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	import os
	import streamlit as st
	import os
	os.system('pip install geemap')
	import geemap.foliumap as geemap
	import ee
	os.system('pip install geopandas')
	import geopandas as gpd
	import tempfile
	import uuid
	os.system('pip install fiona')
	import fiona
	from datetime import datetime
	import base64
	os.system('pip install rasterio')
	import rasterio
	from rasterio.plot import show
	import numpy as np
	os.system('pip install opencv-python')
	import cv2
	import matplotlib.pyplot as plt
	from matplotlib import pyplot as plt
	import pyproj
	from shapely.geometry import Polygon
	from rasterio.features import shapes
	import pandas as pd
	os.system('pip install scikit-image')
	from skimage import measure
	from shapely.geometry import Polygon, box, MultiPolygon
	import matplotlib.pyplot as plt
	import requests
	from io import StringIO
	from rasterio.warp import calculate_default_transform, reproject, Resampling
	import json




	# Set page configuration
	st.set_page_config(layout="wide")


	# Define the custom CSS style for the title and subtitle
	custom_css = """
	<style>
	@import url('https://fonts.googleapis.com/css2?family=Open+Sans:wght@400;700&display=swap');

	.title-custom-style {
	font-family: 'SpaceGrotesk-Light';
	font-size: 64px;
	font-weight: 500;
	color: #fff;
	margin-bottom: 25px;
	margin-top: 125px;
	margin-left: 280px;
	text-shadow: 2px 2px 4px rgba(1, 1, 1, 1);
	}

	.subtitle-custom-style {
	font-family: 'SpaceGrotesk-Medium';
	max-width: 620px;
	margin-left: 280px;
	font-weight: 20;
	text-transform: uppercase;
	color: #fff;
	font-size: 15px;
	}
	</style>
	"""


	import streamlit as st
	os.system('pip install streamlit-navigation-bar')
	from streamlit_navigation_bar import st_navbar


	pages = ["Home","Worldwide Analysis"]
	styles = {
	"nav": {
	"background-color": "rgba(0, 0, 0, 0.5)",
	# Add 50% transparency
	},
	"div": {
	"max-width": "32rem",
	},
	"span": {
	"border-radius": "0.26rem",
	"color": "rgb(255 ,255, 255)",
	"margin": "0 0.225rem",
	"padding": "0.375rem 0.625rem",
	},
	"active": {
	"background-color": "rgba(0 ,0, 200, 0.95)",
	},
	"hover": {
	"background-color": "rgba(255, 255, 255, 0.95)",
	},
	}

	page = st_navbar(pages, styles=styles)

	page = st.sidebar.selectbox("",pages)

	# Apply the custom CSS style and HTML title using Markdown
	st.markdown(f"{custom_css}<h1 class='title-custom-style'>Real-Time Reservoir Monitoring Platform</h1>", unsafe_allow_html=True)
	st.markdown("<h2 class='subtitle-custom-style'>This software allows you to monitorize the volume storage of almost any water body at your choice. It is still in beta version.</h2>", unsafe_allow_html=True)

	import psutil
	import streamlit as st

	# Authenticate to the Earth Engine servers
	#ee.Authenticate()
	#use this line if necessary to authenticate on Google Earth Engine API project

	# Access the secret as an environment variable
	gee_secret_service_account = os.getenv("gee_secret_service_account")

	if gee_secret_service_account:
	# Parse the JSON string
	service_account_info_dict = json.loads(gee_secret_service_account)

	# Authenticate with Google Earth Engine
	try:
	service_account_email = service_account_info_dict["client_email"]

	# Create a temporary JSON file for the credentials
	with open("temp_service_account.json", "w") as temp_file:
	json.dump(service_account_info_dict, temp_file)

	# Authenticate using the temporary file
	credentials = ee.ServiceAccountCredentials(service_account_email, "temp_service_account.json")
	ee.Initialize(credentials)
	print("Authenticated successfully with Google Earth Engine!")
	except Exception as e:
	print(f"Error authenticating with Google Earth Engine: {e}")
	else:
	print("Error: 'gee_secret_service_account' environment variable not found.")

	# Function to process uploaded GeoJSON or KML file and return a GeoDataFrame
	def process_uploaded_file(data):
	_, 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.read()) # Use data.read() to write file content

	if file_extension.lower() == ".kml":
	fiona.drvsupport.supported_drivers["KML"] = "rw"
	gdf = gpd.read_file(file_path, driver="KML")
	elif file_extension.lower() in [".geojson", ".json"]:
	gdf = gpd.read_file(file_path)
	else:
	raise ValueError(f"Unsupported file format: {file_extension}")

	return gdf

	import streamlit as st

	# Sidebar customization
	st.sidebar.title("About")

	st.sidebar.markdown(
	"""
	This Beta version allows you to visualize the volume storage, water surface elevation and other infor of the majority of reservoirs and lakes worlddwide, in real time using remote sensing,
	created by João Pimenta
	"""
	)
	# Create unique keys for each st.radio widget
	world_key = "Worldwide anaysis"
	if page == 'Home':
	# Video URL (ensure it's accessible)
	video_url = "https://raw.githubusercontent.com/joao862/BLU/main/1851190-uhd_3840_2160_25fps.mp4"

	# Fetch the video from the URL
	response = requests.get(video_url)

	# Check if the request was successful
	if response.status_code == 200:
	video_bytes = response.content

	# Convert the video bytes to Base64
	video_base64 = base64.b64encode(video_bytes).decode("utf-8")

	# Set the background video using CSS
	st.markdown(
	f"""
	<style>
	.stApp {{
	background-image: url('data:video/mp4;base64,{video_base64}');
	background-size: cover;
	}}
	</style>
	""",
	unsafe_allow_html=True
	)
	else:
	st.error("Failed to load video. Please check the URL or your internet connection.")

	elif page == "Worldwide Analysis":
	st.title("Worldwide Analysis")
	# File uploader for GeoJSON or KML
	uploaded_file = st.file_uploader("Upload a GeoJSON or KML File")

	HydroLakes = ee.FeatureCollection('projects/ee-joaopedromateusp/assets/HydroLAKES')
	# List of feature collections
	datasets = [
	ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_21'),
	ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_22'),
	ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_23'),
	ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_24'),
	ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_25'),
	ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_26'),
	ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_27'),
	ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_29'),

	# Add more datasets
	]

	# Initialize with the first dataset in the list
	merged_dataset = datasets[0]

	# Loop through and merge each subsequent dataset
	for dataset in datasets[1:]:
	merged_dataset = merged_dataset.merge(dataset)

	night_mode = 'CartoDB.DarkMatter'
	normal_mode = 'HYBRID'

	# Options for confirming the reservoir selection
	modes = ['day theme', 'night theme']

	# Select box to confirm selection
	confirmation_mode = st.sidebar.selectbox("Choose this lake/reservoir", modes)
	if confirmation_mode == 'day theme':
	mode = normal_mode
	else:
	mode = normal_mode
	# Step 1: Create a geemap Map object with the required plugins
	m = geemap.Map(
	basemap=mode,
	plugin_Draw=True,
	Draw_export=True,
	locate_control=True,
	plugin_LatLngPopup=True
	)

	m.set_center(13.5352, 48.8069, 5)

	os.system('pip install streamlit-folium')
	from streamlit_folium import st_folium
	import folium
	import json

	# Define visualization parameters to color the polygons blue or yellow
	vis_params = {'color': 'Blue'}

	#m.addLayer(HydroLakes.style(**vis_params), {}, 'HydroLakes')
	# Add the HydroLakes layer to the map
	m.addLayer(merged_dataset.style(**vis_params), {}, 'Europe')

	# JavaScript for click events to set session state
	click_js = """
	function addClickHandler(map) {
	map.on('click', function(e) {
	const latlng = e.latlng;
	const coords = [latlng.lat, latlng.lng];
	window.parent.postMessage(coords, '*');
	});
	}
	addClickHandler(window.map);
	"""

	# Add JavaScript to the map
	m.add_child(folium.Element(f'<script>{click_js}</script>'))

	# Display the map in Streamlit
	st_data = st_folium(m, height=800, width=1600)

	# Initialize session state for the selected ROI
	if 'roi' not in st.session_state:
	st.session_state['roi'] = None

	if st_data['last_clicked']:
	lat, lng = st_data['last_clicked']['lat'], st_data['last_clicked']['lng']
	point = ee.Geometry.Point([lng, lat])
	filtered = merged_dataset.filterBounds(point)
	info = filtered.getInfo()
	features = info['features']
	if features:
	properties = features[0]['properties']
	coordinates = features[0]['geometry']['coordinates']
	# Extracting the required fields
	lake_name = properties.get('names', 'N/A')
	lake_id = properties.get('lake_id', 'N/A')
	latitude = properties.get('lat', 'N/A')
	longitude = properties.get('lon', 'N/A')
	ref_area = properties.get('ref_area', 'N/A')
	storage = properties.get('storage', 'N/A')

	# Display metrics in Streamlit
	st.title("Lake Information")
	# Using Streamlit columns for a clean layout
	#col1, col2 = st.columns(2)
	#with col1:
	#st.metric("Lake Name", lake_name)
	#st.metric("Lake ID", lake_id)
	#with col2:
	#st.metric("Latitude", round(latitude, 4))
	#st.metric("Longitude", round(longitude, 4))

	# Display reference area with one decimal for clarity
	#st.metric("Reference Area (sq km)", f"{ref_area:.1f}")
	# Ensure coordinates are in the correct format for GeoJSON (swapping lon, lat to lat, lon)
	#if isinstance(coordinates[0][0], list): # Check if it's a nested list (MultiPolygon or Polygon)
	# Swap lon and lat if necessary
	#coordinates = [list(map(lambda coord: [coord[1], coord[0]], sub_coord)) for sub_coord in coordinates]

	# Create the Earth Engine geometry (assuming Polygon type)
	aoi = ee.Geometry.Polygon(coordinates)

	roi = aoi

	globathy_dataset = ee.FeatureCollection("projects/ee-joaopedromateusp/assets/HydroLAKES")

	# Add the HydroLakes layer to the map
	m.addLayer(globathy_dataset.style(**vis_params), {}, 'Globathy')

	point = ee.Geometry.Point([lng, lat])
	filtered = globathy_dataset.filterBounds(point)
	info = filtered.getInfo()
	features = info['features']
	if features:
	properties = features[0]['properties']
	hydrolakes_id = properties.get('Hylak_id', 'N/A')
	Vol_res = properties.get('Vol_res','N/A')
	Grand_id = properties.get('Grand_id','N/A')
	# Using Streamlit columns for a clean layout
	col1, col2 = st.columns(2)
	with col1:
	st.metric("Hydrolakes ID", hydrolakes_id)
	st.metric("Maximum Volume", Vol_res)
	with col2:
	st.metric("GranD ID", Grand_id)

	import ee
	import geemap
	import os
	import matplotlib.pyplot as plt
	import rasterio
	from rasterio.plot import show
	from skimage import measure
	from shapely.geometry import Polygon, box
	from shapely.ops import transform
	import numpy as np
	import json

	# Filter Sentinel-2 images
	sentinelImageCollection = ee.ImageCollection('COPERNICUS/S2') \
	.filterBounds(roi) \
	.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 5)) \
	.sort('system:time_start', False) # Sort by time_start in descending order

