app.py

"""
Original Project Author: IndigoWizard, August 6, 2023.
Project Name: NDVI Viewer
"""
import streamlit as st
import ee
from ee import oauth
from google.oauth2 import service_account
import folium
from streamlit_folium import folium_static, st_folium
from branca.element import Template, MacroElement, Figure, Element
from folium.utilities import escape_backticks
from datetime import datetime, timedelta, date
import json
import pandas as pd
import geopandas as gpd
import calendar
import altair as alt
import tempfile
import zipfile
import os
import xml.etree.ElementTree as ET
import fiona
from shapely.geometry import shape, mapping

st.set_page_config(
    page_title="NDVI Viewer",
    page_icon="https://cdn-icons-png.flaticon.com/512/2516/2516640.png",
    layout="wide",
    initial_sidebar_state="expanded",
    menu_items={
    'Get help': "https://github.com/IndigoWizard/NDVI-Viewer",
    'Report a bug': "https://github.com/IndigoWizard/NDVI-Viewer/issues",
    'About': "This app was developped by [IndigoWizard](https://github.com/IndigoWizard/NDVI-Viewer) for the purpose of environmental monitoring and geospatial analysis. Give proper credit when using this app or forking the open source projet code/piece of code."
    }
)

### CSS STYLING 
st.markdown(
"""
<style>
    /* Header*/
    /* Dark theme version */
    .st-emotion-cache-h4xjwg, .st-emotion-cache-12fmjuu {
        height: 1rem;
        background: none;
    }
    /*Header banner*/
    .st-emotion-cache-ropwps.egexzqm2 h1#wildfire-burn-severity-analysis {
        font-size: 1.75rem;
    }

    /*Main: Smooth scrolling*/
    .stMain.st-emotion-cache-bm2z3a.eht7o1d1 {
        scroll-behavior: smooth;
    }
    
    /* main app body with less padding*/
    .st-emotion-cache-t1wise.eht7o1d4 {
        padding: 0.2rem 2rem;
    }

    /* main app body with less padding in different screen size*/
    @media (min-width: calc(736px + 8rem)) {
        .st-emotion-cache-t1wise {
            padding: 0.2rem 2rem;
        }
    }

    /* ******* Sidebar ******* */
    /* Main container */
    /*Dark theme - Light theme class names*/
    .stSidebar.st-emotion-cache-1wqrzgl.e1c29vlm0, .stSidebar.st-emotion-cache-vmpjyt.e1c29vlm0 {
        min-width: 280px;
        max-width: fit-content;
    }

    /*Light theme sidbar background color*/
    .stSidebar.st-emotion-cache-vmpjyt, .stSidebar.st-emotion-cache-wgfafi.e1c29vlm0 {
        background-color: rgb(38, 39, 48);
        color: #fafafa;
    }
    /*sidebar light theme mobile view*/

    @media (max-width: 576px) {
        .stSidebar.st-emotion-cache-g8bi16.e1c29vlm0 {
            background-color: rgb(38, 39, 48);
            color: #fafafa;
        }
        .stVerticalBlock.st-emotion-cache-10e86g4.e6rk8up3, .stVerticalBlock.st-emotion-cache-1vn87qs.e6rk8up3 {
            gap: 1.6rem;
        }
    }


    /*Sidebar header*/
    .st-emotion-cache-kgpedg {
        padding: 0;
    }
    .st-emotion-cache-1mi2ry5.eczjsme6 {
        height: 0;
    }

    /* Logo */
    .st-emotion-cache-1kyxreq.e115fcil2 {
        justify-content: center;
    }

    /* Sidebar : inside container */
    .css-ge7e53 {
        width: fit-content;
    }

    /*Sidebar : image*/
    .st-emotion-cache-vew1uq.e6rk8up1 {
        display: flex;
        justify-content: center;
    }

    /*Sidebar : Navigation list*/
    div.element-container:nth-child(4) > div:nth-child(1) > div:nth-child(1) > ul:nth-child(1) {
        margin: 0;
        padding: 0;
        list-style: none;
    }
    div.element-container:nth-child(4) > div:nth-child(1) > div:nth-child(1) > ul:nth-child(1) > li {
        padding: 0;
        margin: 0;
        padding: 0;
        font-weight: 600;
    }
    div.element-container:nth-child(4) > div:nth-child(1) > div:nth-child(1) > ul:nth-child(1) > li > a {
        text-decoration: none;
        transition: 0.2s ease-in-out;
        padding-inline: 10px;
    }
    
    div.element-container:nth-child(4) > div:nth-child(1) > div:nth-child(1) > ul:nth-child(1) > li > a:hover {
        color: rgb(46, 206, 255);
        transition: 0.2s ease-in-out;
        background: #131720;
        border-radius: 4px;
    }
    
    /* Sidebar: socials*/
    div.css-rklnmr:nth-child(6) > div:nth-child(1) > div:nth-child(1) > p {
        display: flex;
        flex-direction: row;
        gap: 1rem;
    }

    /*Socials flex properties: dark & light theme*/
    .st-emotion-cache-1espb9k p, .st-emotion-cache-1mw54nq p {
        display: flex;
        flex-direction: row;
        justify-content: start;
        gap: 0.8rem;
        padding-inline: 10px;
    }
    
    /* Linkedin logo*/
    .st-emotion-cache-1espb9k.egexzqm0 p a img, .st-emotion-cache-1mw54nq.egexzqm0 p a img {
        width: 32px;
    }

    /*GitHub logo:  Dark Theme - Light Theme*/
    .st-emotion-cache-14j6x93:nth-child(6) > div:nth-child(1) > div:nth-child(1) > p:nth-child(1) > a:nth-child(2) > img:nth-child(1) {
        background-color: #26273040;
        border-radius: 50%;
    }
    /*GitHub logo:  Dark Theme - Light Theme - Mobile version*/
    div.st-emotion-cache-vew1uq:nth-child(6) > div:nth-child(1) > div:nth-child(1) > p:nth-child(1) > a:nth-child(2) > img:nth-child(1) {
        background-color: #26273040;
        border-radius: 50%;
    }

    /*Main body Title*/
    .st-emotion-cache-ropwps.egexzqm2 h1#wildfire-burn-severity-analysis, .st-emotion-cache-18netey.egexzqm2 h1#wildfire-burn-severity-analysis {
        font-size: 2rem;
        padding: 1.8rem 0 0.5rem;
    }
    
    /* ******* Upload Section ******* */
    /* ***** Upload info box */
    /* Light theme version */
    .st-emotion-cache-1gulkj5.e1blfcsg0 {
        background-color: rgb(215, 210, 225);
        color: rgb(40, 40, 55);
        display: flex;
        flex-direction: column;
        align-items: inherit;
        font-size: 14px;
    }

