lower() in ["lon", "longitude", "long", "lng"]:
-                    lon_index = columns.index(col)
-                elif col.lower() in ["lat", "latitude"]:
-                    lat_index = columns.index(col)
-
-            with row1_col1:
-                x = st.selectbox("Select longitude column", columns, lon_index)
-
-            with row1_col2:
-                y = st.selectbox("Select latitude column", columns, lat_index)
-
-            with row1_col3:
-                popups = st.multiselect("Select popup columns", columns, columns)
-
-            with row1_col4:
-                heatmap = st.checkbox("Add heatmap")
-
-            if heatmap:
-                with row1_col5:
-                    if "pop_max" in columns:
-                        index = columns.index("pop_max")
-                    else:
-                        index = 0
-                    heatmap_col = st.selectbox("Select heatmap column", columns, index)
-                    try:
-                        m.add_heatmap(df, y, x, heatmap_col)
-                    except:
-                        st.error("Please select a numeric column")
-
-            try:
-                m.add_points_from_xy(df, x, y, popups)
-            except:
-                st.error("Please select a numeric column")
-
-        except Exception as e:
-            st.error(e)
-
-    m.to_streamlit()

environment-bk.yml

@@ -1,17 +0,0 @@
-name: geo
-channels:
-  - conda-forge
-dependencies:
-  - gdal=3.4.3
-  - pip
-  - pip:
-      - geopandas
-      - keplergl
-      - streamlit
-      - localtileserver
-      - palettable
-      - streamlit-folium
-      - streamlit-keplergl
-      - streamlit-bokeh-events
-      - git+https://github.com/giswqs/leafmap
-      - git+https://github.com/giswqs/geemap

index.html

@@ -1,39 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head>
-    <title>Streamlit for Geospatial</title>
-    <style type="text/css">
-      html {
-        overflow: auto;
-      }
-      html,
-      body,
-      div,
-      iframe {
-        margin: 0px;
-        padding: 0px;
-        height: 100%;
-        border: none;
-      }
-      iframe {
-        display: block;
-        width: 100%;
-        border: none;
-        overflow-y: auto;
-        overflow-x: hidden;
-      }
-    </style>
-  </head>
-  <body>
-    <iframe
-      src="https://share.streamlit.io/giswqs/streamlit-geospatial/app.py"
-      frameborder="0"
-      marginheight="0"
-      marginwidth="0"
-      width="100%"
-      height="100%"
-      scrolling="auto"
-    >
-    </iframe>
-  </body>
-</html>

multiapp.py

@@ -1,81 +0,0 @@
-"""Frameworks for running multiple Streamlit applications as a single app.
-"""
-import streamlit as st
-
-# app_state = st.experimental_get_query_params()
-# app_state = {k: v[0] if isinstance(v, list) else v for k, v in app_state.items()} # fetch the first item in each query string as we don't have multiple values for each query string key in this example
-
-
-class MultiApp:
-    """Framework for combining multiple streamlit applications.
-    Usage:
-        def foo():
-            st.title("Hello Foo")
-        def bar():
-            st.title("Hello Bar")
-        app = MultiApp()
-        app.add_app("Foo", foo)
-        app.add_app("Bar", bar)
-        app.run()
-    It is also possible keep each application in a separate file.
-        import foo
-        import bar
-        app = MultiApp()
-        app.add_app("Foo", foo.app)
-        app.add_app("Bar", bar.app)
-        app.run()
-    """
-
-    def __init__(self):
-        self.apps = []
-
-    def add_app(self, title, func):
-        """Adds a new application.
-        Parameters
-        ----------
-        func:
-            the python function to render this app.
-        title:
-            title of the app. Appears in the dropdown in the sidebar.
-        """
-        self.apps.append({"title": title, "function": func})
-
-    def run(self):
-        app_state = st.experimental_get_query_params()
-        app_state = {
-            k: v[0] if isinstance(v, list) else v for k, v in app_state.items()
-        }  # fetch the first item in each query string as we don't have multiple values for each query string key in this example
-
-        # st.write('before', app_state)
-
-        titles = [a["title"] for a in self.apps]
-        functions = [a["function"] for a in self.apps]
-        default_radio = titles.index(app_state["page"]) if "page" in app_state else 0
-
-        st.sidebar.title("Navigation")
-
-        title = st.sidebar.radio("Go To", titles, index=default_radio, key="radio")
-
-        app_state["page"] = st.session_state.radio
-        # st.write('after', app_state)
-
-        st.experimental_set_query_params(**app_state)
-        # st.experimental_set_query_params(**st.session_state.to_dict())
-        functions[titles.index(title)]()
-
-        st.sidebar.title("Contribute")
-        st.sidebar.info(
-            "This is an open source project and you are very welcome to contribute your "
-            "comments, questions, resources and apps as "
-            "[issues](https://github.com/giswqs/streamlit-geospatial/issues) or "
-            "[pull requests](https://github.com/giswqs/streamlit-geospatial/pulls) "
-            "to the [source code](https://github.com/giswqs/streamlit-geospatial). "
-        )
-        st.sidebar.title("About")
-        st.sidebar.info(
-            """
-            This web [app](https://share.streamlit.io/giswqs/streamlit-geospatial/app.py) is maintained by [Qiusheng Wu](https://wetlands.io). You can follow me on social media:
-             [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu).
-            This web app URL: <https://streamlit.gishub.org>
-        """
-        )