	# Get the latest (first) image from the sorted collection
	latest_image = sentinelImageCollection.first()

	previous_image = sentinelImageCollection.toList(sentinelImageCollection.size()).get(1)
	previous_image = ee.Image(previous_image)

	# Define a function to calculate NDWI and mask
	def calculate_ndwi_and_mask(image):
	ndwi = image.normalizedDifference(['B3', 'B8']).rename('NDWI')
	ndwi_threshold = ndwi.gte(0.0)
	ndwi_mask = ndwi_threshold.updateMask(ndwi_threshold)
	return ndwi_mask

	# Apply the function to the latest image to calculate NDWI mask
	ndwi_mask = calculate_ndwi_and_mask(latest_image)
	ndwi_prev_mask = calculate_ndwi_and_mask(previous_image)

	# Define a function to calculate water area
	def calculate_water_area(image):
	water_area = image.multiply(ee.Image.pixelArea()).reduceRegion(
	reducer=ee.Reducer.sum(),
	geometry=roi,
	scale=5
	).get('NDWI')
	return image.set('water_area', water_area)

	# Calculate water area for the NDWI mask
	ndwi_mask_with_area = calculate_water_area(ndwi_mask)
	ndwi_pre_mask_with_area = calculate_water_area(ndwi_prev_mask)

	m.add_marker(lat=lat, lon=lng, location=[lat, lng])
	m.set_center(lng, lat, 14)

	globathy_dataset = ee.FeatureCollection("projects/ee-joaopedromateusp/assets/HydroLAKES")

	# Add the HydroLakes layer to the map
	m.addLayer(globathy_dataset.style(**vis_params), {}, 'Globathy')

	point = ee.Geometry.Point([lng, lat])
	filtered = globathy_dataset.filterBounds(point)
	info = filtered.getInfo()
	features = info['features']
	if features:
	properties = features[0]['properties']
	hydrolakes_id = properties.get('Hylak_id', 'N/A')
	Vol_res = properties.get('Vol_res','N/A')
	Grand_id = properties.get('Grand_id','N/A')
	Country = properties.get('Country','N/A')
	try:
	# Get the water area information
	water_area_info = ndwi_mask_with_area.get('water_area').getInfo()
	pre_water_area_info = ndwi_pre_mask_with_area.get('water_area').getInfo()
	prev = round((pre_water_area_info / 1e6), 2)
	water_area_km2 = round((water_area_info / 1e6), 2)
	variance = round(((water_area_km2 - prev) / prev) * 100, 2) # Calculate variance as a percentage
	import netCDF4 as nc
	import numpy as np

	# Open the NetCDF file
	nc_file = nc.Dataset('/Users/joaopimenta/Downloads/Master thesis/GLOBathy_hAV_relationships.nc')

	# Specify the lake ID you want to search for
	target_lake_id = hydrolakes_id # Replace this with the actual lake ID you're interested in

	# Find the index of the lake based on the lake ID
	lake_ids = nc_file.variables['lake_id'][:]

	# Check if the target lake ID exists in the lake_id variable
	lake_index = np.where(lake_ids == target_lake_id)[0]

	if len(lake_index) == 0:
	st.write("Lake not found in the dataset.")
	else:
	lake_index = lake_index[0] # Use the first match if found

	# Extract coefficients of the area-storage equation for the identified lake
	area_storage_coeffs = nc_file.variables['f_hA'][lake_index, :]
	lon_lat = nc_file.variables['lon_lat'][lake_index, :]
	import numpy as np
	# Coefficients obtained from the NetCDF dataset
	a = area_storage_coeffs[0]
	b = area_storage_coeffs[1]
	# Calculate the volume using the area-storage equation
	volume = ((water_area_info/1e6) / a) ** (1 / b)
	volume_prev = ((pre_water_area_info/1e6) / a) ** (1 / b)
	vol_variance = round(((volume - volume_prev) / volume_prev) * 100, 2)
	except Exception as e:
	st.write("Error retrieving water area information:", e)
	# Using Streamlit columns for a clean layout
	col1, col2 = st.columns(2)
	with col1:
	st.metric("Lake Name", lake_name)
	st.metric("Lake ID", lake_id)
	st.metric("Hydrolakes ID", hydrolakes_id)
	st.metric("Maximum Volume(10⁸ m³)", Vol_res/10)
	with col2:
	# Display metric with variance as delta
	st.metric(
	label="Current Water Area",
	value=f"{water_area_km2} km²",
	delta=f"{variance}%", # Add percentage change as delta
	delta_color="normal",
	help=None,
	label_visibility="visible",
	)
	st.metric(
	label="Current Water Volume",
	value=f"{round(volume,2)} x10⁸m³",
	delta=f"{vol_variance}%", # Add percentage change as delta
	delta_color="normal",
	help=None,
	label_visibility="visible",
	)
	st.metric("Country",Country)
	st.metric("GranD ID", Grand_id)
	else:
	st.write("No features were selected")
	# Highlight the selected lake
	m.addLayer(ee.Image().paint(aoi, 1, 3), {'palette': 'red'}, 'Selected Lake')

	else:
	st.write('No polygon found at clicked location.')

	# Function to export ROI as GeoJSON
	def export_roi_as_geojson(roi):
	if roi:
	roi_geojson = roi.getInfo()
	if roi_geojson.get('type') == 'Polygon':
	geojson_str = json.dumps(roi_geojson)
	return geojson_str
	else:
	st.error("GeoJSON type is not supported.")
	return None
	else:
	st.error("No ROI available.")
	return None

	geojson_str = export_roi_as_geojson(aoi)
	if geojson_str:
	st.download_button(
	label="Download ROI as GeoJSON",
	data=geojson_str,
	file_name="roi.geojson",
	mime="application/geo+json"
	)
	# Options for confirming the reservoir selection
	box_reservoir = ['No', 'Yes']

	# Select box to confirm selection
	confirmation = st.selectbox("Choose this lake/reservoir", box_reservoir)

	# Handle the selection
	if confirmation == 'Yes':
	st.session_state['roi'] = aoi # Store the selected ROI in session state
	roi = aoi # Set the roi for further processing
	st.success("Reservoir selected successfully!")
	else:
	st.warning("No reservoir selected yet.")

	# If a region of interest (ROI) is available, provide download
	if uploaded_file is not None:
	try:
	gdf = process_uploaded_file(uploaded_file)

	if not gdf.empty:
	roi_fc = geemap.geopandas_to_ee(gdf)
	roi_geometry = roi_fc.geometry()
	aoi = roi_geometry
	st.session_state['roi'] = aoi # Store the selected ROI in session state
	roi = aoi # Set the roi for further processing
	st.success("Reservoir selected successfully!")

	# Add markers for each feature in the GeoDataFrame
	for index, row in gdf.iterrows():

	latitude, longitude = row.geometry.centroid.coords[0] # Get centroid coordinates
	m.add_marker(location =[lng,lat])
	# Set the map center and zoom level based on the selected location
	m.set_center(lat, lng, 12)
	globathy_dataset = ee.FeatureCollection("projects/ee-joaopedromateusp/assets/HydroLAKES")

	# Add the HydroLakes layer to the map
	m.addLayer(globathy_dataset.style(**vis_params), {}, 'Globathy')

	point = ee.Geometry.Point([lng, lat])
	filtered = globathy_dataset.filterBounds(point)
	info = filtered.getInfo()
	features = info['features']
	if features:
	properties = features[0]['properties']
	hydrolakes_id = properties.get('Hylak_id', 'N/A')
	Vol_res = properties.get('Vol_res','N/A')
	Grand_id = properties.get('Grand_id','N/A')
	# Using Streamlit columns for a clean layout
	col1, col2 = st.columns(2)
	with col1:
	st.metric("Hydrolakes ID", hydrolakes_id)
	st.metric("Maximum Volume", Vol_res)
	with col2:
	st.metric("GranD ID", Grand_id)
	m.addLayer(roi_fc, {}, "Uploaded Data")
	except Exception as e:
	st.write(f"Error processing uploaded file: {e}")

	if 'roi' in st.session_state and 'aoi' in locals():

	roi = aoi # Use the selected ROI
	# Create a select box for choosing the area-volume relationship method
	opt = ["Don't have that info", "Write the A-V function of your reservoir", "upload excel sheet", "upload the DEM"]
	method = st.sidebar.selectbox(
	"Choose the area-volume relationship input",
	opt,
	key="method")

	if method == ("Write the A-V function of your reservoir"):
	volumes =[]
	column1, column2 = st.sidebar.columns(2)
	with column1:
	a = st.number_input("Coefficient a")
	with column2 :
	b = st.number_input("Coefficient b")

	elif method == ("upload excel sheet"):
	# File uploader for Excel files
	uploaded_file = st.file_uploader("Upload an Excel File", type=["xlsx"])

	if uploaded_file is not None:

	# Add an input box for the user to enter a sheet index number
	sheet_index = int(st.number_input("Enter the index of the Excel sheet (first sheet is 0)", min_value=0))
	st.write("You entered sheet index:", sheet_index)

	try:
	# Load the Excel file into a DataFrame from the specified sheet
	df = pd.read_excel(uploaded_file, sheet_name=sheet_index)

	# Check if the required columns are present
	if 'ÁREA (m2)' not in df.columns or 'VOLUME (m3)' not in df.columns:
	st.error("Required columns 'ÁREA (m2)' or 'VOLUME (m3)' not found in the sheet.")
	else:
	# Drop rows with NaN values in the required columns
	df = df.dropna(subset=['ÁREA (m2)', 'VOLUME (m3)'])

	# Initialize a dictionary
	dictionary = {}

	# Populate the dictionary with 'ÁREA (m2)' as keys and 'VOLUME (m3)' as values
	for index, row in df.iterrows():
	area = row['ÁREA (m2)']
	volume = row['VOLUME (m3)']
	dictionary[area] = volume

	# Display the created dictionary
	st.write("dictionary =", dictionary)

	except ValueError as e:
	st.error(f"Error reading sheet index {sheet_index}: {e}")
	except Exception as e:
	st.error(f"An error occurred: {e}")

	elif method == "upload the DEM":

	import rasterio
	import numpy as np
	import matplotlib.pyplot as plt
	from mpl_toolkits.axes_grid1 import make_axes_locatable

	# File uploader for GeoTIFF
	dem_file = st.file_uploader("Upload a GeoTIFF File")

	if dem_file is not None:

	with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
	tmp_file.write(dem_file.getbuffer())
	tmp_file_path = tmp_file.name

	# Load the raster data
	lakeRst = rasterio.open(tmp_file_path)
	st.write("Number of bands:", lakeRst.count)

	# Raster resolution
	resolution = lakeRst.res
	st.write("Resolution:", resolution)

	# Read the first band (assuming single band raster)
	lakeBottom = lakeRst.read(1)
	st.write("Sample raster data:\n", lakeBottom[:5, :5])

	# Replace no-data value with np.nan
	noDataValue = np.copy(lakeBottom[0, 0])
	lakeBottom = lakeBottom.astype(float)
	lakeBottom[lakeBottom == noDataValue] = np.nan

	# Display the raster data
	plt.figure(figsize=(12, 12))
	plt.imshow(lakeBottom, cmap='viridis')
	plt.title('Lake Bottom Elevation')
	plt.colorbar(label='Elevation (masl)')
	st.pyplot(plt)