    /* ***** Upload SVG: Mobile view */
    @media (max-width: 576px) {
        /* Dark theme version*/
        .st-emotion-cache-wn8ljn.e1b2p2ww13 {
            display: unset;
        }

        /* Light theme version*/
        .st-emotion-cache-nwtri.e1b2p2ww13 {
            display: unset;
        }
    }
    
    /* ***** Upload button: dark theme*/
    .st-emotion-cache-1erivf3.e1blfcsg0 {
        display: flex;
        flex-direction: column;
        align-items: inherit;
        font-size: 14px;
    }
    .st-emotion-cache-19rxjzo.ef3psqc12 {
        display: flex;
        flex-direction: row;
        margin-inline: 0;
    }
    
    /* ***** Upload button: light theme*/
    .st-emotion-cache-1gulkj5.e1b2p2ww15 {
        display: flex;
        flex-direction: column;
        align-items: inherit;
        font-size: 14px;
    }

    .st-emotion-cache-7ym5gk.ef3psqc12 {
        display: flex;
        flex-direction: row;
        margin-inline: 0;
        background: rgba(0, 3, 172, 0.15);
    }

    /* ******* Form Submit ******* */
    /* ***** Generate Map */
    /* Dark theme version */
    .st-emotion-cache-19rxjzo.ef3psqc7 {
        width: 100%;
    }
    /* Light Theme Version */
    .st-emotion-cache-7ym5gk.ef3psqc7 {
        width: 100%;
        background: rgba(0, 3, 172, 0.25);
    }

    /* Buttons */
    /* Light theme verison; hober effect */
    .st-emotion-cache-7ym5gk:hover {
        border-color: rgb(255, 0, 110);
        color: rgb(255, 0, 110);
    }

    /* ******* Legend style ******* */

    .ndwilegend {
        transition: 0.2s ease-in-out;
        border-radius: 5px;
        box-shadow: 0 0 5px rgba(0, 0, 0, 0.2);
        background: rgba(0, 0, 0, 0.05);
    }
    .ndwilegend:hover {
        transition: 0.3s ease-in-out;
        box-shadow: 0 0 5px rgba(0, 0, 0, 0.8);
        background: rgba(0, 0, 0, 0.12);
        cursor: pointer;
    }
    .reclassifieddNBR {
        transition: 0.2s ease-in-out;
        border-radius: 5px;
        box-shadow: 0 0 5px rgba(0, 0, 0, 0.2);
        background: rgba(0, 0, 0, 0.05);
    }
    .reclassifieddNBR:hover {
        transition: 0.3s ease-in-out;
        box-shadow: 0 0 5px rgba(0, 0, 0, 0.8);
        background: rgba(0, 0, 0, 0.12);
        cursor: pointer;
    }
    
    .stCustomComponentV1.st-emotion-cache-1tvzk6f.e1begtbc0 {
        width: 100%;
        height: 500px !important;
        min-height: 500px !important;
        max-height: 500px !important;
        overflow: hidden !important;
    }
    
</style>
""", unsafe_allow_html=True)

# Initializing the Earth Engine library
# Use ee.Initialize() only on local machine! Comment back before deployement (Unusable on deployment)
#ee.Initialize()
# GEE Servuce Account Auth+init for cloud deployment
@st.cache_data(persist=True)
def ee_authenticate():
    # Check for json key in Streamlit Secrets
    if "json_key" in st.secrets:
        json_creds = st.secrets["json_key"]
        service_account_info = json.loads(json_creds)
        # Catching eventual email related error
        if "client_email" not in service_account_info:
            raise ValueError("Service account email address missing in json key")
        creds = service_account.Credentials.from_service_account_info(service_account_info, scopes=oauth.SCOPES)
        # Initializing gee for each run of the app
        ee.Initialize(creds)
    else:
        # Fallback to normal init method if no json key/st secrets available. (local machine)
        ee.Initialize()


# Error dialog box
@st.dialog("Error Report:")
def show_error_dialog(messages):
    st.error(messages)


# Earth Engine drawing method setup
def add_ee_layer(self, ee_image_object, vis_params, name):
    map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)
    layer = folium.raster_layers.TileLayer(
        tiles=map_id_dict['tile_fetcher'].url_format,
        attr='Map Data &copy; <a href="https://earthengine.google.com/">Google Earth Engine</a>',
        name=name,
        overlay=True,
        control=True
    )
    layer.add_to(self)
    return layer

# Configuring Earth Engine display rendering method in Folium
folium.Map.add_ee_layer = add_ee_layer


# Defining a function to create and filter a GEE image collection for results
def satCollection(cloudRate, initialDate, updatedDate, aoi):
    try:
        collection = (
            ee.ImageCollection('COPERNICUS/S2_SR')
            .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", cloudRate))
            .filterDate(initialDate, updatedDate)
            .filterBounds(aoi)
            )
            
        # Check collection size
        collection_size = collection.size().getInfo()

        if collection_size == 0:
            return None, (
                "No Sentinel-2 L2A images found for the selected parameters. \n\n"
                "Try increasing the cloud threshold or expanding the date range."
            )

        # Defining a function to clip the colleciton to the area of interst
        def clipCollection(image):
            return image.clip(aoi).divide(10000)
        # clipping the collection
        collection = collection.map(clipCollection)
        
        return collection, None
    
    except Exception as e:
        return collection, f"Earth Engine encountered an error while building Sentinel-2 Collection: \n {str(e)}"


# File Parser: GeoPackage (.gpkg)
def parse_geopackage(gpkg_path):
    # prepare geometry
    geometry_list = []

    try:
        with fiona.open(gpkg_path) as fu:

            if len(fu) == 0:
                return [], "GeoPackage contains no features."

            for feat in fu:
                if not feat or not feat.get("geometry"):
                    continue

                try:
                    geom = shape(feat["geometry"])
                except Exception:
                    continue

                # Polygon
                if geom.geom_type == "Polygon":
                    try:
                        geometry_list.append(ee.Geometry.Polygon(list(geom.exterior.coords)))
                    except Exception:
                        continue

                # MultiPolygon
                elif geom.geom_type == "MultiPolygon":
                    try:
                        coords = [list(poly.exterior.coords) for poly in geom.geoms]
                        geometry_list.append(ee.Geometry.MultiPolygon(coords))
                    except Exception:
                        continue

                # Ignore other geometry types silently

        if not geometry_list:
            return [], "No valid Polygon or MultiPolygon geometries found in GeoPackage."

        return geometry_list, None

    except fiona.errors.DriverError:
        return [], "Invalid or corrupted GeoPackage file."

    except Exception as e:
        return [], f"Error processing GeoPackage file: {str(e)}"