pages/10_🌍_Earth_Engine_Datasets.py

@@ -1,4 +1,5 @@
 import ee
+import json
 import streamlit as st
 import geemap.foliumap as geemap
 
@@ -79,8 +80,7 @@ def search_data():
 
     dataset = None
     with col2:
-        keyword = st.text_input(
-            "Enter a keyword to search (e.g., elevation)", "")
+        keyword = st.text_input("Enter a keyword to search (e.g., elevation)", "")
         if keyword:
             ee_assets = geemap.search_ee_data(keyword)
             asset_titles = [x["title"] for x in ee_assets]
@@ -102,8 +102,7 @@ def search_data():
                 with st.expander("Show dataset details", True):
                     index = asset_titles.index(dataset)
 
-                    html = geemap.ee_data_html(
-                        st.session_state["ee_assets"][index])
+                    html = geemap.ee_data_html(st.session_state["ee_assets"][index])
                     html = html.replace("\n", "")
                     st.markdown(html, True)
 
@@ -111,6 +110,14 @@ def search_data():
                 uid = ee_assets[index]["uid"]
                 st.markdown(f"""**Earth Engine Snippet:** `{ee_id}`""")
                 ee_asset = f"{translate[asset_types[index]]}{ee_id}')"
+
+                if ee_asset.startswith("ee.ImageCollection"):
+                    ee_asset = ee.ImageCollection(ee_id)
+                elif ee_asset.startswith("ee.Image"):
+                    ee_asset = ee.Image(ee_id)
+                elif ee_asset.startswith("ee.FeatureCollection"):
+                    ee_asset = ee.FeatureCollection(ee_id)
+
                 vis_params = st.text_input(
                     "Enter visualization parameters as a dictionary", {}
                 )
@@ -122,12 +129,11 @@ def search_data():
                         if vis_params.strip() == "":
                             # st.error("Please enter visualization parameters")
                             vis_params = "{}"
-                        vis = eval(vis_params)
+                        vis = json.loads(vis_params.replace("'", '"'))
                         if not isinstance(vis, dict):
-                            st.error(
-                                "Visualization parameters must be a dictionary")
+                            st.error("Visualization parameters must be a dictionary")
                         try:
-                            Map.addLayer(eval(ee_asset), vis, layer_name)
+                            Map.addLayer(ee_asset, vis, layer_name)
                         except Exception as e:
                             st.error(f"Error adding layer: {e}")
                     except Exception as e:
@@ -143,8 +149,7 @@ def search_data():
 def app():
     st.title("Earth Engine Data Catalog")
 
-    apps = ["Search Earth Engine Data Catalog",
-            "National Land Cover Database (NLCD)"]
+    apps = ["Search Earth Engine Data Catalog", "National Land Cover Database (NLCD)"]
 
     selected_app = st.selectbox("Select an app", apps)
 

pages/13_🏘️_Global_Building_Footprints.py

@@ -6,7 +6,6 @@ import streamlit as st
 st.set_page_config(layout="wide")
 
 
-@st.cache(persist=True)
 def ee_authenticate(token_name="EARTHENGINE_TOKEN"):
     geemap.ee_initialize(token_name=token_name)
 