	# Calculate minimum and maximum elevation
	minElev = np.nanmin(lakeBottom)
	maxElev = np.nanmax(lakeBottom)
	st.write('Min bottom elevation: %.2f m, Max bottom elevation: %.2f m' % (minElev, maxElev))

	# Define the number of steps for calculation
	nSteps = 20

	# Generate elevation steps
	elevSteps = np.round(np.linspace(minElev, maxElev, nSteps), 2)
	st.write("Elevation steps:", elevSteps)

	# Define function to calculate volume at a given elevation step
	def calculateVol(elevStep, elevDem, lakeRst):
	tempDem = elevStep - elevDem[elevDem < elevStep]
	tempVol = tempDem.sum() * lakeRst.res[0] * lakeRst.res[1]
	return tempVol

	# Define function to calculate inundated area for a given elevation
	def calculateArea(elevStep, elevDem):
	inundated_mask = np.where(elevDem <= elevStep, 1, 0)
	area = np.sum(inundated_mask) * resolution[0] * resolution[1]
	return area

	# Calculate volumes and areas for each elevation step
	volArray = []
	areaArray = []
	for elev in elevSteps:
	tempVol = calculateVol(elev, lakeBottom, lakeRst)
	tempArea = calculateArea(elev, lakeBottom)
	volArray.append(tempVol)
	areaArray.append(tempArea)
	st.write(f"Elevation: {elev}, Area: {tempArea}, Volume: {tempVol / 1e6} MCM")

	# Convert volumes to million cubic meters
	volArrayMCM = [round(vol / 1e6, 2) for vol in volArray]

	# Print results
	st.write("Elevation steps (m):", elevSteps)
	st.write("Volumes (MCM):", volArrayMCM)

	# Plot elevation vs volume
	fig, ax = plt.subplots(figsize=(12, 5))
	ax.plot(volArrayMCM, elevSteps, label='Lake Volume Curve')
	ax.grid(True)
	ax.legend()
	ax.set_xlabel('Volume (MCM)')
	ax.set_ylabel('Elevation (masl)')
	st.pyplot(fig)

	# Plot lake bottom elevation and volume curve side by side
	fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(20, 8), gridspec_kw={'width_ratios': [2, 1]})
	ax1.set_title('Lake Bottom Elevation')
	botElev = ax1.imshow(lakeBottom, cmap='viridis')

	divider = make_axes_locatable(ax1)
	cax = divider.append_axes('bottom', size='5%', pad=0.5)
	fig.colorbar(botElev, cax=cax, orientation='horizontal', label='Elevation (masl)')

	ax2.plot(volArrayMCM, elevSteps, label='Lake Volume Curve')
	ax2.grid(True)
	ax2.legend()
	ax2.set_xlabel('Volume (MCM)')
	ax2.set_ylabel('Elevation (masl)')
	st.pyplot(fig)

	# Print elevation and corresponding inundated area
	st.write("Elevation (m) Inundated Area (sq. meters)")
	for elev, area in zip(elevSteps, areaArray):
	st.write("{:.2f} {:.2f}".format(elev, area))

	st.write("Inundated Area (sq. meters) Volume (MCM)")
	for area, vol in zip(areaArray, volArrayMCM):
	st.write("{:.2f} {:.2f}".format(area, vol))

	# Plot the inundated area-volume curve
	fig, ax = plt.subplots(figsize=(10, 6))
	ax.plot(areaArray, volArrayMCM, label='Inundated Area-Volume Curve')
	ax.set_xlabel('Inundated Area (square meters)')
	ax.set_ylabel('Volume (MCM)')
	ax.grid(True)
	ax.legend()
	plt.title('Inundated Area-Volume Curve')
	st.pyplot(fig)
	# Create and display area-volume curve dictionary
	area_volume_curve = {}
	for area,vol in zip(areaArray, volArrayMCM):
	area_volume_curve[float(area)]= vol

	st.write(area_volume_curve)

	import datetime
	# Date input for filtering Sentinel-2 images
	startDate = st.sidebar.date_input("Start Date", value=None, min_value=None, max_value=None, key=None, help=None, on_change=None, args=None, kwargs=None, format="YYYY/MM/DD", disabled=False, label_visibility="visible")
	endDate = st.sidebar.date_input("End Date", value=datetime.datetime.now(), min_value=None, max_value=None, key=None, help=None, on_change=None, args=None, kwargs=None, format="YYYY/MM/DD", disabled=False, label_visibility="visible")
	# Sidebar selection for output
	output = st.sidebar.multiselect("Select the output",
	["Water Area", "Water Surface Elevation", "Water Volume",
	"Bathymetry file", "Timelapse", "Storage-Capacity curve"
	])

	st.sidebar.info("Choose the cloud coverage percentage of the satellite images")
	threshold = st.sidebar.slider("Cloud Percentage Threshold", 0, 20, 5)

	if st.sidebar.button("Start computing") and startDate and endDate and threshold:
	if "Timelapse" in output:
	with st.spinner('Creating Timelapse...'):
	# Export the GIF
	import geemap
	gif_path = "/Users/joaopimenta/Downloads/Master thesis/Python scripts/Test_gee/ndwi_timelapse.gif"
	Map = geemap.Map()
	Map.add_landsat_ts_gif(layer_name='Timelapse', roi=roi, label=f'{lat}, {lng}', start_year=2021, end_year=2024, start_date='06-10', end_date='09-20', bands=['SWIR1', 'NIR', 'Red'], vis_params=None, dimensions=768, frames_per_second=2, font_size=30, font_color='white', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, out_gif=gif_path, download=True, apply_fmask=True, nd_bands=None, nd_threshold=0, nd_palette=['black', 'blue'])
	file_ = open(gif_path, "rb")
	contents = file_.read()
	data_url = base64.b64encode(contents).decode("utf-8")
	file_.close()

	st.markdown(
	f'<img src="data:image/gif;base64,{data_url}" alt="timelapse gif">',
	unsafe_allow_html=True,
	)
	# Convert the date objects to strings in the format expected by EE
	start_date_str = startDate.strftime('%Y-%m-%d')
	end_date_str = endDate.strftime('%Y-%m-%d')

	sentinel_image_collection = ee.ImageCollection('COPERNICUS/S2') \
	.filterBounds(roi) \
	.filterDate(start_date_str, end_date_str)

	sentinel_image = sentinel_image_collection \
	.sort('CLOUDY_PIXEL_PERCENTAGE') \
	.first() \
	.clip(roi)

	# Visualize using RGB
	m.addLayer(sentinel_image,
	{'min': 0.0, 'max': 2000, 'bands': ['B4', 'B3', 'B2']},
	'RGB')
	ndwi = sentinel_image.normalizedDifference(['B3', 'B8']).rename('NDWI')
	m.addLayer(ndwi,
	{'palette': ['red', 'yellow', 'green', 'cyan', 'blue']},
	'NDWI')

	# Create NDWI mask
	ndwi_threshold = ndwi.gte(0.0)
	ndwi_mask = ndwi_threshold.updateMask(ndwi_threshold)
	m.addLayer(ndwi_threshold,
	{'palette': ['black', 'white']},
	'NDWI Binary Mask')
	m.addLayer(ndwi_mask,
	{'palette': ['blue']},
	'NDWI Mask')
	if "Water Surface Elevation" in output:
	# Send a request to the Hydrocron API to get lake data in CSV format
	url = (
	"https://soto.podaac.earthdatacloud.nasa.gov/hydrocron/v1/timeseries?"
	"feature=PriorLake"
	f"&feature_id={lake_id}"
	f"&start_time={start_date_str}T00:00:00Z"
	f"&end_time={end_date_str}T00:00:00Z"
	"&output=csv"
	"&fields=time_str,wse"
	)
	# Request the data
	hydrocron_response = requests.get(url).json()

	# Extract CSV data from the response
	csv_str = hydrocron_response['results']['csv']

	# Convert the CSV string into a pandas DataFrame
	df = pd.read_csv(StringIO(csv_str))

	# Prepare to plot water surface elevation (WSE) and area
	# Convert 'time_str' column to datetime format
	df['time_str'] = pd.to_datetime(df['time_str'], errors='coerce')

	# Filter and store dates and elevations
	df_filtered = df.dropna(subset=['time_str', 'wse'])
	df_filtered['wse'] = pd.to_numeric(df_filtered['wse'], errors='coerce')
	df_filtered = df_filtered.dropna(subset=['wse'])

	# Plot water surface elevation (WSE) over time
	plt.figure(figsize=(10, 5))
	plt.plot(df_filtered['time_str'], df_filtered['wse'], marker='o', linestyle='-')
	plt.xlabel('Date')
	plt.ylabel('Water Surface Elevation (m)')
	plt.title(f'Water Surface Elevation for Lake {lake_name}')
	plt.xticks(rotation=45)
	plt.grid(True)

	# Show the plot in Streamlit
	st.pyplot(plt)

	# Display the filtered DataFrame
	st.write(df_filtered[['time_str', 'wse']])

	with st.spinner('Retrieving satilite images...'):
	#Define a function to calculate NDWI
	def calculate_ndwi(image):
	ndwi = image.normalizedDifference(["B8", "B3"]) # B8 is NIR and B3 is green
	return ndwi
	# Filter Sentinel-2 images
	sentinelImageCollection = ee.ImageCollection('COPERNICUS/S2') \
	.filterBounds(roi) \
	.filterDate(start_date_str, end_date_str) \
	.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', threshold)) \

	# Check if images are available
	num_images = sentinelImageCollection.size().getInfo()
	st.write("Number of images:", num_images)
	volumes = []
	# Alternatively, convert acquisition times to readable format (if needed)
	acquisition_times = sentinelImageCollection.aggregate_array('system:time_start').getInfo()
	acquisition_dates = [datetime.datetime.utcfromtimestamp(time / 1000).strftime('%Y-%m-%d') for time in acquisition_times]

	if num_images == 0:
	st.warning("No images available within the specified date range.")
	else:
	if threshold >= 15:
	st.write("CLoudless Algorithm will identify and remove the effects of clouds and shadows")

	START_DATE = start_date_str
	END_DATE = end_date_str
	CLOUD_FILTER = 40
	CLD_PRB_THRESH = 70
	NIR_DRK_THRESH = 0.15
	CLD_PRJ_DIST = 2
	BUFFER = 100
	# Function to get Sentinel-2 surface reflectance and cloud probability collections
	def get_s2_sr_cld_col(aoi, start_date, end_date):
	s2_sr_col = (ee.ImageCollection('COPERNICUS/S2_SR')
	.filterBounds(aoi)
	.filterDate(start_date, end_date)
	.filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', CLOUD_FILTER)))

	s2_cloudless_col = (ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')
	.filterBounds(aoi)
	.filterDate(start_date, end_date))

	return ee.ImageCollection(ee.Join.saveFirst('s2cloudless').apply(**{
	'primary': s2_sr_col,
	'secondary': s2_cloudless_col,
	'condition': ee.Filter.equals(**{
	'leftField': 'system:index',
	'rightField': 'system:index'
	})
	}))