# File Parser: CSV
# column name variations found in CSV datasets 
COLUMN_SYNONYMS = {
    "x": ["x", "ln", "lon", "lons", "lng", "lngs", "longitude", "longitudes"],
    "y": ["y", "lt", "lat", "lats", "latitude", "latitudes"]
}

# finding coordinates colomns
def find_column(df, possible_col_name):
    for c in possible_col_name:
        if c in df.columns:
            return c
    return None

# main csv parse function
def parse_csv(upload_file):
    try:
        try:
            df = pd.read_csv(upload_file)
        except Exception:
            return [], "Invalid CSV file. Could not be read."
        if df.empty:
            return [], "CSV file is empty."

        df.columns = df.columns.str.lower().str.strip()
        geometry_list = []

        # single-row polygon coordinates
        if "coordinates" in df.columns:
            for idx, row in df.iterrows():
                try:
                    coords = json.loads(row["coordinates"])
                    geometry_list.append(ee.Geometry.Polygon(coords))
                except Exception:
                    return [], "Invalid 'coordinates' column. Must contain valid JSON polygon coordinates."
            if not geometry_list:
                return [], "No valid geometries found in 'coordinates' column."
            return geometry_list, None

        # multi-row polygon coordinates
        required_cols = ["id", "vertex_index"]
        for col in required_cols:
            if col not in df.columns:
                return [], f"Missing required column '{col}'."

        x_col = find_column(df, COLUMN_SYNONYMS["x"])
        y_col = find_column(df, COLUMN_SYNONYMS["y"])

        if not x_col or not y_col:
            return [], "Could not detect longitude/latitude columns."

        for gid, group in df.groupby("id"):
            try:
                group = group.sort_values("vertex_index")
                coords = group[[x_col, y_col]].values.tolist()
                
                # checks for minimum polygon vertices
                if len(coords) < 3:
                    return [], f"Polygon with id '{gid}' has fewer than 3 vertices."
                # always check if  the polygon coords close the shape and fix it
                if coords[0] != coords[-1]:
                    coords.append(coords[0])
                geometry_list.append(ee.Geometry.Polygon(coords))

            except Exception:
                return [], f"Invalid polygon geometry for id '{gid}'."

        if not geometry_list:
            return [], "No valid polygon geometries could be constructed from CSV."
        return geometry_list, None

    except Exception as e:
        return [], f"Error processing CSV file: {str(e)}"


# File Parser: Zipped Shapefile (.zip)
def parse_zip_shapefile(upload_file):
    try:
        # creating a temporary directoruy
        with tempfile.TemporaryDirectory() as tmpdir:
            zip_path = os.path.join(tmpdir, "uploaded.zip")

            try:
                # write uploaded file to disk
                with open(zip_path, "wb") as f:
                    f.write(upload_file.read())
            except Exception:
                return [], "Couldn't read uploaded Zip file."

            try:
                # extract zipfile content
                with zipfile.ZipFile(zip_path, "r") as zf:
                    zf.extractall(tmpdir)
            except zipfile.BadZipFile:
                return [], "Not a valid zip file."

            # parse for .shp file within extracted content
            shp_files = [os.path.join(tmpdir, f) for f in os.listdir(tmpdir) if f.endswith(".shp")]
            if not shp_files:
                return [], "Zip file does not contain .SHP file."

            #laod shapefile with geopandas
            try:
                gdf = gpd.read_file(shp_files[0])
            except Exception as e:
                return [], f"Failed to read Shapefile with GeoPandas: Missing one or multiple companion files (.SHX, .DBF, .CPG, .PRJ) {str(e)}"

            if gdf.empty:
                return [], "Shapefile contains no features."

            # convert geometry to match earth engine geometry object (as multipolygon)
            geometry_list = []

            for geom in gdf.geometry:
                try:
                    if geom.geom_type == "Polygon":
                        coords = [list(geom.exterior.coords)]
                        ee_geom = ee.Geometry.Polygon(coords)
                    elif geom.geom_type == "MultiPolygon":
                        coords = [list(p.exterior.coords) for p in geom.geoms]
                        ee_geom = ee.Geometry.MultiPolygon(coords)
                    else:
                        continue  # ignore non-area geometries
                    geometry_list.append(ee_geom)

                except Exception:
                    continue  # skip invalid EE geometries

            if not geometry_list:
                return [], "No valid Polygon or MultiPolygon geometries found in Shapefile."

            return geometry_list, None

    except Exception as e:
        return [], f"Unexpected error while processing Shapefile: {str(e)}"
        


# File Parser: KML (.kml)
def parse_kml(upload_file):
    try:
        upload_file.seek(0)
        try:
            tree = ET.parse(upload_file)
        except ET.ParseError:
            return [], "Invalid KML file. XML could not be parsed."

        # get kml tree structure
        root = tree.getroot()

        # namespace
        ns = {"kml": "http://www.opengis.net/kml/2.2"}

        placemarks = root.findall(".//kml:Placemark", ns)
        if not placemarks:
            return [], "No Placemark elements found in KML file."

        geometry_list = []

        # getting coordinates from placemark in the kml
        for placemark in placemarks:
            coords_elem = placemark.find(".//kml:coordinates", ns)
            if coords_elem is None or not coords_elem.text:
                continue

            try:
                coords_raw = coords_elem.text.strip().split()
                coords = []

                for c in coords_raw:
                    lon, lat, *_ = map(float, c.split(","))
                    coords.append([lon, lat])

                # valid polygon needs at least 3 points
                if len(coords) < 3:
                    continue

                # ensure closed polygon
                if coords[0] != coords[-1]:
                    coords.append(coords[0])

                ee_geom = ee.Geometry.Polygon([coords])
                geometry_list.append(ee_geom)

            except Exception:
                # skip malformed placemark
                continue

        if not geometry_list:
            return [], "No valid polygon geometries could be constructed from KML."

        return geometry_list, None

    except Exception as e:
        return [], f"Error processing KML file: {str(e)}"


# File Parser: TopoJSON (.topojson)
def parse_topojson(upload_file):
    try:
        bytes_data = upload_file.read()

        try:
            topojson_data = json.loads(bytes_data)
        except json.JSONDecodeError:
            return [], "Invalid TopoJSON file. JSON could not be decoded."

        # Check TopoJSON signature
        if topojson_data.get("type") != "Topology":
            return [], "File is not a valid TopoJSON (missing or invalid 'Topology' type)."

        # Manually decode TopoJSON arcs to avoid library performance issues
        # Extract arcs and transform parameters
        arcs = topojson_data.get("arcs")
        objects = topojson_data.get("objects")
        transform = topojson_data.get("transform", {})
        scale = transform.get("scale", [1, 1])
        translate = transform.get("translate", [0, 0])

        if not arcs or not isinstance(arcs, list):
            return [], "TopoJSON file contains no valid arcs."

        if not objects or not isinstance(objects, dict):
            return [], "TopoJSON file contains no objects."