@@ -30,25 +29,26 @@ st.title("Global Building Footprints")
 col1, col2 = st.columns([8, 2])
 
 
-@st.cache(allow_output_mutation=True)
+@st.cache_data
 def read_data(url):
     return gpd.read_file(url)
 
 
-countries = 'https://github.com/giswqs/geemap/raw/master/examples/data/countries.geojson'
-states = 'https://github.com/giswqs/geemap/raw/master/examples/data/us_states.json'
+countries = (
+    "https://github.com/giswqs/geemap/raw/master/examples/data/countries.geojson"
+)
+states = "https://github.com/giswqs/geemap/raw/master/examples/data/us_states.json"
 
 countries_gdf = read_data(countries)
 states_gdf = read_data(states)
 
-country_names = countries_gdf['NAME'].values.tolist()
-country_names.remove('United States of America')
-country_names.append('USA')
+country_names = countries_gdf["NAME"].values.tolist()
+country_names.remove("United States of America")
+country_names.append("USA")
 country_names.sort()
-country_names = [name.replace('.', '').replace(' ', '_')
-                 for name in country_names]
+country_names = [name.replace(".", "").replace(" ", "_") for name in country_names]
 
-state_names = states_gdf['name'].values.tolist()
+state_names = states_gdf["name"].values.tolist()
 
 basemaps = list(geemap.basemaps)
 
@@ -56,41 +56,44 @@ Map = geemap.Map()
 
 with col2:
 
-    basemap = st.selectbox("Select a basemap", basemaps,
-                           index=basemaps.index('HYBRID'))
+    basemap = st.selectbox("Select a basemap", basemaps, index=basemaps.index("HYBRID"))
     Map.add_basemap(basemap)
 
-    country = st.selectbox('Select a country', country_names,
-                           index=country_names.index('USA'))
+    country = st.selectbox(
+        "Select a country", country_names, index=country_names.index("USA")
+    )
 
-    if country == 'USA':
-        state = st.selectbox('Select a state', state_names,
-                             index=state_names.index('Florida'))
+    if country == "USA":
+        state = st.selectbox(
+            "Select a state", state_names, index=state_names.index("Florida")
+        )
         layer_name = state
 
         try:
             fc = ee.FeatureCollection(
-                f'projects/sat-io/open-datasets/MSBuildings/US/{state}')
+                f"projects/sat-io/open-datasets/MSBuildings/US/{state}"
+            )
         except:
-            st.error('No data available for the selected state.')
+            st.error("No data available for the selected state.")
 
     else:
         try:
             fc = ee.FeatureCollection(
-                f'projects/sat-io/open-datasets/MSBuildings/{country}')
+                f"projects/sat-io/open-datasets/MSBuildings/{country}"
+            )
         except:
-            st.error('No data available for the selected country.')
+            st.error("No data available for the selected country.")
 
         layer_name = country
 
-    color = st.color_picker('Select a color', '#FF5500')
+    color = st.color_picker("Select a color", "#FF5500")
 
-    style = {'fillColor': '00000000', 'color': color}
+    style = {"fillColor": "00000000", "color": color}
 
     split = st.checkbox("Split-panel map")
 
     if split:
-        left = geemap.ee_tile_layer(fc.style(**style), {}, 'Left')
+        left = geemap.ee_tile_layer(fc.style(**style), {}, "Left")
         right = left
         Map.split_map(left, right)
     else:

pages/1_📷_Timelapse.py

@@ -1,4 +1,5 @@
 import ee
+import json
 import os
 import warnings
 import datetime
@@ -15,7 +16,7 @@ st.set_page_config(layout="wide")
 warnings.filterwarnings("ignore")
 
 
-@st.cache(allow_output_mutation=True)
+@st.cache_data
 def ee_authenticate(token_name="EARTHENGINE_TOKEN"):
     geemap.ee_initialize(token_name=token_name)
 