	# Apply the function to build the collection
	s2_sr_cld_col = get_s2_sr_cld_col(roi, START_DATE, END_DATE)

	# Function to get cloud cover percentage for an image
	def get_cloud_cover_percentage(image):
	cloud_cover = ee.Image(image).get('CLOUDY_PIXEL_PERCENTAGE')
	return ee.Feature(None, {'cloud_cover': cloud_cover, 'image_id': image.id()})

	# Apply the function to the collection
	image_list = s2_sr_cld_col.map(get_cloud_cover_percentage).getInfo()

	# Debug: Print the properties of the first image to inspect the available properties
	print("Inspecting the first image's properties:")
	print(image_list['features'][0]['properties'])

	# Extract the image ids, cloud covers, and dates (if available)
	image_info = []
	for f in image_list['features']:
	image_id = f['properties'].get('image_id', 'Unknown')
	cloud_cover = f['properties'].get('cloud_cover', 'Unknown')
	timestamp = f['properties'].get('system:time_start', None)

	# If timestamp is None, we'll set it to 'Unknown'
	if timestamp:
	date = datetime.utcfromtimestamp(timestamp / 1000).strftime('%Y-%m-%d')
	else:
	date = 'Unknown'

	image_info.append((image_id, cloud_cover, date))

	print("Available images and their cloud cover percentages:")
	for idx, (image_id, cloud_cover, date) in enumerate(image_info):
	print(f"{idx}: Image ID: {image_id}, Date: {date}, Cloud Cover: {cloud_cover}%")

	water_area_info = []
	# Count the number of images in the collection
	num_images = s2_sr_cld_col.size().getInfo()
	print(f"Total number of images in the collection: {num_images}")
	# Loop through each image in the collection and print its cloud cover
	for i in range(num_images):
	selected_idx = i
	selected_image_id = image_info[selected_idx][0]
	cloud_cover = image_info[selected_idx][1] # Get the cloud cover for the selected image
	selected_image = ee.Image(s2_sr_cld_col.filter(ee.Filter.eq('system:index', selected_image_id)).first())
	print(f"Image ID: {selected_image_id}, Cloud Cover: {cloud_cover}%")
	if cloud_cover >= 15:
	# Define functions to add cloud and shadow bands
	def add_cloud_bands(img):
	cld_prb = ee.Image(img.get('s2cloudless')).select('probability')
	is_cloud = cld_prb.gt(CLD_PRB_THRESH).rename('clouds')
	return img.addBands(ee.Image([cld_prb, is_cloud]))

	def add_shadow_bands(img):
	not_water = img.select('SCL').neq(6)
	SR_BAND_SCALE = 1e4
	dark_pixels = img.select('B8').lt(NIR_DRK_THRESH * SR_BAND_SCALE).multiply(not_water).rename('dark_pixels')
	shadow_azimuth = ee.Number(90).subtract(ee.Number(img.get('MEAN_SOLAR_AZIMUTH_ANGLE')))
	cld_proj = (img.select('clouds').directionalDistanceTransform(shadow_azimuth, CLD_PRJ_DIST * 10)
	.reproject(crs=img.select(0).projection(), scale=100)
	.select('distance').mask().rename('cloud_transform'))
	shadows = cld_proj.multiply(dark_pixels).rename('shadows')
	return img.addBands(ee.Image([dark_pixels, cld_proj, shadows]))

	def add_cld_shdw_mask(img):
	img_cloud = add_cloud_bands(img)
	img_cloud_shadow = add_shadow_bands(img_cloud)
	is_cld_shdw = img_cloud_shadow.select('clouds').add(img_cloud_shadow.select('shadows')).gt(0)
	is_cld_shdw = (is_cld_shdw.focalMin(2).focalMax(BUFFER * 2 / 20)
	.reproject(crs=img.select([0]).projection(), scale=20)
	.rename('cloudmask'))
	return img_cloud_shadow.addBands(is_cld_shdw)

	# Define the function to apply the cloud and shadow mask
	def apply_cld_shdw_mask(img):
	not_cld_shdw = img.select('cloudmask').Not()
	return img.select('B.*').updateMask(not_cld_shdw)

	# Add cloud and shadow bands, apply the mask
	selected_image_with_mask = add_cld_shdw_mask(selected_image)
	cloud_free_image = apply_cld_shdw_mask(selected_image_with_mask)

	# Define a function to calculate NDWI and mask
	def calculate_ndwi_and_mask(image):
	ndwi = image.normalizedDifference(['B3', 'B8']).rename('NDWI')
	ndwi_threshold = ndwi.gte(0.0)
	ndwi_mask = ndwi_threshold.updateMask(ndwi_threshold)
	return ndwi_mask

	# Apply the function to the latest image to calculate NDWI mask
	ndwi_mask = calculate_ndwi_and_mask(selected_image)

	# Define a function to calculate water area
	def calculate_water_area(image):
	water_area = image.multiply(ee.Image.pixelArea()).reduceRegion(
	reducer=ee.Reducer.sum(),
	geometry=roi,
	scale=5
	).get('NDWI')
	return image.set('water_area', water_area)

	# Calculate water area for the NDWI mask
	ndwi_mask_with_area = calculate_water_area(ndwi_mask)

	waterarea = ndwi_mask_with_area.get('water_area').getInfo()
	w = waterarea
	#print(f"This is the water area of the NDWI image:{w}")
	# Load the bathymetry dataset from Earth Engine
	globathy = ee.Image("projects/sat-io/open-datasets/GLOBathy/GLOBathy_bathymetry")

	# Export the data as an image
	out_dir = "/Users/joaopimenta/Desktop/GEE_test" # Specify the output directory
	if not os.path.exists(out_dir):
	os.makedirs(out_dir)
	out_image_path = os.path.join(out_dir, "globathy_bathymetry.tif") # Specify the output image path

	# Export the image
	geemap.ee_export_image(globathy, filename=out_image_path, scale=10, region=roi)

	# Load Bathymetry image
	bathymetry_path = out_image_path
	bathymetry_dataset = rasterio.open(bathymetry_path)

	#print(f"This is the ndwi area of the lake {ndwi_masked_area}")
	# Export the binary water mask to a GeoTIFF file
	folder_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + "_dam_volume_images_tif"
	directory = "/Users/joaopimenta/Desktop"
	folder_path = os.path.join(directory, folder_name)
	os.makedirs(folder_path)

	geemap.ee_export_image(
	ndwi_mask,
	filename=os.path.join(folder_path, "binary_NDWI.tif"),
	region=roi,
	scale=10
	)

	file_name = "binary_NDWI.tif"
	file_path = os.path.join(folder_path, file_name)

	# Load NDWI image
	ndwi_path = file_path # Update this path
	ndwi_dataset = rasterio.open(ndwi_path)
	ndwi = ndwi_dataset.read(1)

	with rasterio.open(file_path) as src:
	ndwi_data = src.read(1) # Read the first band
	transform = src.transform

	# Convert NDWI to binary format for visualization
	binary_ndwi = np.where(ndwi_data == 1, 255, 0).astype(np.uint8)

	# Calculate the area of the detected water bodies from binary mask
	def calculate_area(image, transform):
	# Mask the image to include only water
	water_mask = image == 0

	# Compute the area in square meters
	pixel_area = abs(transform[0] * transform[4]) # pixel size (in square meters)
	water_area_pixels = np.sum(water_mask)
	total_area_m2 = water_area_pixels * pixel_area
	return total_area_m2

	# Calculate the area using the converted binary mask
	total_area_m2 = calculate_area(binary_ndwi, transform)/ 1e3
	#print(f"Total area calculated from binary mask: {total_area_m2 :.2f} km²")

	# Plot the results
	plt.figure(figsize=(15, 10))

	# Binary NDWI (K-means method)
	plt.subplot(1, 2, 1)
	plt.imshow(binary_ndwi, cmap='gray')
	plt.title('Binary NDWI (K-means)')

	# Identified contour (K-means method)
	plt.subplot(1, 2, 2)
	contour_image = np.zeros_like(binary_ndwi)
	contours, _ = cv2.findContours(binary_ndwi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
	if contours:
	cv2.drawContours(contour_image, [max(contours, key=cv2.contourArea)], -1, (255), 2)
	plt.imshow(contour_image, cmap='gray')
	plt.title('Identified Dam Contour (K-means)')

	plt.show()
	# Check for cloud pixels within the dam (ROI)
	cloud_pixels_in_roi = selected_image_with_mask.select('cloudmask').reduceRegion(
	reducer=ee.Reducer.sum(),
	geometry=roi,
	scale=10
	).get('cloudmask').getInfo()

	print(f"This is the cloud pixels in the ROI:{cloud_pixels_in_roi}")
	# Export the cloud mask
	geemap.ee_export_image(
	selected_image_with_mask.select('cloudmask'),
	filename=os.path.join(folder_path, "cloud_mask.tif"),
	region=roi,
	scale=10
	)

	cloud_mask_path = os.path.join(folder_path, "cloud_mask.tif")
	cloud_mask_dataset = rasterio.open(cloud_mask_path)
	cloud_mask = cloud_mask_dataset.read(1)

	# Reproject the cloud mask to match NDWI resolution
	resampled_cloud_mask = np.empty_like(ndwi)

	reproject(
	source=cloud_mask,
	destination=resampled_cloud_mask,
	src_transform=cloud_mask_dataset.transform,
	src_crs=cloud_mask_dataset.crs,
	dst_transform=ndwi_dataset.transform,
	dst_crs=ndwi_dataset.crs,
	resampling=Resampling.nearest)

	# Mask the NDWI image by removing cloud pixels
	ndwi_masked = np.where(resampled_cloud_mask == 0, ndwi, np.nan)

	# Load Bathymetry image
	bathymetry_dataset = rasterio.open(bathymetry_path)

	# Reproject Bathymetry to the NDWI CRS
	dst_crs = ndwi_dataset.crs

	transform, width, height = calculate_default_transform(
	bathymetry_dataset.crs, dst_crs, bathymetry_dataset.width,
	bathymetry_dataset.height, *bathymetry_dataset.bounds)
	kwargs = bathymetry_dataset.meta.copy()
	kwargs.update({
	'crs': dst_crs,
	'transform': transform,
	'width': width,
	'height': height
	})

	reprojected_bathymetry = np.empty((height, width), dtype=np.float32)

	reproject(
	source=rasterio.band(bathymetry_dataset, 1),
	destination=reprojected_bathymetry,
	src_transform=bathymetry_dataset.transform,
	src_crs=bathymetry_dataset.crs,
	dst_transform=transform,
	dst_crs=dst_crs,
	resampling=Resampling.nearest)

	# Resample Bathymetry to match NDWI resolution
	resampled_bathymetry = np.empty_like(ndwi)

	reproject(
	source=reprojected_bathymetry,
	destination=resampled_bathymetry,
	src_transform=transform,
	src_crs=dst_crs,
	dst_transform=ndwi_dataset.transform,
	dst_crs=dst_crs,
	resampling=Resampling.bilinear)

	# Plot the images
	fig, ax = plt.subplots(figsize=(10, 10))

	# Plot the NDWI image
	ndwi_extent = (ndwi_dataset.bounds.left, ndwi_dataset.bounds.right,
	ndwi_dataset.bounds.bottom, ndwi_dataset.bounds.top)
	cax_ndwi = ax.imshow(ndwi_masked, cmap='Blues', extent=ndwi_extent,
	alpha=0.6)