        # Decode arcs from delta-encoded to absolute coordinates
        decoded_arcs = []
        for arc in arcs:
            x, y = 0, 0
            points = []
            for dx, dy in arc:
                x += dx
                y += dy
                lon = x * scale[0] + translate[0]
                lat = y * scale[1] + translate[1]
                points.append([lon, lat])
            decoded_arcs.append(points)

        # Extract geometries from objects
        geometries = []
        for obj in objects.values():
            if obj.get("type") == "GeometryCollection":
                geometries.extend(obj.get("geometries", []))
            else:
                geometries.append(obj)

        if not geometries:
            return [], "TopoJSON file contains no geometries."

        # Convert geometries to Earth Engine geometry objects
        geometry_list = []

        # Build Earth Engine geometries
        for geom_data in geometries:
            geom_type = geom_data.get("type")
            arcs_refs = geom_data.get("arcs")

            if not arcs_refs:
                continue

            try:
                # Polygon
                if geom_type == "Polygon":
                    # Polygon: arcs_refs is a list of arc index lists (one per ring)
                    rings = []
                    for ring_refs in arcs_refs:
                        ring = []
                        for arc_ref in ring_refs:
                            arc_idx = abs(arc_ref)
                            arc_points = (
                                decoded_arcs[arc_idx]
                                if arc_ref >= 0
                                else list(reversed(decoded_arcs[arc_idx]))
                            )
                            ring.extend(arc_points)
                        rings.append(ring)

                    geometry_list.append(ee.Geometry.Polygon(rings))

                # MultiPolygon
                elif geom_type == "MultiPolygon":
                    polygons = []
                    for polygon_refs in arcs_refs:
                        rings = []
                        for ring_refs in polygon_refs:
                            ring = []
                            for arc_ref in ring_refs:
                                arc_idx = abs(arc_ref)
                                arc_points = (
                                    decoded_arcs[arc_idx]
                                    if arc_ref >= 0
                                    else list(reversed(decoded_arcs[arc_idx]))
                                )
                                ring.extend(arc_points)
                            rings.append(ring)
                        polygons.append(rings)

                    geometry_list.append(ee.Geometry.MultiPolygon(polygons))

                # Ignore other geometry types

            except Exception:
                continue

        if not geometry_list:
            return [], "No valid Polygon or MultiPolygon geometries could be constructed from TopoJSON."

        return geometry_list, None

    except Exception as e:
        return [], f"Error processing TopoJSON file: {str(e)}"


# File Parser: GeoJSON (.geojson, .json)
def parse_geojson(upload_file):
    try:
        bytes_data = upload_file.read()
        try:
            geojson_data = json.loads(bytes_data)
        except json.JSONDecodeError:
            return [], "Invalid GeoJSON file. JSON could not be decoded."

        # detect the correct container of features
        if 'features' in geojson_data and isinstance(geojson_data['features'], list):
            features = geojson_data['features']
        elif 'geometries' in geojson_data and isinstance(geojson_data['geometries'], list):
            # Handle GeometryCollection-style structures
            features = [{'geometry': geo} for geo in geojson_data['geometries']]
        else:
            # skip unsupported or invalid GeoJSON
            return [], "Unsupported GeoJSON structure. Must containe a 'features' or 'geometries' field."

        geometry_list = []

        # build Earth Engine geometries
        for feature in features:
            if 'geometry' in feature and 'coordinates' in feature['geometry']:
                coordinates = feature['geometry']['coordinates']
                geometry_type = feature['geometry']['type']

                # Create Polygon or MultiPolygon geometry
                geometry = (
                    ee.Geometry.Polygon(coordinates)
                    if geometry_type == 'Polygon'
                    else ee.Geometry.MultiPolygon(coordinates)
                )

                geometry_list.append(geometry)

        if not geometry_list:
            return [], "No valide geometries in the GeoJSON file. Ensure it contains Polygon or MultiPolygon features."
        return geometry_list, None
    except Exception as e:
        return [], f"Error processing GeoJSON file: {str(e)} Please verify the file is valid."


# Main Upload Function
last_uploaded_centroid = None
def upload_files_proc(upload_files):
    # A global variable to track the latest geojson uploaded
    global last_uploaded_centroid
    # Setting up a variable that takes all polygons/geometries within the same/different geojson
    geometry_aoi_list = []
    # Variable to store all error messages from various parsers
    error_messages = []
    
    for upload_file in upload_files:
        # Get the file name for extension detection
        file_name = getattr(upload_file, 'name').lower()
        # reset file pointer if it was read before
        upload_file.seek(0)

        # File Parser: GeoPackage
        if file_name.endswith(".gpkg"):
            # store gpkg into a temporary file for Fiona
            with tempfile.NamedTemporaryFile(suffix=".gpkg", delete=False) as tmp:
                # write uploaded file content to temp file
                tmp.write(upload_file.getbuffer())
                # write data to disk for readability
                tmp.flush()
                # parse temporary geopackage
                gpkg_geoms, error = parse_geopackage(tmp.name)
            if error:
                error_messages.append(f"→ {file_name}:\n > {error}")
            geometry_aoi_list.extend(gpkg_geoms)
            if gpkg_geoms:
                last_uploaded_centroid = gpkg_geoms[0].centroid(maxError=1).getInfo()["coordinates"]
            continue

        # File Parser: CSV
        if file_name.endswith(".csv"):
            csv_geoms, error = parse_csv(upload_file)
            if error:
                error_messages.append(f"→ {file_name}:\n > {error}")
            geometry_aoi_list.extend(csv_geoms)
            if csv_geoms:
                last_uploaded_centroid = csv_geoms[0].centroid(maxError=1).getInfo()["coordinates"]
            continue

        # File Parser: KML
        if file_name.endswith(".kml"):
            kml_geoms, error = parse_kml(upload_file)
            if error:
                error_messages.append(f"→ {file_name}:\n > {error}")
            geometry_aoi_list.extend(kml_geoms)
            if kml_geoms:
                last_uploaded_centroid = kml_geoms[0].centroid(maxError=1).getInfo()['coordinates']
            continue

        # File Parser: ZIP .Shapefile
        if file_name.endswith(".zip"):
            shp_geoms, error = parse_zip_shapefile(upload_file)
            if error:
                error_messages.append(f"→ {file_name}:\n > {error}")
            geometry_aoi_list.extend(shp_geoms)
            if shp_geoms:
                last_uploaded_centroid = shp_geoms[0].centroid(maxError=1).getInfo()['coordinates']
            continue

        # File Parser: TopoJSON files
        if file_name.endswith(".topojson"):
            topojson_geoms, error = parse_topojson(upload_file)
            if error:
                error_messages.append(f"→ {file_name}:\n > {error}")
            geometry_aoi_list.extend(topojson_geoms)
            if topojson_geoms:
                last_uploaded_centroid = topojson_geoms[0].centroid(maxError=1).getInfo()['coordinates']
            continue