@@ -205,7 +206,7 @@ ocean_rois = {
 }
 
 
-@st.cache(allow_output_mutation=True)
+@st.cache_data
 def uploaded_file_to_gdf(data):
     import tempfile
     import os
@@ -235,8 +236,8 @@ def app():
 
     st.markdown(
         """
-        An interactive web app for creating [Landsat](https://developers.google.com/earth-engine/datasets/catalog/landsat)/[GOES](https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16) timelapse for any location around the globe. 
-        The app was built using [streamlit](https://streamlit.io), [geemap](https://geemap.org), and [Google Earth Engine](https://earthengine.google.com). For more info, check out my streamlit [blog post](https://blog.streamlit.io/creating-satellite-timelapse-with-streamlit-and-earth-engine). 
+        An interactive web app for creating [Landsat](https://developers.google.com/earth-engine/datasets/catalog/landsat)/[GOES](https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16) timelapse for any location around the globe.
+        The app was built using [streamlit](https://streamlit.io), [geemap](https://geemap.org), and [Google Earth Engine](https://earthengine.google.com). For more info, check out my streamlit [blog post](https://blog.streamlit.io/creating-satellite-timelapse-with-streamlit-and-earth-engine).
     """
     )
 
@@ -376,7 +377,9 @@ def app():
                         st.write(
                             cm.plot_colormap(cmap=palette_options, return_fig=True)
                         )
-                        st.session_state["palette"] = eval(palette)
+                        st.session_state["palette"] = json.loads(
+                            palette.replace("'", '"')
+                        )
 
                     if bands:
                         vis_params = st.text_area(
@@ -391,7 +394,9 @@ def app():
                             "{}",
                         )
                     try:
-                        st.session_state["vis_params"] = eval(vis_params)
+                        st.session_state["vis_params"] = json.loads(
+                            vis_params.replace("'", '"')
+                        )
                         st.session_state["vis_params"]["palette"] = st.session_state[
                             "palette"
                         ]
@@ -412,13 +417,13 @@ def app():
             MODIS_options = ["Daytime (1:30 pm)", "Nighttime (1:30 am)"]
             MODIS_option = st.selectbox("Select a MODIS dataset:", MODIS_options)
             if MODIS_option == "Daytime (1:30 pm)":
-                st.session_state[
-                    "ee_asset_id"
-                ] = "projects/sat-io/open-datasets/gap-filled-lst/gf_day_1km"
+                st.session_state["ee_asset_id"] = (
+                    "projects/sat-io/open-datasets/gap-filled-lst/gf_day_1km"
+                )
             else:
-                st.session_state[
-                    "ee_asset_id"
-                ] = "projects/sat-io/open-datasets/gap-filled-lst/gf_night_1km"
+                st.session_state["ee_asset_id"] = (
+                    "projects/sat-io/open-datasets/gap-filled-lst/gf_night_1km"
+                )
 
             palette_options = st.selectbox(
                 "Color palette",
@@ -431,7 +436,7 @@ def app():
                 palette_values,
             )
             st.write(cm.plot_colormap(cmap=palette_options, return_fig=True))
-            st.session_state["palette"] = eval(palette)
+            st.session_state["palette"] = json.loads(palette.replace("'", '"'))
         elif collection == "MODIS Ocean Color SMI":
             with st.expander("Show dataset details", False):
                 st.markdown(
@@ -489,7 +494,7 @@ def app():
                 palette_values,
             )
             st.write(cm.plot_colormap(cmap=palette_options, return_fig=True))
-            st.session_state["palette"] = eval(palette)
+            st.session_state["palette"] = json.loads(palette.replace("'", '"'))
 
         sample_roi = st.selectbox(
             "Select a sample ROI or upload a GeoJSON file:",
@@ -1341,7 +1346,9 @@ def app():
                                 if vis_params.startswith("{") and vis_params.endswith(
                                     "}"
                                 ):
-                                    vis_params = eval(vis_params)
+                                    vis_params = json.loads(
+                                        vis_params.replace("'", '"')
+                                    )
                                 else:
                                     vis_params = None
                                 out_gif = geemap.modis_ocean_color_timelapse(

pages/2_🏠_U.S._Housing.py

@@ -95,7 +95,7 @@ def get_data_columns(df, category, frequency="monthly"):
         return cols[1:]
 
 
-@st.cache(allow_output_mutation=True)
+@st.cache_data
 def get_inventory_data(url):
     df = pd.read_csv(url)
     url = url.lower()
@@ -139,7 +139,7 @@ def get_periods(df):
     return [str(d) for d in list(set(df["month_date_yyyymm"].tolist()))]
 