	# Overlay the Bathymetry image
	bathy_extent = (ndwi_dataset.bounds.left, ndwi_dataset.bounds.right,
	ndwi_dataset.bounds.bottom, ndwi_dataset.bounds.top)
	cax_bathy = ax.imshow(resampled_bathymetry, cmap='viridis',
	extent=bathy_extent, alpha=0.4)
	fig.colorbar(cax_bathy, ax=ax, fraction=0.046, pad=0.04,
	label='Bathymetry')

	# Plot the NDWI cloud-removed image
	fig, ax = plt.subplots(figsize=(10, 10))

	# Plot the NDWI image
	ndwi_extent = (ndwi_dataset.bounds.left, ndwi_dataset.bounds.right, ndwi_dataset.bounds.bottom, ndwi_dataset.bounds.top)
	cax_ndwi = ax.imshow(ndwi_masked, cmap='Blues', extent=ndwi_extent)
	fig.colorbar(cax_ndwi, ax=ax, fraction=0.046, pad=0.04, label='NDWI')

	# Get the cloud mask from the selected image
	cloud_mask = selected_image_with_mask.select('cloudmask')

	# Apply cloud mask to the NDWI mask
	ndwi_cloud_removed_mask = ndwi_mask.updateMask(cloud_mask.Not())

	# Calculate the pixel area for the masked NDWI image
	pixel_area = ndwi_cloud_removed_mask.multiply(ee.Image.pixelArea())

	# Reduce the region to calculate the total water area
	water_area = pixel_area.reduceRegion(
	reducer=ee.Reducer.sum(),
	geometry=roi,
	scale=10, # Adjust the scale as needed
	maxPixels=1e10
	)

	# Assuming water_area is the result from reduceRegion
	water = water_area.getInfo().get('NDWI')
	print(water)
	# Get the total water area in square meters
	total_water_area_m2 = total_area_m2

	# Convert the area to square kilometers
	total_water_area_km2 = total_water_area_m2 / 1e6# Convert the area to square kilometers
	total_water_area_adjusted = total_water_area_m2

	area_cloud_aftected = w - total_water_area_adjusted
	cloud_affect_percentage = area_cloud_aftected/ cloud_pixels_in_roi

	print(f"The total NDWI water area is:{w}")
	print(f"The adjusted water area is: {total_water_area_adjusted}")
	print(f"The total amount of pixels covering the reservoir is:{area_cloud_aftected}")
	print(f"This is the area cloud pixels in the ROI:{cloud_pixels_in_roi*10}")
	print(f"The percentage of pixels which affect the reservoir's area are :{cloud_affect_percentage}")

	if cloud_pixels_in_roi > 0:
	import rasterio
	import numpy as np
	import matplotlib.pyplot as plt
	from skimage import measure
	from shapely.geometry import Polygon
	from pyproj import Transformer
	import rasterio.transform
	from scipy.ndimage import binary_fill_holes # To fill inside polygons
	from rasterio.warp import reproject, Resampling, calculate_default_transform

	# Path to bathymetry raster file
	path_bathymetry = "/Users/joaopimenta/Desktop/GEE_test/globathy_bathymetry.tif"
	# Path to NDWI raster file (the one with the projection you want)
	path_ndwi = ndwi_path
	path_cloud_mask = cloud_mask_path

	# Load Bathymetry image
	bathymetry_dataset = rasterio.open(path_bathymetry)
	cloud_mask_dataset = rasterio.open(path_cloud_mask)
	ndwi_dataset = rasterio.open(path_ndwi)

	# Reproject Bathymetry to the NDWI CRS if necessary
	dst_crs = ndwi_dataset.crs

	transform, width, height = calculate_default_transform(
	bathymetry_dataset.crs, dst_crs, bathymetry_dataset.width, bathymetry_dataset.height, *bathymetry_dataset.bounds)

	kwargs = bathymetry_dataset.meta.copy()
	kwargs.update({
	'crs': dst_crs,
	'transform': transform,
	'width': width,
	'height': height
	})

	reprojected_bathymetry = np.empty((height, width), dtype=np.float32)

	reproject(
	source=rasterio.band(bathymetry_dataset, 1),
	destination=reprojected_bathymetry,
	src_transform=bathymetry_dataset.transform,
	src_crs=bathymetry_dataset.crs,
	dst_transform=transform,
	dst_crs=dst_crs,
	resampling=Resampling.nearest)

	# Reproject cloud mask to the bathymetry CRS if necessary
	if bathymetry_dataset.crs != cloud_mask_dataset.crs:
	print("CRS misalignment detected. Reprojecting cloud mask to bathymetry CRS.")
	reprojected_cloud_mask = np.empty_like(reprojected_bathymetry)
	reproject(
	source=rasterio.band(cloud_mask_dataset, 1),
	destination=reprojected_cloud_mask,
	src_transform=cloud_mask_dataset.transform,
	src_crs=cloud_mask_dataset.crs,
	dst_transform=transform,
	dst_crs=dst_crs,
	resampling=Resampling.nearest
	)
	else:
	reprojected_cloud_mask = cloud_mask_dataset.read(1)

	# Resample Bathymetry and cloud mask to match NDWI resolution if necessary
	resampled_bathymetry = np.empty_like(ndwi_dataset.read(1))
	resampled_cloud_mask = np.empty_like(ndwi_dataset.read(1))

	reproject(
	source=reprojected_bathymetry,
	destination=resampled_bathymetry,
	src_transform=transform,
	src_crs=dst_crs,
	dst_transform=ndwi_dataset.transform,
	dst_crs=dst_crs,
	resampling=Resampling.bilinear)

	reproject(
	source=reprojected_cloud_mask,
	destination=resampled_cloud_mask,
	src_transform=transform,
	src_crs=dst_crs,
	dst_transform=ndwi_dataset.transform,
	dst_crs=dst_crs,
	resampling=Resampling.nearest)

	# Load bathymetry raster data
	lakeBottom = resampled_bathymetry
	resolution = bathymetry_dataset.res
	lake_crs = bathymetry_dataset.crs
	lake_transform = bathymetry_dataset.transform

	# Load NDWI raster data (to get CRS and extent)
	ndwi_extent = (ndwi_dataset.bounds.left, ndwi_dataset.bounds.right, ndwi_dataset.bounds.bottom, ndwi_dataset.bounds.top)

	# Replace no-data value with np.nan for the bathymetry raster
	noDataValue = lakeBottom[0, 0]
	lakeBottom = lakeBottom.astype(float)
	lakeBottom[lakeBottom == noDataValue] = np.nan

	# Calculate minimum and maximum elevation
	minElev = np.nanmin(lakeBottom)
	maxElev = np.nanmax(lakeBottom)

	# Define number of steps for calculation
	nSteps = 50
	elevSteps = np.round(np.linspace(minElev, maxElev, nSteps), 2)

	# Define function to create a mask for a specific elevation
	def createMaskForElevation(elevation, elevDem, cloud_mask):
	# Create a mask based on the elevation
	mask = np.where(elevDem <= elevation, 1, 0)

	# Fill holes inside the polygon
	filled_mask = binary_fill_holes(mask) * mask # Ensures it's a binary mask

	waterarea = np.sum(mask) * (resolution[0] * resolution[1])
	area_Array.append(waterarea)
	# Apply the cloud mask, setting cloud-covered pixels to 0
	filled_mask[cloud_mask == 1] = 0

	return filled_mask

	# Set up transformation to match NDWI CRS
	transformer = Transformer.from_crs(lake_crs, ndwi_dataset.crs, always_xy=True)

	# Arrays to store the areas for each elevation step
	areaArray = []
	area_Array = []

	# Plot setup
	fig, ax = plt.subplots(figsize=(12, 10))
	colors = plt.cm.viridis(np.linspace(0, 1, len(elevSteps)))

	for i, elev in enumerate(elevSteps):
	# Create a mask for the current elevation and apply cloud mask
	mask = createMaskForElevation(elev, lakeBottom, resampled_cloud_mask)

	# Calculate water area by summing valid pixels (non-cloud, non-zero)
	water_area = np.sum(mask) * (resolution[0] * resolution[1]) # Pixel resolution area

	areaArray.append(water_area)

	# Find contours (polygons) from the mask
	contours = measure.find_contours(mask, 0.5)

	# Reproject and plot each contour as a polygon
	for contour in contours:
	lon_lat_coords = rasterio.transform.xy(lake_transform, contour[:, 0], contour[:, 1])
	x_coords, y_coords = np.array(lon_lat_coords[0]), np.array(lon_lat_coords[1])

	# Reproject coordinates to NDWI CRS
	x_proj, y_proj = transformer.transform(x_coords, y_coords)

	# Plot the reprojected contour
	ax.plot(x_proj, y_proj, color=colors[i], label=f'Elevation {elev} m' if i == 0 else "")

	# Set the same extent as the NDWI image
	ax.set_xlim(ndwi_extent[0], ndwi_extent[1])
	ax.set_ylim(ndwi_extent[2], ndwi_extent[3])

	# Plot the elevation vs area
	areaArraySqM = [area * 1e8 for area in areaArray] # Convert to square meters
	area_ArraySqM = [area * 1e8 for area in area_Array]
	# Paths to the raster file
	path = "/Users/joaopimenta/Desktop/GEE_test/globathy_bathymetry.tif"

	# Load the raster data
	lakeRst = rasterio.open(path)
	lakeBottom = lakeRst.read(1)

	# Raster resolution (in meters, assuming UTM projection)
	resolution = lakeRst.res
	print("Resolution:", resolution)

	# Replace no-data value with np.nan
	noDataValue = np.copy(lakeBottom[0, 0])
	lakeBottom = lakeBottom.astype(float)
	lakeBottom[lakeBottom == noDataValue] = np.nan
	# Get the pixel size from raster resolution (in meters)
	pixelArea = lakeRst.res[0] * lakeRst.res[1] # in square meters

	# Calculate the area of the detected water bodies from binary mask
	def calculate_area(image, transform):
	# Mask the image to include only water
	water_mask = image == 0

	# Compute the area in square meters
	pixel_area = abs(transform[0] * transform[4]) # pixel size (in square meters)
	water_area_pixels = np.sum(water_mask)
	total_area_m2 = water_area_pixels * pixel_area
	return total_area_m2
	# Define function to create mask for a specific elevation
	def createMaskForElevation(elevation, elevDem):
	mask = np.where(elevDem <= elevation, 1, 0) # White pixels for inundated area
	return mask

	# Arrays to store the areas for each elevation step
	area_normal_Array = []

	# Plot all polygons representing water area for each elevation step
	fig, ax = plt.subplots(figsize=(12, 10))

	# Colors for different elevation levels
	colors = plt.cm.viridis(np.linspace(0, 1, len(elevSteps)))

	# Loop over each elevation step, calculate area, and plot polygons
	for i, elev in enumerate(elevSteps):
	# Create a mask for the current elevation step
	mask = createMaskForElevation(elev, lakeBottom)

	# Calculate the water area at this elevation
	waterArea = np.sum(mask) * pixelArea # sum of all '1' pixels * pixel area
	area_normal_Array.append(waterArea) # Store the area for this elevation