        # File Parser: GeoJSON files
        if file_name.endswith(".geojson") or file_name.endswith(".json"):
            geojson_geoms, error = parse_geojson(upload_file)
            if error:
                error_messages.append(f"→ {file_name}: \n > {error}")
            geometry_aoi_list.extend(geojson_geoms)
            if geojson_geoms:
                last_uploaded_centroid = geojson_geoms[0].centroid(maxError=1).getInfo()['coordinates']
            continue
    
    if error_messages:
        show_error_dialog("\n\n---\n\n".join(error_messages))
    # assembling aoi geometries
    if geometry_aoi_list:
        geometry_aoi = ee.Geometry.MultiPolygon(geometry_aoi_list)
    else:
        geometry_aoi = ee.Geometry.Point([16.25, 36.65])

    return geometry_aoi


# Time input processing function
def date_input_proc(input_date, time_range):
    end_date = input_date
    start_date = input_date - timedelta(days=time_range)
    
    str_start_date = start_date.strftime('%Y-%m-%d')
    str_end_date = end_date.strftime('%Y-%m-%d')
    return str_start_date, str_end_date

# Main function to run the Streamlit app
def main():
    # initiate gee 
    ee_authenticate()

    # Session states
    st.session_state.setdefault("ee_ready", False)

    # sidebar
    with st.sidebar:
        st.title("NDVI Viewer App")
        st.image("https://cdn-icons-png.flaticon.com/512/2516/2516640.png", width=90)
        st.subheader("Navigation:")
        st.markdown(
            """
                - [NDVI Map](#ndvi-viewer)
                - [Map Legend](#map-legend)
                - [Process workflow](#process-workflow-aoi-date-range-and-classification)
                - [Interpreting the Results](#interpreting-the-results)
                - [Environmental Index](#using-an-environmental-index-ndvi)
                - [Data](#data-sentinel-2-imagery-and-l2a-product)
                - [Contribution](#contribute-to-the-app)
                - [About](#about)
                - [Credit](#credit)
            """)
    
        st.subheader("Contact:")
        st.markdown("[![LinkedIn](https://static.licdn.com/sc/h/8s162nmbcnfkg7a0k8nq9wwqo)](https://linkedin.com/in/ahmed-islem-mokhtari) [![GitHub](https://github.githubassets.com/favicons/favicon-dark.png)](https://github.com/IndigoWizard) [![Medium](https://miro.medium.com/1*m-R_BkNf1Qjr1YbyOIJY2w.png)](https://medium.com/@Indigo.Wizard/mt-chenoua-forest-fires-analysis-with-remote-sensing-614681f468e9)")

        st.caption("ʕ •ᴥ•ʔ Star⭐the [project on GitHub](https://github.com/IndigoWizard/NDVI-Viewer/)!")

    with st.container():
        st.title("NDVI Viewer")
        st.markdown("**Monitor Vegetation Health by Viewing & Comparing NDVI Values Through Time and Location with Sentinel-2 Satellite Images on The Fly!**")
    
    # columns for input - map
    with st.form("input_form"):
        c1, c2 = st.columns([3, 1])
        #### User input section - START
        
        with st.container():
            with c2:
            ## Cloud coverage input
                st.info("Cloud Coverage 🌥️")
                cloud_pixel_percentage = st.slider(label="cloud pixel rate", min_value=5, max_value=100, step=5, value=100 , label_visibility="collapsed")

            ## File upload
                # User input GeoJSON file
                st.info("Upload Area Of Interest file:")
                upload_files = st.file_uploader("Crete a GeoJSON file at: [geojson.io](https://geojson.io/)", accept_multiple_files=True)
                # calling upload files function
                geometry_aoi = upload_files_proc(upload_files)
            
            ## Accessibility: Color palette input
                st.info("Custom Color Palettes")
                accessibility = st.selectbox("Accessibility: Colorblind-friendly Palettes", ["Normal", "Deuteranopia", "Protanopia", "Tritanopia", "Achromatopsia"])

                # Define default color palettes: used in map layers & map legend
                default_ndvi_palette = ["#ffffe5", "#f7fcb9", "#78c679", "#41ab5d", "#238443", "#005a32"]
                default_reclassified_ndvi_palette = ["#a50026","#ed5e3d","#f9f7ae","#f4ff78","#9ed569","#229b51","#006837"]
                
                # a copy of default colors that can be reaffected
                ndvi_palette = default_ndvi_palette.copy() 
                reclassified_ndvi_palette = default_reclassified_ndvi_palette.copy()

                if accessibility == "Deuteranopia":
                    ndvi_palette = ["#fffaa1","#f4ef8e","#9a5d67","#573f73","#372851","#191135"]
                    reclassified_ndvi_palette = ["#95a600","#92ed3e","#affac5","#78ffb0","#69d6c6","#22459c","#000e69"]
                elif accessibility == "Protanopia":
                    ndvi_palette = ["#a6f697","#7def75","#2dcebb","#1597ab","#0c677e","#002c47"]
                    reclassified_ndvi_palette = ["#95a600","#92ed3e","#affac5","#78ffb0","#69d6c6","#22459c","#000e69"]
                elif accessibility == "Tritanopia":
                    ndvi_palette = ["#cdffd7","#a1fbb6","#6cb5c6","#3a77a5","#205080","#001752"]
                    reclassified_ndvi_palette = ["#ed4700","#ed8a00","#e1fabe","#99ff94","#87bede","#2e40cf","#0600bc"]
                elif accessibility == "Achromatopsia":
                    ndvi_palette = ["#407de0", "#2763da", "#394388", "#272c66", "#16194f", "#010034"]
                    reclassified_ndvi_palette = ["#004f3d", "#338796", "#66a4f5", "#3683ff", "#3d50ca", "#421c7f", "#290058"]

        with st.container():
            ## Time range input
            with c1:
                col1, col2 = st.columns(2)
                
                # Creating a 2 days delay for the date_input placeholder to be sure there are satellite images in the dataset on app start
                today = datetime.today()
                delay = today - timedelta(days=2)

                # Date input widgets
                col1.warning("Initial NDVI Date 📅")
                initial_date = col1.date_input("initial", datetime(2026, 1, 12), label_visibility="collapsed")

                col2.success("Updated NDVI Date 📅")
                updated_date = col2.date_input("updated", datetime(2026, 1, 12), label_visibility="collapsed")

                # Setting up the time range variable for an image collection
                time_range = 7
                # Process initial date
                str_initial_start_date, str_initial_end_date = date_input_proc(initial_date, time_range)

                # Process updated date
                str_updated_start_date, str_updated_end_date = date_input_proc(updated_date, time_range)
    