 
-@st.cache(allow_output_mutation=True)
+@st.cache_data
 def get_geom_data(category):
 
     prefix = (
@@ -224,7 +224,7 @@ def app():
     st.title("U.S. Real Estate Data and Market Trends")
     st.markdown(
         """**Introduction:** This interactive dashboard is designed for visualizing U.S. real estate data and market trends at multiple levels (i.e., national,
-         state, county, and metro). The data sources include [Real Estate Data](https://www.realtor.com/research/data) from realtor.com and 
+         state, county, and metro). The data sources include [Real Estate Data](https://www.realtor.com/research/data) from realtor.com and
          [Cartographic Boundary Files](https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html) from U.S. Census Bureau.
          Several open-source packages are used to process the data and generate the visualizations, e.g., [streamlit](https://streamlit.io),
           [geopandas](https://geopandas.org), [leafmap](https://leafmap.org), and [pydeck](https://deckgl.readthedocs.io).

pages/3_🪟_Split_Map.py

@@ -23,8 +23,8 @@ with st.expander("See source code"):
     with st.echo():
         m = leafmap.Map()
         m.split_map(
-            left_layer='ESA WorldCover 2020 S2 FCC', right_layer='ESA WorldCover 2020'
+            left_layer="ESA WorldCover 2020 S2 FCC", right_layer="ESA WorldCover 2020"
         )
-        m.add_legend(title='ESA Land Cover', builtin_legend='ESA_WorldCover')
+        m.add_legend(title="ESA Land Cover", builtin_legend="ESA_WorldCover")
 
 m.to_streamlit(height=700)

pages/4_🔥_Heatmap.py

@@ -22,7 +22,7 @@ st.title("Heatmap")
 with st.expander("See source code"):
     with st.echo():
         filepath = "https://raw.githubusercontent.com/giswqs/leafmap/master/examples/data/us_cities.csv"
-        m = leafmap.Map(center=[40, -100], zoom=4, tiles="stamentoner")
+        m = leafmap.Map(center=[40, -100], zoom=4)
         m.add_heatmap(
             filepath,
             latitude="latitude",

pages/5_📍_Marker_Cluster.py

@@ -23,16 +23,16 @@ with st.expander("See source code"):
     with st.echo():
 
         m = leafmap.Map(center=[40, -100], zoom=4)
-        cities = 'https://raw.githubusercontent.com/giswqs/leafmap/master/examples/data/us_cities.csv'
-        regions = 'https://raw.githubusercontent.com/giswqs/leafmap/master/examples/data/us_regions.geojson'
+        cities = "https://raw.githubusercontent.com/giswqs/leafmap/master/examples/data/us_cities.csv"
+        regions = "https://raw.githubusercontent.com/giswqs/leafmap/master/examples/data/us_regions.geojson"
 
-        m.add_geojson(regions, layer_name='US Regions')
+        m.add_geojson(regions, layer_name="US Regions")
         m.add_points_from_xy(
             cities,
             x="longitude",
             y="latitude",
-            color_column='region',
-            icon_names=['gear', 'map', 'leaf', 'globe'],
+            color_column="region",
+            icon_names=["gear", "map", "leaf", "globe"],
             spin=True,
             add_legend=True,
         )

pages/6_🗺️_Basemaps.py

@@ -22,7 +22,7 @@ def app():
     st.title("Search Basemaps")
     st.markdown(
         """
-    This app is a demonstration of searching and loading basemaps from [xyzservices](https://github.com/geopandas/xyzservices) and [Quick Map Services (QMS)](https://github.com/nextgis/quickmapservices). Selecting from 1000+ basemaps with a few clicks.  
+    This app is a demonstration of searching and loading basemaps from [xyzservices](https://github.com/geopandas/xyzservices) and [Quick Map Services (QMS)](https://github.com/nextgis/quickmapservices). Selecting from 1000+ basemaps with a few clicks.
     """
     )
 
@@ -47,8 +47,7 @@ def app():
                 if qms is not None:
                     options = options + qms
 
-            tiles = empty.multiselect(
-                "Select XYZ tiles to add to the map:", options)
+            tiles = empty.multiselect("Select XYZ tiles to add to the map:", options)
 
         with row1_col1:
             m = leafmap.Map()

pages/7_📦_Web_Map_Service.py

@@ -1,4 +1,5 @@
 import ast
+import json
 import streamlit as st
 import leafmap.foliumap as leafmap
 
@@ -18,19 +19,29 @@ st.sidebar.info(
     """
 )
 