	# Find contours (polygons) from the mask
	contours = measure.find_contours(mask, 0.5)

	# Plot each contour as a polygon
	for contour in contours:
	# Transform contour coordinates to UTM coordinates using the raster transform
	utm_coords = rasterio.transform.xy(lakeRst.transform, contour[:, 0], contour[:, 1])
	x_coords, y_coords = np.array(utm_coords[0]), np.array(utm_coords[1])

	# Plot the polygon for the current elevation step
	ax.plot(x_coords, y_coords, color=colors[i], label=f'Elevation {elev} m' if i == 0 else "")

	# Plot the elevation vs area
	# Multiply the area by 1,000,000 to convert from km² to m² if necessary
	area_normal_ArraySqM = [area * 1e8 for area in area_normal_Array] # Convert to square meters

	# Function to create a binary mask for the chosen elevation
	def createMaskForElevation(elevation, elevDem, resolution):
	# Step 1: Generate the initial binary mask (1 for water, 0 for no water)
	mask = np.where(elevDem <= elevation, 1, 0)

	# Step 2: Fill the holes inside the lake region
	filled_mask = binary_fill_holes(mask) * mask # Fill holes only inside the mask

	# Step 3: Create a mask for the lake region (anything inside the boundary is considered lake)
	lake_mask = np.where(np.isnan(elevDem), 1, 0) # NaN represents outside the lake

	# Step 4: Assign a value of 1 to everything outside the lake region
	result_mask = np.where(lake_mask == 1, 1, filled_mask)

	# Step 5: Calculate the inundated area for the white pixels inside the lake
	area = np.sum(filled_mask) * resolution[0] * resolution[1]

	return result_mask, area
	# Ensure differences, areaArraySqM, and elev are arrays or lists
	differences = []

	for area in areaArraySqM:
	dif = abs(water - area) # Absolute difference
	differences.append(dif)

	print(f"The water area without the cloud pixels is: {water}")

	# Find the index of the smallest difference
	best_match_index = differences.index(min(differences))
	best_match = area_ArraySqM[best_match_index]

	# Reverse the elevation steps and convert to a list to allow indexing
	step_elevation_reversed = list(reversed(elevSteps))

	# Allow user to input a specific elevation based on the best match index
	specificElevation = step_elevation_reversed[best_match_index]

	# Generate the binary mask and calculate the area for the selected elevation
	maskForSpecificElevation, specificArea = createMaskForElevation(specificElevation, lakeBottom, resolution)

	# Load NDWI image and bathymetry mask
	ndwi_dataset = rasterio.open(path_ndwi) # Path to NDWI image
	ndwi_crs = ndwi_dataset.crs
	ndwi_transform = ndwi_dataset.transform
	ndwi_res = ndwi_dataset.res

	# Ensure the mask for the specific elevation is reprojected to the NDWI's CRS, extent, and resolution
	mask_for_elevation_reprojected = np.empty_like(ndwi_dataset.read(1))

	reproject(
	source=maskForSpecificElevation, # Mask to reproject
	destination=mask_for_elevation_reprojected,
	src_transform=lake_transform, # Transform from the bathymetry mask
	src_crs=lake_crs, # CRS of the mask
	dst_transform=ndwi_transform, # NDWI transform
	dst_crs=ndwi_crs, # NDWI CRS
	resampling=Resampling.nearest # Nearest neighbor interpolation for binary masks
	)

	# Now overlay the NDWI image with the mask
	ndwi_image = ndwi_dataset.read(1) # Read the NDWI image (band 1)

	# Assign value 1 to NDWI where mask is 1
	ndwi_image[mask_for_elevation_reprojected == 0] = 1

	# Save the NDWI image locally as a GeoTIFF
	output_path = '/Users/joaopimenta/Desktop/GEE_test/reconstructed_plygon.tif' # Define the output path for the saved image

	# Retrieve the metadata from the NDWI dataset to use it for saving the file
	meta = ndwi_dataset.meta.copy()

	# Update metadata for a single band output
	meta.update({
	'dtype': 'float32', # or 'uint8' depending on the NDWI data type
	'count': 1, # Number of bands
	'driver': 'GTiff', # Save as a GeoTIFF file
	'crs': ndwi_crs, # Coordinate reference system
	'transform': ndwi_transform # Affine transform for georeferencing
	})

	# Save the NDWI image with the mask applied as a GeoTIFF
	with rasterio.open(output_path, 'w', **meta) as dst:
	dst.write(ndwi_image.astype('float32'), 1) # Write the NDWI data to band 1
	print(f'Saved NDWI image as {output_path}')

	# Create a figure with 2 subplots side by side
	fig, axes = plt.subplots(1, 2, figsize=(12, 6)) # 1 row, 2 columns

	# Plot the first contour: Identified contour (K-means method) on binary_ndwi
	contour_image_binary = np.zeros_like(binary_ndwi)
	contours_binary, _ = cv2.findContours(binary_ndwi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
	if contours_binary:
	cv2.drawContours(contour_image_binary, [max(contours_binary, key=cv2.contourArea)], -1, (255), 2)

	axes[0].imshow(contour_image_binary, cmap='gray')
	axes[0].set_title('Polygon of the NDWI affected by clouds')

	# Plot the second contour: Identified contour (K-means method) on ndwi_image
	contour_image_ndwi = np.zeros_like(ndwi_image)
	contours_ndwi, _ = cv2.findContours(ndwi_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
	if contours_ndwi:
	cv2.drawContours(contour_image_ndwi, [max(contours_ndwi, key=cv2.contourArea)], -1, (255), 2)

	axes[1].imshow(contour_image_ndwi, cmap='gray')
	axes[1].set_title('Polygon of the reconstructed Image')

	# Show the plots
	plt.tight_layout()
	plt.show()

	# Create contour image for binary NDWI
	contour_image_binary = np.zeros_like(binary_ndwi)
	contours_binary, _ = cv2.findContours(binary_ndwi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
	if contours_binary:
	cv2.drawContours(contour_image_binary, [max(contours_binary, key=cv2.contourArea)], -1, (255), 2)

	# Create contour image for NDWI image
	contour_image_ndwi = np.zeros_like(ndwi_image)
	contours_ndwi, _ = cv2.findContours(ndwi_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
	if contours_ndwi:
	# Draw only the largest contour for the NDWI image
	largest_contour = max(contours_ndwi, key=cv2.contourArea)
	cv2.drawContours(contour_image_ndwi, [max(contours_ndwi, key=cv2.contourArea)], -1, (255), 2)

	# Stack the binary contour and NDWI contour into a 3-channel image for overlay (Red, Green, Blue)
	overlay_image = np.zeros((contour_image_binary.shape[0], contour_image_binary.shape[1], 3), dtype=np.uint8)
	overlay_image[..., 0] = contour_image_binary # Red channel for binary NDWI contour
	overlay_image[..., 1] = contour_image_ndwi # Green channel for NDWI image contour

	# Create a mask to fill the inside of the largest NDWI contour
	mask = np.zeros_like(ndwi_image, dtype=np.uint8)
	if contours_ndwi:
	cv2.drawContours(mask, [largest_contour], -1, (255), thickness=cv2.FILLED)

	# Create a new output image, initialized to zeros (0 for a black image)
	output_image = np.zeros_like(ndwi_image, dtype=np.uint8)

	# Set the inside of the largest contour to one (255)
	output_image[mask == 255] = 1 # Change to fill with pixel value 1

	# Optionally convert to uint8 range for visualization
	output_image *= 255 # If you need the output image to be in the 0-255 range

	# Save the NDWI image locally as a GeoTIFF
	output_path = '/Users/joaopimenta/Desktop/GEE_test/reconstructed_water_mask.tif' # Define the output path for the saved image

	# Retrieve the metadata from the NDWI dataset to use it for saving the file
	meta = ndwi_dataset.meta.copy()

	# Update metadata for a single band output
	meta.update({
	'dtype': 'float32', # or 'uint8' depending on the NDWI data type
	'count': 1, # Number of bands
	'driver': 'GTiff', # Save as a GeoTIFF file
	'crs': ndwi_crs, # Coordinate reference system
	'transform': ndwi_transform # Affine transform for georeferencing
	})

	# Save the NDWI image with the mask applied as a GeoTIFF
	with rasterio.open(output_path, 'w', **meta) as dst:
	dst.write(output_image.astype('float32'), 1) # Write the NDWI data to band 1
	print(f'Saved NDWI image as {output_path}')

	# If resolution is a tuple (x_resolution, y_resolution)
	x_resolution, y_resolution = resolution
	pixel_area = x_resolution * y_resolution # Area of one pixel in square meters

	# Count water pixels
	water_pixels = ndwi_image > 0
	water_pixel_count = np.sum(water_pixels)

	# Calculate total water area
	total_water_area = water_pixel_count * pixel_area*1e8

	# Print the result
	print(f'Total water area: {total_water_area} square meters')
	water_area_info.append(total_water_area)
	# Optionally, save the modified NDWI image as a new file
	out_meta = ndwi_dataset.meta.copy()
	with rasterio.open('ndwi_with_elevation_mask.tif', 'w', **out_meta) as dst:
	dst.write(ndwi_image, 1) # Write the new image to disk

	# Close datasets
	ndwi_dataset.close()
	else:
	print("There are no cloud pixels inside the reservoir's area.")
	water_area_info.append(waterarea)

	st.write(water_area_info)
	else:
	# Options for confirming the reservoir selection
	water_method = ['Fixed thershold', 'Dynamic']

	# Select box to confirm selection
	water = st.selectbox("Choose this method of identifying water pixels", water_method)

	def extract_bbox_from_aoi(aoi):
	# Get the bounding box of the AOI
	bounds = aoi.bounds().getInfo()

	# Extract the coordinates based on the observed structure
	try:
	lon_min = bounds['coordinates'][0][0][0] # First point's longitude
	lat_min = bounds['coordinates'][0][0][1] # First point's latitude
	lon_max = bounds['coordinates'][0][2][0] # Third point's longitude
	lat_max = bounds['coordinates'][0][2][1] # Third point's latitude

	return lat_min, lon_min, lat_max, lon_max
	except (IndexError, KeyError, TypeError):
	print("Unexpected bounds structure:", bounds)
	raise ValueError("Unable to extract bounding box; check structure of bounds data.")

	lat_min, lon_min, lat_max, lon_max = extract_bbox_from_aoi(roi)

	# Function to query bridges
	def check_bridge_in_area(lat_min, lon_min, lat_max, lon_max):
	overpass_url = "http://overpass-api.de/api/interpreter"
	overpass_query = f"""
	[out:json];
	(
	way["man_made"="bridge"]({lat_min},{lon_min},{lat_max},{lon_max});
	node(w);
	);
	out body qt;
	"""

	print(f"Querying Overpass API with:\n{overpass_query}")

	response = requests.get(overpass_url, params={'data': overpass_query})

	if response.status_code == 200:
	print("Received response from Overpass API.")
	return response.json()
	else:
	print(f"Error: {response.status_code}")
	return None

	# Query the Overpass API for bridges in the bounding box
	bridge_data = check_bridge_in_area(lat_min, lon_min, lat_max, lon_max)