    #### User input section - END

            #### Map section - START
            global last_uploaded_centroid

            # Create the initial map
            if last_uploaded_centroid is not None:
                latitude = last_uploaded_centroid[1]
                longitude = last_uploaded_centroid[0]
                m = folium.Map(location=[latitude, longitude], tiles=None, zoom_start=12, control_scale=True, attributionControl=0)
            else:
                # Default location if no file is uploaded
                m = folium.Map(location=[36.45, 10.85], tiles=None, zoom_start=5, control_scale=True, attributionControl=0)

            ### BASEMAPS - START
            ## Primary basemaps
            # OSM
            b0 = folium.TileLayer('OpenStreetMap', name='Open Street Map', attr='OSM')
            b0.add_to(m)

            # Mapbox
            mapbox_api = st.secrets["mapbox_token"]
            mapbox_url = f"https://api.mapbox.com/styles/v1/mapbox/dark-v11/tiles/{{z}}/{{x}}/{{y}}?access_token={mapbox_api}"

            b1 = folium.TileLayer(tiles=mapbox_url, attr='Mapbox', name='Mapbox Dark', overlay=False, control=True, max_zoom=20, min_zoom=1 )
            b1.add_to(m)

            # custom attribution textbox
            class MyCustomAttribution(MacroElement):
                _template = Template("""
                    {% macro script(this, kwargs) %}

                    L.Control.MyCustomAttribution = L.Control.extend({
                        onAdd: function(map) {
                            let div = L.DomUtil.create('div', 'map-credit-box');
                            div.innerHTML = `{{ this.injectedHtml }}`;
                            L.DomEvent.disableClickPropagation(div);
                            return div;
                        }
                    });

                    L.control.myCustomAttribution = function(opts) {
                        return new L.Control.MyCustomAttribution(opts);
                    };

                    L.control.myCustomAttribution({
                        position: "{{ this.position }}"
                    }).addTo({{ this._parent.get_name() }});

                    {% endmacro %}
                """)

                def __init__(self, injectedHtml, position="bottomright"):
                    super().__init__()
                    self.injectedHtml = escape_backticks(injectedHtml)
                    self.position = position

            credit_html = """
                <style>
                    .map-credit-box.leaflet-control {
                        bottom: -10px;
                        right: -10px;
                        z-index: 9999;
                        background: rgba(255, 255, 255, 0.85);
                        color: #333;
                        padding: 2px 2px;
                        border-radius: 4px;
                        font-size: 0.9rem;
                        font-weight: 600;
                        font-family: "Segoe UI", "Noto Sans", sans-serif;
                        line-height: 1.2;
                        max-width: 90vw;
                        white-space: normal;
                    }

                    .map-credit-box.leaflet-control a {
                        color: #0078A8;
                        text-decoration: none;
                    }
                    
                    .leaflet-bottom .leaflet-control-scale{
                        font-weight: 600;
                        font-family: "Source Sans Pro", sans-serif;
                        margin-bottom: 0;
                    }

                    /* Mobile adjustments */

                    @media (max-width: 825px) {
                        .leaflet-bottom .leaflet-control-scale{
                            margin-bottom: 25px;
                        }
                    }
                    @media (max-width: 815px) {
                        .map-credit-box.leaflet-control {
                            max-width: 100%;
                            width: 100%;
                        }
                        .leaflet-bottom .leaflet-control-scale{
                            margin-bottom: 45px;
                        }
                    }
                    @media (max-width: 610px) {
                        .map-credit-box.leaflet-control {
                            font-size: 0.8rem;
                            max-width: 100%;
                            text-align: center;
                        }
                        .leaflet-bottom .leaflet-control-scale{
                            margin-bottom: 45px;
                        }
                    }
                    @media (max-width: 550px) {
                        .map-credit-box.leaflet-control {
                            width: 100%;
                            text-align: center;
                        }
                        .leaflet-bottom .leaflet-control-scale{
                            margin-bottom: 45px;
                        }
                    }
                </style>

                🇵🇸 NDVI Viewer by <a href="https://github.com/IndigoWizard/NDVI-Viewer" target="_blank" rel="noopener noreferrer">@IndigoWizard</a> | Map Data: <a href="https://leafletjs.com/" target="_blank" rel="noopener noreferrer">Leaflet</a>, <a href="https://www.openstreetmap.org/about" target="_blank" rel="noopener noreferrer">OSM</a>, <a href="https://www.mapbox.com/about/maps" target="_blank" rel="noopener noreferrer">Mapbox</a>, <a href="https://sentinels.copernicus.eu/sentinel-data-access/sentinel-products/sentinel-2-data-products/collection-1-level-2a" target="_blank" rel="noopener noreferrer">Sentinel-2</a>, <a href="https://earthengine.google.com/" target="_blank" rel="noopener noreferrer">EarthEngine</a>
            """

            # add attribution control
            MyCustomAttribution(credit_html, position="bottomright").add_to(m)


            #### Satellite imagery Processing Section - START
            ee_errors = []
            ee_ready = True
            
            ## Defining and clipping image collections for both dates:
           
            # initial Image collection
            initial_collection, initial_collection_error = satCollection(cloud_pixel_percentage, str_initial_start_date, str_initial_end_date, geometry_aoi)
            
            if initial_collection_error:
                ee_errors.append(f"INITIAL DATE - NDVI Collection Error: \n > {initial_collection_error}")
                ee_ready = False

            # updated Image collection
            updated_collection, updated_collection_error = satCollection(cloud_pixel_percentage, str_updated_start_date, str_updated_end_date, geometry_aoi)
            
            if updated_collection_error:
                ee_errors.append(f"UPDATED DATE - NDVI Collection Error: \n > {updated_collection_error}")
                ee_ready = False

            # in case where both dates have no corresponding results to the query parameters (avoids double dialog popup crash)
            # if initial_collection_error and updated_collection_error:
            #     show_error_dialog(f"No Image Collection Found for any Date: \n > {initial_collection_error} \n {updated_collection_error}")
            #     ee_ready = False

            if ee_errors:
                show_error_dialog("\n\n---\n\n".join(ee_errors))
                
            if ee_ready:
                # setting a sat_imagery variable that could be used for various processes later on (tci, ndvi... etc)
                initial_sat_imagery = initial_collection.median()
                updated_sat_imagery = updated_collection.median()

                ## TCI (True Color Imagery)
                # Clipping the image to the area of interest "aoi"
                initial_tci_image = initial_sat_imagery
                updated_tci_image = updated_sat_imagery

                # TCI image visual parameters
                tci_params = {
                'bands': ['B4', 'B3', 'B2'], #using Red, Green & Blue bands for TCI.
                'min': 0,
                'max': 1,
                'gamma': 1
                }

