Py.Cafe

import streamlit as st
import numpy as np
import pandas as pd
import ee
import geemap.foliumap as geemap
from google.oauth2 import id_token
from google.auth.transport import requests
from google.auth import compute_engine
import json

st.set_page_config(layout="wide")

Map = None
vegetation_image = None
evapotranspiration_image = None
composite_image = None


def app():
    global Map
    st.title("Streamlit App - Composite - evapotranspiration and vegetation")


    # Replace contents of service account json file corresponding to your account from here:
    # https://console.cloud.google.com/iam-admin/serviceaccounts

    json_data = '''
    {
        "type": "service_account",
        "project_id": "vegetation-2023-408901",
        "private_key_id": "15e3efcc0c9cf688ecda768f9babceb9d23288e1",
        "private_key": "-----BEGIN PRIVATE KEY-----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\nxUgdzlJxtI0tpBSMGkUKSQ==\n-----END PRIVATE KEY-----\n",
        "client_email": "vegetation-public-service@vegetation-2023-408901.iam.gserviceaccount.com",
        "client_id": "114235178928524820853",
        "auth_uri": "https://accounts.google.com/o/oauth2/auth",
        "token_uri": "https://oauth2.googleapis.com/token",
        "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
        "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/vegetation-public-service%40vegetation-2023-408901.iam.gserviceaccount.com",
        "universe_domain": "googleapis.com
    }
    '''
    # Preparing values
    json_object = json.loads(json_data, strict=False)
    service_account = json_object['client_email']
    json_object = json.dumps(json_object)
    # Authorising the app
    credentials = ee.ServiceAccountCredentials(service_account, key_data=json_object)
    print("credentials = ", credentials)
    ee.Initialize(credentials)

    Map = geemap.Map()
    add_evapotranspiration_layer()
    add_vegetation_layer()

    # Create composite image with above layers
    concat_images()
    # combine_images_method_2()

def add_vegetation_layer():
    print("in add_vegetation_layer")
    global Map
    global vegetation_image

    # Load the image collection
    dataset = ee.ImageCollection('MODIS/061/MOD13A2') \
        .filterDate('2015-01-01', '2015-01-31') \
        .mean()

    # Select the NDVI band
    ndvi = dataset.select('NDVI')

    # Visualization parameters
    ndviVis = {
        'min': 0,
        'max': 9000,
        'palette': [
            'ffffff', 'ce7e45', 'df923d', 'f1b555', 'fcd163', '99b718', '74a901',
            '66a000', '529400', '3e8601', '207401', '056201', '004c00', '023b01',
            '012e01', '011d01', '011301'
        ]
    }

    try:
        print("Adding NDVI vegetation layer/loading to streamlit")
        # Add the NDVI layer to the map
        vegetation_image = ndvi
        print("vegetation_image:", vegetation_image.getInfo())

        # # Map.addLayer(ndvi, ndviVis, 'NDVI vegetation layer')
        ## Map.addLayer(vegetation_image, ndviVis, 'NDVI vegetation layer')
        # Map.addLayer(eval(ee_asset))
        # st.write(Map)
        # Map.to_streamlit()
    except Exception as e:
        st.error(f"Error adding NDVI vegetation layer/loading to streamlit: {e}")


def add_evapotranspiration_layer():
    print("in add_evapotranspiration_layer")
    global Map
    global evapotranspiration_image

    # Load the image collection
    dataset = ee.ImageCollection("CSIC/SPEI/2_9") \
        .filterDate('2015-01-01', '2015-01-31') \
        .mean()