+# Define a whitelist of trusted URLs
+trusted_urls = [
+    "https://services.terrascope.be/wms/v2",
+    # Add more trusted URLs here
+]
 
-@st.cache(allow_output_mutation=True)
+
+@st.cache_data
 def get_layers(url):
     options = leafmap.get_wms_layers(url)
     return options
 
 
+def is_trusted_url(url):
+    return url in trusted_urls
+
+
 def app():
     st.title("Web Map Service (WMS)")
     st.markdown(
         """
-    This app is a demonstration of loading Web Map Service (WMS) layers. Simply enter the URL of the WMS service 
-    in the text box below and press Enter to retrieve the layers. Go to https://apps.nationalmap.gov/services to find 
+    This app is a demonstration of loading Web Map Service (WMS) layers. Simply enter the URL of the WMS service
+    in the text box below and press Enter to retrieve the layers. Go to https://apps.nationalmap.gov/services to find
     some WMS URLs if needed.
     """
     )
@@ -49,7 +60,14 @@ def app():
         empty = st.empty()
 
         if url:
-            options = get_layers(url)
+
+            if is_trusted_url(url):
+                options = get_layers(url)
+                # Process options as needed
+            else:
+                st.error(
+                    "The entered URL is not trusted. Please enter a valid WMS URL."
+                )
 
             default = None
             if url == esa_landcover:
@@ -78,7 +96,7 @@ def app():
                         url, layers=layer, name=layer, attribution=" ", transparent=True
                     )
             if add_legend and legend_text:
-                legend_dict = ast.literal_eval(legend_text)
+                legend_dict = json.loads(legend_text.replace("'", '"'))
                 m.add_legend(legend_dict=legend_dict)
 
             m.to_streamlit(height=height)

pages/8_🏜️_Raster_Data_Visualization.py

@@ -1,3 +1,4 @@
+import json
 import os
 import leafmap.foliumap as leafmap
 import leafmap.colormaps as cm
@@ -20,7 +21,7 @@ st.sidebar.info(
 )
 
 
-@st.cache(allow_output_mutation=True)
+@st.cache_data
 def load_cog_list():
     print(os.getcwd())
     in_txt = os.path.join(os.getcwd(), "data/cog_files.txt")
@@ -28,7 +29,7 @@ def load_cog_list():
         return [line.strip() for line in f.readlines()[1:]]
 
 
-@st.cache(allow_output_mutation=True)
+@st.cache_data
 def get_palettes():
     return list(cm.palettes.keys())
     # palettes = dir(palettable.matplotlib)[:-16]
@@ -44,6 +45,14 @@ An interactive web app for visualizing local raster datasets and Cloud Optimized
 """
 )
 
+
+def is_trusted_url(url):
+    if url.startswith("https://opendata.digitalglobe.com/events/california-fire-2020/"):
+        return True
+    else:
+        return False
+
+
 row1_col1, row1_col2 = st.columns([2, 1])
 
 with row1_col1:
@@ -58,7 +67,7 @@ with row1_col2:
         cog,
     )
 
-    if url:
+    if is_trusted_url(url):
         try:
             options = leafmap.cog_bands(url)
         except Exception as e:
@@ -73,6 +82,8 @@ with row1_col2:
             pass
         else:
             st.error("Please select one or three bands")
+    else:
+        st.error("Please enter a trusted URL")
 
     add_params = st.checkbox("Add visualization parameters")
     if add_params:
@@ -82,7 +93,7 @@ with row1_col2:
 
     if len(vis_params) > 0:
         try:
-            vis_params = eval(vis_params)
+            vis_params = json.loads(vis_params.replace("'", '"'))
         except Exception as e:
             st.error(
                 f"Invalid visualization parameters. It should be a dictionary. Error: {e}"

pages/9_🔲_Vector_Data_Visualization.py

@@ -20,6 +20,17 @@ st.sidebar.info(
 )
 
 
+# Define a whitelist of trusted URLs
+trusted_urls = [
+    "https://github.com/giswqs/streamlit-geospatial/raw/master/data/us_states.geojson",
+    # Add more trusted URLs here
+]
+
+
+def is_trusted_url(url):
+    return url in trusted_urls
+
+
 def save_uploaded_file(file_content, file_name):
     """
     Save the uploaded file to a temporary directory
@@ -70,7 +81,7 @@ def app():
 
         container = st.container()
 