                ## Other imagery processing operations go here 
                # NDVI
                def getNDVI(collection):
                    return collection.normalizedDifference(['B8', 'B4'])

                # clipping to AOI
                initial_ndvi = getNDVI(initial_sat_imagery)
                updated_ndvi = getNDVI(updated_sat_imagery)

                # NDVI visual parameters:
                ndvi_params = {
                'min': 0,
                'max': 1,
                'palette': ndvi_palette
                }

                # Masking NDVI over the water & show only land
                def satImageMask(sat_image):
                    masked_image = sat_image.updateMask(sat_image.gte(0))
                    return masked_image
                
                # Mask NDVI images
                initial_ndvi = satImageMask(initial_ndvi)
                updated_ndvi = satImageMask(updated_ndvi)

                # ##### NDVI classification: 7 classes
                def classify_ndvi(masked_image): # better use a masked image to avoid water bodies obstracting the result as possible
                    ndvi_classified = ee.Image(masked_image) \
                    .where(masked_image.gte(0).And(masked_image.lt(0.15)), 1) \
                    .where(masked_image.gte(0.15).And(masked_image.lt(0.25)), 2) \
                    .where(masked_image.gte(0.25).And(masked_image.lt(0.35)), 3) \
                    .where(masked_image.gte(0.35).And(masked_image.lt(0.45)), 4) \
                    .where(masked_image.gte(0.45).And(masked_image.lt(0.65)), 5) \
                    .where(masked_image.gte(0.65).And(masked_image.lt(0.75)), 6) \
                    .where(masked_image.gte(0.75), 7) \
                    
                    return ndvi_classified

                # Classify masked NDVI
                initial_ndvi_classified = classify_ndvi(initial_ndvi)
                updated_ndvi_classified = classify_ndvi(updated_ndvi)

                # Classified NDVI visual parameters
                ndvi_classified_params = {
                'min': 1,
                'max': 7,
                'palette': reclassified_ndvi_palette
                # each color corresponds to an NDVI class.
                }

            #### Satellite imagery Processing Section - END

            #### Layers section - START
            if ee_ready:
                st.session_state.ee_ready = True
                # Check if the initial and updated dates are the same
                if initial_date == updated_date:
                    # Only display the layers based on the updated date without dates in their names
                    m.add_ee_layer(updated_tci_image, tci_params, 'Satellite Imagery')
                    m.add_ee_layer(updated_ndvi, ndvi_params, 'Raw NDVI')
                    m.add_ee_layer(updated_ndvi_classified, ndvi_classified_params, 'Reclassified NDVI')
                    
                    st.toast(f"Results found for: \n\n {initial_date}")

                else:
                    # Show both dates in the appropriate layers
                    # Satellite image
                    m.add_ee_layer(initial_tci_image, tci_params, f'Initial Satellite Imagery: {initial_date}')
                    m.add_ee_layer(updated_tci_image, tci_params, f'Updated Satellite Imagery: {updated_date}')

                    # NDVI
                    m.add_ee_layer(initial_ndvi, ndvi_params, f'Initial Raw NDVI: {initial_date}')
                    m.add_ee_layer(updated_ndvi, ndvi_params, f'Updated Raw NDVI: {updated_date}')

                    # Add layers to the second map (m.m2)
                    # Classified NDVI
                    m.add_ee_layer(initial_ndvi_classified, ndvi_classified_params, f'Initial Reclassified NDVI: {initial_date}')
                    m.add_ee_layer(updated_ndvi_classified, ndvi_classified_params, f'Updated Reclassified NDVI: {updated_date}')
                    
                    st.toast(f"Results found for: [{initial_date}]-[{updated_date}]")

            else:
                st.session_state.ee_ready = False
                st.toast("No satellite imagery available for the selected parameters.")
            
            #### Layers section - END

            #### Map result display - START
            
            # full screen plugin
            folium.plugins.Fullscreen(position="bottomright", title="Expand", title_cancel="Exit", force_separate_button=True).add_to(m)
            # Folium Map Layer Control: we can see and interact with map layers
            folium.LayerControl(collapsed=True).add_to(m)

            # Display the map
        submitted = c2.form_submit_button("Generate map")
        if submitted:
            with c1:
                st_folium(m, use_container_width=True, height="500")
        else:
            with c1:
                st_folium(m, use_container_width=True, height="500")

    #### Map result display - END

    #### Legend - START
    with st.container():
        st.subheader("Map Legend:")
        col3, col4, col5 = st.columns([1,2,1])

        with col3:            
            # Create an HTML legend for NDVI classes
            ndvi_legend_html = """
                <div class="ndvilegend">
                    <h5>Raw NDVI</h5>
                    <div style="display: flex; flex-direction: row; align-items: flex-start; gap: 1rem; width: 100%;">
                        <div style="width: 30px; height: 200px; background: linear-gradient({0},{1},{2},{3},{4},{5});"></div>
                        <div style="display: flex; flex-direction: column; justify-content: space-between; height: 200px;">
                            <span>-1</span>
                            <span style="align-self: flex-end;">1</span>
                        </div>
                    </div>
                </div>
            """.format(*ndvi_palette)

            # Display the NDVI legend using st.markdown
            st.markdown(ndvi_legend_html, unsafe_allow_html=True)

        with col4:            
            # Create an HTML legend for NDVI classes
            reclassified_ndvi_legend_html = """
                <div class="reclassifiedndvi">
                    <h5>NDVI Classes</h5>
                    <ul style="list-style-type: none; padding: 0;">
                        <li style="margin: 0.2em 0px; padding: 0;"><span style="color: {0};">&#9632;</span> Absent Vegetation. (Water/Clouds/Built-up/Rocks/Sand Surfaces..)</li>
                        <li style="margin: 0.2em 0px; padding: 0;"><span style="color: {1};">&#9632;</span> Bare Soil.</li>
                        <li style="margin: 0.2em 0px; padding: 0;"><span style="color: {2};">&#9632;</span> Low Vegetation.</li>
                        <li style="margin: 0.2em 0px; padding: 0;"><span style="color: {3};">&#9632;</span> Light Vegetation.</li>
                        <li style="margin: 0.2em 0px; padding: 0;"><span style="color: {4};">&#9632;</span> Moderate Vegetation.</li>
                        <li style="margin: 0.2em 0px; padding: 0;"><span style="color: {5};">&#9632;</span> Strong Vegetation.</li>
                        <li style="margin: 0.2em 0px; padding: 0;"><span style="color: {6};">&#9632;</span> Dense Vegetation.</li>
                    </ul>
                </div>
            """.format(*reclassified_ndvi_palette)

            # Display the Reclassified NDVI legend using st.markdown
            st.markdown(reclassified_ndvi_legend_html, unsafe_allow_html=True)