	# Initialize lists for GeoDataFrame
	bridge_names = []
	elem = None
	# Parse and display bridges with their shapes and names
	if bridge_data and 'elements' in bridge_data:
	node_coords = { # Store node coordinates for reference
	int(node['id']): (node['lat'], node['lon'])
	for node in bridge_data['elements']
	if node['type'] == 'node'
	}

	if len(node_coords) > 0:
	print(f"Node coordinates found: {node_coords}")
	else:
	print("No node coordinates found.")

	for element in bridge_data['elements']:
	if element['type'] == 'way':
	elem = element['type']
	bridge_name = element.get('tags', {}).get('name')
	if bridge_name: # Check if bridge has a name
	st.write(f"It was detected the bridge {bridge_name} inside the ROI")
	coords = [node_coords.get(node_id) for node_id in element.get('nodes', [])]
	coords = [coord for coord in coords if coord is not None] # Remove invalid coordinates
	else:
	st.write(f"Bridge ID: {element['id']} has no name and will be excluded.")
	else:
	st.write("No bridge data or 'elements' not in the response.")

	if water == 'Fixed thershold':
	# Define a function to calculate NDWI and mask for each image
	def calculate_ndwi_and_mask(image):
	ndwi = image.normalizedDifference(['B3', 'B8']).rename('NDWI')
	ndwi_threshold = ndwi.gte(0.0)
	ndwi_mask = ndwi_threshold.updateMask(ndwi_threshold)
	return ndwi_mask

	# Map the function over the image collection to get NDWI masks for each image
	ndwi_masks = sentinelImageCollection.map(calculate_ndwi_and_mask)

	# Perform erosion (shrinking the mask slightly to remove small gaps and noise)
	eroded_ndwi = ndwi_masks.map(lambda img: img.focal_min(radius=1, kernelType='circle', iterations=1))

	# Perform dilation after erosion (expanding the mask back to restore shape)
	closed_ndwi = eroded_ndwi.map(lambda img: img.focal_max(radius=1, kernelType='circle', iterations=1))
	# Now, closed_ndwi contains the NDWI masks that have been eroded and then dilated for each image in the collection.

	# Define a function to calculate water area
	def calculate_water_area(image):
	water_area = image.multiply(ee.Image.pixelArea()).reduceRegion(
	reducer=ee.Reducer.sum(),
	geometry=roi,
	bestEffort=True,
	scale=5
	).get('NDWI')
	return image.set('water_area', water_area)

	if elem is not None:
	# Map the function over the NDWI masks to calculate water area for each image
	ndwi_masks_with_area = closed_ndwi.map(calculate_water_area)
	else:
	# Map the function over the NDWI masks to calculate water area for each image
	ndwi_masks_with_area = ndwi_masks.map(calculate_water_area)

	# Get the water area information
	water_area_info = ndwi_masks_with_area.aggregate_array('water_area').getInfo()

	# Display the list of water areas
	#st.write(water_area_info)

	# Get acquisition dates in human-readable format
	dates = ndwi_masks_with_area.aggregate_array('system:time_start') \
	.map(lambda d: ee.Date(d).format('YYYY-MM-dd')).getInfo()

	# Display the dates
	#st.write("Acquisition dates for each image:", dates)

	# Alternatively, convert acquisition times to readable format (if needed)
	acquisition_times = sentinelImageCollection.aggregate_array('system:time_start').getInfo()
	acquisition_dates = [datetime.datetime.utcfromtimestamp(time / 1000).strftime('%Y-%m-%d') for time in acquisition_times]
	#st.write("Alternative acquisition dates:", acquisition_dates)

	else:
	# Define a function to calculate NDWI
	def calculate_ndwi(image):
	ndwi = image.normalizedDifference(['B3', 'B8']).rename('NDWI')
	return image.addBands(ndwi)

	# Define a function to sample NDWI values for clustering
	def sample_ndwi(image):
	ndwi = image.select('NDWI')
	sampled_ndwi = ndwi.sample(
	region=roi_geometry,
	scale=10,
	numPixels=10000,
	seed=0
	).select('NDWI')
	return sampled_ndwi

	# Define a function to perform K-means clustering
	def cluster_ndwi(sampled_ndwi):
	clusterer = ee.Clusterer.wekaKMeans(2).train(sampled_ndwi)
	return clusterer

	# Define a function to determine the water cluster
	def get_water_cluster(clustered_image):
	mean_ndwi_per_cluster = clustered_image.reduceRegion(
	reducer=ee.Reducer.mean(),
	geometry=roi_geometry,
	scale=10
	)
	mean_values = ee.List(mean_ndwi_per_cluster.values())
	water_cluster = mean_values.indexOf(mean_values.reduce(ee.Reducer.max()))
	return water_cluster

	# Define a function to create a binary water mask based on the cluster
	def create_water_mask(clustered_image, water_cluster):
	water_mask = clustered_image.eq(water_cluster).rename('water_mask')
	return water_mask

	# Define a function to compute the area of water bodies in square meters
	def compute_water_area(water_mask):
	water_area = water_mask.reduceRegion(
	reducer=ee.Reducer.sum(),
	geometry=roi_geometry,
	scale=10
	).get('water_mask')
	water_area = ee.Number(water_area).multiply(100).divide(1e4) # Convert to square kilometers
	return water_area

	if elem is not None:
	# Combine all functions into one for mapping
	def process_image(image):
	# Calculate NDWI
	image = calculate_ndwi(image)

	# Sample and cluster NDWI for water detection
	sampled_ndwi = sample_ndwi(image)
	clusterer = cluster_ndwi(sampled_ndwi)
	clustered_image = image.select('NDWI').cluster(clusterer).rename('cluster')

	# Determine which cluster represents water
	water_cluster = get_water_cluster(clustered_image)
	water_mask = create_water_mask(clustered_image, water_cluster)

	# Perform morphological operations (closing)
	eroded_ndwi = water_mask.focal_min(radius=1, kernelType='circle', iterations=1)
	closed_ndwi = eroded_ndwi.focal_max(radius=1, kernelType='circle', iterations=1)

	water_area = compute_water_area(closed_ndwi)

	return image.set('water_area_km2', water_area)
	else:
	# Combine all functions into one for mapping
	def process_image(image):
	image = calculate_ndwi(image)
	sampled_ndwi = sample_ndwi(image)
	clusterer = cluster_ndwi(sampled_ndwi)
	clustered_image = image.select('NDWI').cluster(clusterer).rename('cluster')
	water_cluster = get_water_cluster(clustered_image)
	water_mask = create_water_mask(clustered_image, water_cluster)
	water_area = compute_water_area(water_mask)

	return image.set('water_area_km2', water_area)

	# Apply the processing function to each image in the collection
	processed_images = sentinelImageCollection.map(process_image)

	# Extract the water area and date information
	water_area_info = processed_images.aggregate_array('water_area_km2').getInfo()
	dates = processed_images.aggregate_array('system:time_start').map(lambda d: ee.Date(d).format('YYYY-MM-dd')).getInfo()

	# Get acquisition times of the images
	acquisition_times = sentinelImageCollection.aggregate_array('system:time_start').getInfo()

	# Convert acquisition times to human-readable dates
	acquisition_dates = [datetime.datetime.utcfromtimestamp(time / 1000).strftime('%Y-%m-%d') for time in acquisition_times]

	if method == ("Write the A-V function of your reservoir"):

	for area in water_area_info:
	volume = None
	# Calculate the volume using the area-storage equation
	volume = (area / a) ** (1 / b)

	if volume is not None:
	volumes.append(volume)
	else:
	st.write("Error with the coefficients")

	st.write(f"The list of the volumes in cubic meters for the chosen dates is: {volumes}")

	elif method == ("upload excel sheet"):

	if dictionary:
	for area in water_area_info:
	volume = None
	keys = sorted(dictionary.keys())
	for i in range(len(keys)):
	key = keys[i]
	if key >= area:
	if i == 0:
	volume = dictionary[key]
	st.write(f"This is the volume {volume/10**6}km³")
	volumes.append(volume)
	else:
	prev_key = keys[i - 1]
	delta_volume = dictionary[key] - dictionary[prev_key]
	delta_key = key - prev_key
	delta_area = area - prev_key
	interpolated_volume = dictionary[prev_key] + (delta_volume * delta_area / delta_key)
	volume = (interpolated_volume/10**6)
	st.write(f"This is the volume {volume}km³")
	volumes.append(volume)
	break
	else:
	# This else block belongs to the for loop, not the if condition
	st.write("Dam value not found in the dictionary ")
	elif method == ("upload the DEM"):

	if dictionary:
	for area in water_area_info:
	volume = None
	keys = sorted(dictionary.keys())
	for i in range(len(keys)):
	key = keys[i]
	if key >= area:
	if i == 0:
	volume = dictionary[key]
	st.write(f"This is the volume {volume/10**6}km³")
	volumes.append(volume)
	else:
	prev_key = keys[i - 1]
	delta_volume = dictionary[key] - dictionary[prev_key]
	delta_key = key - prev_key
	delta_area = area - prev_key
	interpolated_volume = dictionary[prev_key] + (delta_volume * delta_area / delta_key)
	volume = (interpolated_volume/10**6)
	st.write(f"This is the volume {volume}km³")
	volumes.append(volume)
	break
	else:
	# This else block belongs to the for loop, not the if condition
	st.write("Dam value not found in the dictionary ")
	elif method == "Don't have that info":

	import netCDF4 as nc
	import numpy as np

	volumes =[]
	# Open the NetCDF file
	nc_file = nc.Dataset('/Users/joaopimenta/Downloads/Master thesis/GLOBathy_hAV_relationships.nc')

	# Specify the lake ID you want to search for
	target_lake_id = hydrolakes_id # Replace this with the actual lake ID you're interested in

	# Find the index of the lake based on the lake ID
	lake_ids = nc_file.variables['lake_id'][:]

	# Check if the target lake ID exists in the lake_id variable
	lake_index = np.where(lake_ids == target_lake_id)[0]

	if len(lake_index) == 0:
	st.write("Lake not found in the dataset.")
	else:
	lake_index = lake_index[0] # Use the first match if found

	# Extract coefficients of the area-storage equation for the identified lake
	area_storage_coeffs = nc_file.variables['f_hA'][lake_index, :]
	lon_lat = nc_file.variables['lon_lat'][lake_index, :]

	# Print the lake's ID, coordinates, and area-storage equation coefficients
	st.write("Coordinates (Lon, Lat):", lon_lat)
	# Print the coefficients
	st.write("Area-Storage equation coefficients:")
	st.write("a:", area_storage_coeffs[0])
	st.write("b:", area_storage_coeffs[1])
	st.write("R^2:", area_storage_coeffs[2])
	import numpy as np

	for area in water_area_info:

	# Coefficients obtained from the NetCDF dataset
	a = area_storage_coeffs[0]
	b = area_storage_coeffs[1]

	# Calculate the volume using the area-storage equation
	volume = ((area/1e6) / a) ** (1 / b)
	volumes.append(volume)


	from io import BytesIO
	# Function to generate the sample data DataFrame
	def generate_sample_data():
	date = acquisition_dates
	area = water_area_info
	vol = volumes
	return pd.DataFrame({'Date': date, 'Volume (10⁸m³)': vol, 'Area (km²)': area })