-        if data or url:
+        if data or is_trusted_url(url):
             if data:
                 file_path = save_uploaded_file(data, data.name)
                 layer_name = os.path.splitext(data.name)[0]

postBuild

@@ -1,6 +0,0 @@
-# enable nbserverproxy
-jupyter serverextension enable --sys-prefix nbserverproxy
-# streamlit launches at startup
-mv streamlit_call.py ${NB_PYTHON_PREFIX}/lib/python*/site-packages/
-# enable streamlit extension
-jupyter serverextension enable --sys-prefix streamlit_call

requirements.txt

@@ -1,24 +1,25 @@
 --find-links=https://girder.github.io/large_image_wheels GDAL
 # cartopy
-folium==0.13.0
+fiona
+folium #==0.13.0
 # ipywidgets<8.0.5
-geemap
+# geemap
 ffmpeg-python
 geopandas
 # jupyter-server-proxy
 # keplergl
-leafmap>=0.18.6
+leafmap>=0.35.2
 # localtileserver
 # nbserverproxy
 owslib
 palettable
 plotly
 streamlit
-streamlit-bokeh-events
+# streamlit-bokeh-events
 streamlit-folium
 # streamlit-keplergl
 # tropycal
 # git+https://github.com/giswqs/leafmap
-# git+https://github.com/giswqs/geemap
-altair<5
+git+https://github.com/giswqs/geemap
+# altair<5
 

setup.sh

@@ -1,18 +0,0 @@
-# sudo add-apt-repository ppa:ubuntugis/ppa && sudo apt-get update
-# sudo apt-get update
-# sudo apt-get install python3-dev
-# sudo apt-get install gdal-bin
-# sudo apt-get install libgdal-dev
-# export CPLUS_INCLUDE_PATH=/usr/include/gdal
-# export C_INCLUDE_PATH=/usr/include/gdal
-# gdal-config --version
-# pip install GDAL==$(gdal-config --version | awk -F'[.]' '{print $1"."$2}') localtileserver
-
-mkdir -p ~/.streamlit/
-echo "\
-[server]\n\
-headless = true\n\
-port = $PORT\n\
-enableCORS = false\n\
-\n\
-" > ~/.streamlit/config.toml

streamlit_app.py

@@ -1,43 +0,0 @@
-import streamlit as st
-import leafmap.foliumap as leafmap
-
-st.set_page_config(layout="wide")
-
-st.sidebar.info(
-    """
-    - Web App URL: <https://streamlit.gishub.org>
-    - GitHub repository: <https://github.com/giswqs/streamlit-geospatial>
-    """
-)
-
-st.sidebar.title("Contact")
-st.sidebar.info(
-    """
-    Qiusheng Wu at [wetlands.io](https://wetlands.io) | [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
-    """
-)
-
-# Customize page title
-st.title("Streamlit for Geospatial Applications")
-
-st.markdown(
-    """
-    This multipage app template demonstrates various interactive web apps created using [streamlit](https://streamlit.io) and [leafmap](https://leafmap.org). It is an open-source project and you are very welcome to contribute to the [GitHub repository](https://github.com/giswqs/streamlit-multipage-template).
-    """
-)
-
-st.header("Instructions")
-
-markdown = """
-1. For the [GitHub repository](https://github.com/giswqs/streamlit-multipage-template) or [use it as a template](https://github.com/giswqs/streamlit-multipage-template/generate) for your own project.
-2. Customize the sidebar by changing the sidebar text and logo in each Python files.
-3. Find your favorite emoji from https://emojipedia.org.
-4. Add a new app to the `pages/` directory with an emoji in the file name, e.g., `1_🚀_Chart.py`.
-
-"""
-
-st.markdown(markdown)
-
-m = leafmap.Map(minimap_control=True)
-m.add_basemap("OpenTopoMap")
-m.to_streamlit(height=500)

streamlit_call.py

@@ -1,14 +0,0 @@
-from subprocess import Popen
-
-
-def load_jupyter_server_extension(nbapp):
-    """serve the streamlit app"""
-    Popen(
-        [
-            "streamlit",
-            "run",
-            "Home.py",
-            "--browser.serverAddress=0.0.0.0",
-            "--server.enableCORS=False",
-        ]
-    )