    #### Legend - END

    #### Miscs Infos - START
    with st.container():
        st.subheader("Information")

        ## How It Works
        st.write("#### Process workflow: AOI, Date Range, and Classification")
        st.write("This app provides a simple interface to explore NDVI changes over time for a specified Area of Interest (AOI). Here's how it works:")

        st.write("1. **Upload GeoJSON AOI:** Start by uploading a GeoJSON file that outlines your Area of Interest. This defines the region where NDVI analysis will be performed. You can create any polygon-shaped area of interest at [geojson.io](https://geojson.io).")
        st.write("2. **Select Date Range:** Choose a date, this input triggers the app to gather images from a **7-days range** leading to that date. These images blend into a mosaic that highlights vegetation patterns while minimizing disruptions like clouds. ")
        st.write("3. **Select Cloud Coverate Rate:** Choose a value for cloud coverage, this input triggers the app to gather images with relevant value of clouds covering the images. A higher value will gather more images but may be of poor quality, lower cloud coverage value gathers clearer images, but may have less images in the collection.")
        st.write("4. **Image Collection and Processing:** Once the date range is established, the app collects satellite images spanning that period. These images are then clipped to your chosen Area of Interest (AOI) and undergo processing to derive raw NDVI values using wavelength calculations. This method ensures that the resulting NDVI map accurately reflects the vegetation status within your specific region of interest.")
        st.write("5. **NDVI Classification:** The raw NDVI results are classified into distinct vegetation classes. This classification provides a simplified visualization of vegetation density, aiding in interpretation.")
        st.write("6. **Map Visualization:** The results are displayed on an interactive map, allowing you to explore NDVI patterns and changes within your AOI.")

        st.write("This app is designed to provide an accessible tool for both technical and non-technical users to explore and interpret vegetation health and density changes.")
        st.write("Keep in mind that while the NDVI map is a valuable tool, its interpretation requires consideration of various factors. Enjoy exploring the world of vegetation health and density!")

        # Results interpretation
        st.write("#### Interpreting the Results")
        st.write("When exploring the NDVI map, keep in mind:")

        st.write("- Clouds, atmospheric conditions, and water bodies can affect the map's appearance.")
        st.write("- Satellite sensors have limitations in distinguishing surface types, leading to color variations.")
        st.write("- NDVI values vary with seasons, growth stages, and land cover changes.")
        st.write("- The map provides visual insights rather than precise representations.")

        st.write("Understanding these factors will help you interpret the results more effectively. This application aims to provide you with an informative visual aid for vegetation analysis.")

        ## NDVI/Environmental Index
        st.write("#### Using an Environmental Index - NDVI:")
        st.write("The [Normalized Difference Vegetation Index (NDVI)](https://eos.com/make-an-analysis/ndvi/) is an essential environmental index that provides insights into the health and density of vegetation. It is widely used in remote sensing and geospatial analysis to monitor changes in land cover, vegetation growth, and environmental conditions.")

        st.write("NDVI is calculated using satellite imagery that captures both Near-Infrared **(NIR)** and Red **(R)** wavelengths. The formula is:")
        st.latex(r'''
        \text{NDVI} = \frac{\text{NIR} - \text{R}}{\text{NIR} + \text{R}}
        ''')

        st.write("NDVI values range from **[-1** to **1]**, with higher values indicating denser and healthier vegetation. Lower values represent non-vegetated surfaces like water bodies, bare soil, or built-up areas.")

        ## Data
        st.write("#### Data: Sentinel-2 Imagery and L2A Product")
        st.write("This app utilizes **Sentinel-2 Level-2A atmospherically corrected Surface Reflectance images**. The [Sentinel-2 satellite constellation](https://sentiwiki.copernicus.eu/web/s2-applications) consists of twin satellites (Sentinel-2A and Sentinel-2B) that capture high-resolution multispectral imagery of the Earth's surface.")

        st.write("The [Level-2A](https://sentiwiki.copernicus.eu/web/s2-products#S2Products-Level-2AProductsS2-Products-L2Atrue) products have undergone atmospheric correction, enhancing the accuracy of surface reflectance values. These images are suitable for various land cover and vegetation analyses, including NDVI calculations.")


        #### Miscs Info - END

        #### Contributiuon - START
        st.header("Contribute to the App")
        con1, con2 = st.columns(2)
        con1.image("https://www.pixenli.com/image/SoL3iZMG")
        con2.markdown("""
            Contributions are welcome from the community to help improve this app! Whether you're interested in fixing bugs 🐞, implementing a new feature 🌟, or enhancing the user experience 🪄, your contributions are valuable.
                    
            The project is listed under **Hacktoberfest** lalbel for those of you [Hacktoberfest](https://hacktoberfest.com/) enthusiasts! Since the reward for contributing 4 PRs is getting a tree planted in your name through [TreeNation](https://tree-nation.com/), I see it fits the theme of this project.
            """)
        st.markdown("""
            #### Ways to Contribute

            - **Report Issues**: If you come across any bugs, issues, or unexpected behavior, please report them in the [GitHub Issue Tracker](https://github.com/IndigoWizard/NDVI-Viewer/issues).

            - **Suggest Enhancements**: Have an idea to make the app better? Share your suggestions in the [GitHub Issue Tracker](https://github.com/IndigoWizard/NDVI-Viewer/issues).

            - **Code Contributions**: If you're comfortable with coding, you can contribute by submitting pull requests against the `dev` branch of the [Project's GitHub repository](https://github.com/IndigoWizard/NDVI-Viewer/).
        """)

        #### Contributiuon - START

        #### About App - START
        st.subheader("About:")
        st.markdown("This project was first developed by me ([IndigoWizard](https://github.com/IndigoWizard)) and [Emmarie-Ahtunan](https://github.com/Emmarie-Ahtunan) as a submission to the **Environemental Data Challenge** of [Global Hack Week: Data](https://ghw.mlh.io/) by [Major League Hacking](https://mlh.io/).<br> I continued developing the base project to make it a feature-complete app. Check the project's GitHub Repo here: [IndigoWizard/NDVI-Viewer](https://github.com/IndigoWizard/NDVI-Viewer)",  unsafe_allow_html=True)
        st.image("https://www.pixenli.com/image/Hn1xkB-6")
        #### About App - END

        #### Credit - START
        st.subheader("Credit:")
        st.markdown("""The app was developped by [IndigoWizard](https://github.com/IndigoWizard) using; [Streamlit](https://streamlit.io/), [Google Earth Engine](https://github.com/google/earthengine-api) Python API, [Folium](https://github.com/python-visualization/folium). Agriculture icons created by <a href="https://www.flaticon.com/free-icons/agriculture" title="agriculture icons">dreamicons - Flaticon</a>""", unsafe_allow_html=True)
        #### Credit - END
    

# Run the app
if __name__ == "__main__":
    main()