    # Select the 24-month analysis
    spei24 = dataset.select('SPEI_24_month')

    # Set the visualization parameters
    visParams = {
        'min': -2.33,
        'max': 2.33,
        'palette': [
            '8b1a1a', 'de2929', 'f3641d',
            'fdc404', '9afa94', '03f2fd',
            '12adf3', '1771de', '00008b',
        ]
    }

    try:
        # Display the SPEI 24-month layer
        print("Adding SPEI 24-month evapotranspiration layer/loading to streamlit")
        # evapotranspiration_image = spei24.filterBounds(regionOfInterest).first()
        evapotranspiration_image = spei24
        print("evapotranspiration_image:", evapotranspiration_image.getInfo())

        # Map.addLayer(spei24, visParams, 'SPEI 24 month evapotranspiration layer')
        ##Map.addLayer(evapotranspiration_image, visParams, 'SPEI 24 month evapotranspiration layer')
        # Map.to_streamlit()
    except Exception as e:
        st.error(f"Error adding SPEI 24-month evapotranspiration layer/loading to streamlit: {e}")

def concat_images():
    print("in concat_images")
    global Map
    global vegetation_image
    global evapotranspiration_image

    try:
        # Create a concat image with vegetation_image and evapotranspiration_image
        composite_image = ee.Image.cat([evapotranspiration_image, vegetation_image])
        print("composite_image =", composite_image)
        Map.addLayer(composite_image, {'bands': ['SPEI_24_month', 'NDVI'], 'min': -1, 'max': 1}, 'Composite Image')
        #Map.addLayer(composite_image, name='Concat Image')

        evapotranspiration_viz_params = {
            'bands': ['SPEI_24_month', 'NDVI'],
            'min': -1,
            'max': 1
            # 'palette': [
            #     '8b1a1a', 'de2929', 'f3641d',
            #     'fdc404', '9afa94', '03f2fd',
            #     '12adf3', '1771de', '00008b',
            # ]
        }

        #Map.addLayer(composite_image, name='Concat Image', viz_params=evapotranspiration_viz_params)        

        Map.to_streamlit()
    except Exception as e:
        st.error(f"Error creating concat image: {e}")

def combine_images():
    print("in combine_images")
    global Map
    global vegetation_image
    global evapotranspiration_image
    # try:
    # Method 1
    # Create a combined image with vegetation_image and evapotranspiration_image
    composite_image = ee.Image.combine_([evapotranspiration_image, vegetation_image])
    print("Combined image:", composite_image.getInfo())
    # Map.addLayer(composite_image, {'bands': ['SPEI_24_month', 'NDVI']}, name='Composite Image')
    Map.addLayer(composite_image, {'bands': ['SPEI_24_month', 'NDVI'], 'min': -1, 'max': 1}, 'Composite Image')

    # Map.addLayer(composite_image, name='Combined Image')

    Map.to_streamlit()
    # except Exception as e:
    #     st.error(f"Error creating combined image: {e}")

def combine_images_method_2():
    print("in combine_images_method_2")
    global Map
    global vegetation_image
    global evapotranspiration_image
    # try:
    # Method 2
    # try:
    # Create a combined image with vegetation_image and evapotranspiration_image
    print("evapotranspiration_image image:", evapotranspiration_image)
    print("vegetation_image image:", vegetation_image.getInfo())
    # composite_image = evapotranspiration_image.addBands(vegetation_image)
    composite_image = vegetation_image.addBands(evapotranspiration_image)
    # print("Combined image:", composite_image.getInfo())

    viz_params = {
        'bands': ['SPEI_24_month', 'NDVI'],
        'min': -1,
        'max': 1,
        'gamma': [1, 1, 1]  # Adjust gamma if needed
    }

    evapotranspiration_viz_params = {
        'min': -2.33,
        'max': 2.33,
        'palette': [
            '8b1a1a', 'de2929', 'f3641d',
            'fdc404', '9afa94', '03f2fd',
            '12adf3', '1771de', '00008b',
        ]
    }

    Map.addLayer(composite_image, name='Combined Image', viz_params=evapotranspiration_viz_params)
    Map.to_streamlit()
    # except Exception as e:
    #     st.error(f"Error creating combined image: {e}")

app()