	# Generate the sample data DataFrame
	df = generate_sample_data()

	# Save the DataFrame to an Excel file in memory
	excel_buffer = BytesIO()

	import pandas as pd
	import io

	# Assuming excel_buffer and output, area_storage_coeffs are defined elsewhere in your code
	excel_buffer = io.BytesIO()

	# Use a single `ExcelWriter` for writing all sheets
	with pd.ExcelWriter(excel_buffer, engine='xlsxwriter') as writer:
	# Check if 'Water Surface Elevation' is in output and write relevant data
	if 'Water Surface Elevation' in output:
	# Convert date to timezone-unaware if necessary
	elevation_dates = pd.to_datetime(df_filtered['time_str']).dt.tz_localize(None)
	elevations = df_filtered['wse']
	df_2 = pd.DataFrame({'Date': elevation_dates, 'Water Surface Elevations': elevations})
	df_2.to_excel(writer, sheet_name='Elevations Data', index=False)

	# Check if 'Storage-Capacity curve' is in output and write relevant data
	if 'Storage-Capacity curve' in output:
	# Assume `area_storage_coeffs` contains appropriate data in tuple or list format
	df_3 = pd.DataFrame({
	'a': [area_storage_coeffs[0]],
	'b': [area_storage_coeffs[1]],
	'R^2': [area_storage_coeffs[2]]
	})
	df_3.to_excel(writer, sheet_name='Storage_capacity_curve', index=False)

	# Assuming `df` is a base DataFrame you want to write to a default sheet
	df.to_excel(writer, sheet_name='Reservoir Data', index=False)

	# Reset buffer position to the start for reading/download
	excel_buffer.seek(0)
	# Create a download button for the Excel file
	st.download_button(
	label="Download Excel file",
	data=excel_buffer,
	file_name="reservoir_data.xlsx",
	mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
	)

	st.write("Click the button above to download the data as an Excel file.")

	import tempfile

	# Load the bathymetry dataset from Earth Engine
	globathy = ee.Image("projects/sat-io/open-datasets/GLOBathy/GLOBathy_bathymetry")
	# Define the function to export the image and return the path
	def export_image_for_download(image, roi, scale=10):
	# Use a temporary directory for saving the file
	with tempfile.NamedTemporaryFile(delete=False, suffix=".tif") as temp_file:
	out_image_path = temp_file.name
	geemap.ee_export_image(image, filename=out_image_path, scale=scale, region=roi)
	return out_image_path


	if 'Bathymetry file' in output:
	# Call the function and set up the download button
	if st.button("Download Image"):
	# Assuming `globathy` is your Earth Engine image and `roi` is the region of interest
	image_path = export_image_for_download(globathy, roi)

	# Read the file as bytes for download
	with open(image_path, "rb") as file:
	file_bytes = file.read()
	st.download_button(
	label="Click here to download the image",
	data=file_bytes,
	file_name="exported_image.tif",
	mime="image/tiff"
	)

	# Display the bar charts
	col1, col2= st.columns([7,3])
	# Combine acquisition dates and volumes into a list of tuples
	with col1:

	import pandas as pd
	import altair as alt
	# Function to generate sample data
	def generate_sample_data():
	date = acquisition_dates
	vol= volumes
	return pd.DataFrame({'Date': date, 'Volume': vol})

	# Sample data
	df = generate_sample_data()

	if 'Water Surface Elevation' in output:
	st.subheader("WSE over the chosen date range")

	# Convert 'time_str' to timezone-unaware and set up DataFrame for plotting
	elevation_dates = pd.to_datetime(df_filtered['time_str']).dt.tz_localize(None)
	elevations = df_filtered['wse']
	df_wse = pd.DataFrame({'Date': elevation_dates, 'Water Surface Elevations': elevations})

	# Create the line chart for water surface elevations
	wse_chart = alt.Chart(df_wse).mark_line(
	color='#00FFFF'
	).encode(
	x=alt.X('Date:T', title='Date'),
	y=alt.Y('Water Surface Elevations:Q', title='Water Surface Elevation (m)')
	)

	# Display the WSE chart in Streamlit
	st.altair_chart(wse_chart, use_container_width=True)

	st.subheader("Volume over the chosen date range")

	# Ensure that 'Date' and 'Volume' columns are available in df
	volume_chart = alt.Chart(df).mark_line(
	color='#00FFFF'
	).encode(
	x=alt.X('Date:T', title='Date'),
	y=alt.Y('Volume:Q', title='Volume (10⁶ m³)',scale=alt.Scale(zero=False))
	)

	# Display the volume chart in Streamlit
	st.altair_chart(volume_chart, use_container_width=True)
	with col2:

	import pandas as pd
	# Donut chart function
	def make_donut(input_response, input_text, input_color):
	if input_color == 'green':
	chart_color = ['#27AE60', '#12783D']
	elif input_color == 'red':
	chart_color = ['#E74C3C', '#781F16']
	elif input_color == 'yellow':
	chart_color = ['#FFFF00', '#FFD700'] # Yellow colors
	elif input_color == 'orange':
	chart_color = ['#FFA500', '#FF4500'] # Orange colors
	elif input_color == 'light green':
	chart_color = ['#90EE90', '#006400'] # Light green colors
	else:
	raise ValueError("Invalid color. Please choose either 'green' or 'red'.")

	source = pd.DataFrame({
	"Topic": ['', input_text],
	"% value": [100-input_response, input_response]
	})
	source_bg = pd.DataFrame({
	"Topic": ['', input_text],
	"% value": [100, 0]
	})

	plot = alt.Chart(source).mark_arc(innerRadius=45, cornerRadius=25).encode(
	theta="% value",
	color=alt.Color("Topic:N",
	scale=alt.Scale(
	domain=[input_text, ''],
	range=chart_color),
	legend=None),
	).properties(width=130, height=130)

	text = plot.mark_text(align='center', color=chart_color[0], font="sans-serif", fontSize=20, fontWeight=500, fontStyle="italic").encode(text=alt.value(f'{input_response} %'))

	plot_bg = alt.Chart(source_bg).mark_arc(innerRadius=45, cornerRadius=20).encode(
	theta="% value",
	color=alt.Color("Topic:N",
	scale=alt.Scale(
	domain=[input_text, ''],
	range=chart_color), # 31333F
	legend=None),
	).properties(width=130, height=130)

	return plot_bg + plot + text


	def get_color(value):
	"""Helper function to determine the color based on percentage."""
	if value < 25:
	return 'red'
	elif 25 <= value < 50:
	return 'orange'
	elif value == 50:
	return 'yellow'
	elif 50 < value < 75:
	return 'light green'
	else:
	return 'green'

	# Check if storage is not None, not an empty string, and can be converted to a float
	if Vol_res is not None:
	try:
	storage_float = Vol_res/10
	if storage_float > 0:
	total_volume = storage_float
	worst = (min(volumes) / total_volume) * 100
	best = (max(volumes) / total_volume) * 100

	# Colors for worst and best day
	wrst_color = get_color(worst)
	bst_color = get_color(best)

	# Display donut charts
	st.subheader("Lower storage")
	st.altair_chart(make_donut(round(worst, 2), 'Worst day', wrst_color), use_container_width=True)

	st.subheader("Higher storage")
	st.altair_chart(make_donut(round(best, 2), 'Best day', bst_color), use_container_width=True)
	except ValueError:
	st.write("Invalid storage value; cannot convert to float.")

	# Fallback if storage is invalid or not provided, and ref_area is available
	elif properties and ref_area is not None:
	ref_area_float = (float(ref_area)*1e6)
	worst = (min(water_area_info) / ref_area_float) * 100
	best = (max(water_area_info) / ref_area_float) * 100

	# Colors for worst and best day
	wrst_color = get_color(worst)
	bst_color = get_color(best)

	# Display fallback donut charts
	st.subheader("Lower storage")
	st.altair_chart(make_donut(round(worst, 2), 'Worst day', wrst_color), use_container_width=True)

	st.subheader(" Higher storage")
	st.altair_chart(make_donut(round(best, 2), 'Best day', bst_color), use_container_width=True)

	def calculate_max_percentage_variation(volumes, acquisition_dates):
	max_variation = 0
	max_variation_index = None

	for i in range(1, len(volumes)):
	# Calculate percentage variation
	percentage_variation = abs((volumes[i] - volumes[i - 1]) / volumes[i - 1]) * 100

	# Update max variation and index if current variation is greater
	if percentage_variation > max_variation:
	max_variation = percentage_variation
	max_variation_index = i - 1 # Store index of the first date in the pair

	# Get the dates corresponding to the max variation
	date1 = acquisition_dates[max_variation_index]
	date2 = acquisition_dates[max_variation_index + 1]

	return max_variation, date1, date2

	max_variation, date1, date2 = calculate_max_percentage_variation(volumes, acquisition_dates)

	import statistics

	std_dev = round(statistics.stdev(volumes),2)

	mean_vol = round(statistics.mean(volumes),2)

	mean_area = round(statistics.mean(water_area_info),2)

	max_variation_area, date1, date2 = calculate_max_percentage_variation(water_area_info, acquisition_dates)
	#st.write(" The greates variation occured between:", date1, "and", date2)

	if max_variation >=0:
	delta = 1
	else:
	delta = -1

	max_area = max(water_area_info)
	max_volume = max(volumes)

	current_volume = round(volumes[-1],2)
	current_area = round(water_area_info[-1],2)

	#create column span
	col1, col2, col3 = st.columns(3)

	#Customize metric style to have white text color
	metric_style = "color: black;"

	col1.metric(label="Max variation", value="%" + " " + f"{max_variation:,.2f}", delta=delta)
	col2.metric(label="Mean area", value="km²" + " " + f"{mean_area/1e6:,.2f}", delta=round(max_variation_area,2))
	col3.metric(label="standard deviation", value = "km³ " + " " + f"{std_dev:,.2f}")

	st.write(" The greates variation occured between:", date1, "and", date2)
	# Create a select box for asking ChatGPT about this reservoir
	opt = ["No","Yes"]
	info = st.selectbox(
	f"Ask ChatGPT more information about this lake {lake_name}",
	opt,
	key="info")
	if info == "Yes":
	import streamlit as st
	from openai import OpenAI

	# Define the reservoir information retrieval function
	def get_reservoir_info(lake_name, country, lng, lat):
	client = OpenAI(api_key="sk-proj-uK1IbwMXiNImV7RFDxr3T3BlbkFJXjBzHIIJYRoQbiJE6Kc5")

	prompt = (
	f"Provide detailed information about the dam/reservoir {lake_name}, "
	f"located in {country}, with coordinates (longitude: {lng}, latitude: {lat}). "
	f"Use short sentences and split the response into concise, clear lines."
	f"Specify the type of use of that reservoir or lake and when it wast built. "
	)

	stream = client.chat.completions.create(
	model="gpt-3.5-turbo",
	messages=[{"role": "user", "content": prompt}],
	stream=True,
	)

	response_content = ""

	for chunk in stream:
	if chunk.choices[0].delta.content is not None:
	response_content += chunk.choices[0].delta.content

	return response_content

	st.subheader(f"Information about the reservoir {lake_name}")

	info_text = get_reservoir_info(lake_name, Country, lng, lat)

	# Displaying the information with line breaks
	for line in info_text.split(". "): # Split into sentences
	st.text(line.strip() + ".")
	else:
	st.write("Please select the date range and cloud coverage thershold for the analysis.")
	else:
	st.write("Please search on the map the lake you want to analyse and click on it to select it")