import streamlit as st import folium import geopandas as gpd import json import geemap.foliumap as geemap import ee import pandas as pd import os from google.auth.transport.requests import Request import google.auth.exceptions from streamlit.components.v1 import html # Path to your shapefiles and service account key SHAPEFILE_DIR = 'C:\\Users\\piv\\Desktop\\y\\media\\shp' # Fixed path separator # Function to initialize GEE def initialize_gee(): service = os.getenv('SA') file = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'gee', 'ee-muzzamil1-37ebc3dece52.json') credentials = ee.ServiceAccountCredentials(service, file) try: ee.Initialize(credentials) st.success("Google Earth Engine initialized successfully.") except google.auth.exceptions.RefreshError: try: request = Request() credentials.refresh(request) ee.Initialize(credentials) st.success("Google Earth Engine token refreshed and initialized successfully.") except Exception as e: st.error(f"Error refreshing Google Earth Engine token: {e}") except Exception as e: st.error(f"Error initializing Google Earth Engine: {e}") # Function to create Folium map with selected dataset def create_folium_map(selected_dataset, ee_object, start_date_str, end_date_str): folium_map = folium.Map(location=[25.5973518, 65.54495724], zoom_start=7) try: if selected_dataset == "Modis": dataset = ee.ImageCollection('MODIS/006/MOD13Q1') \ .filter(ee.Filter.date(start_date_str, end_date_str)) \ .filterBounds(ee_object) def clip_image(img): return img.clip(ee_object).select('NDVI') clipped_collection = dataset.map(clip_image) modis_ndvi = clipped_collection.mean() vis_params_ndvi = { 'min': 0, 'max': 9000, 'palette': ['FE8374', 'C0E5DE', '3A837C', '034B48'] } modis_ndvi_map_id = modis_ndvi.getMapId(vis_params_ndvi) folium.TileLayer( tiles=modis_ndvi_map_id['tile_fetcher'].url_format, attr='Google Earth Engine', name='NDVI', overlay=True, control=True ).add_to(folium_map) elif selected_dataset == "dataset_nighttime": dataset_nighttime = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG') \ .filter(ee.Filter.date(start_date_str, end_date_str)) nighttime = dataset_nighttime.select('avg_rad').mosaic() nighttime_clipped = nighttime.clip(ee_object) nighttime_vis = { 'min': 0.0, 'max': 60.0, 'palette': ['1a3678', '2955bc', '5699ff', '8dbae9', 'acd1ff', 'caebff', 'e5f9ff', 'fdffb4', 'ffe6a2', 'ffc969', 'ffa12d', 'ff7c1f', 'ca531a', 'ff0000', 'ab0000'] } nighttime_map_id = nighttime_clipped.getMapId(nighttime_vis) folium.TileLayer( tiles=nighttime_map_id['tile_fetcher'].url_format, attr='Google Earth Engine', name='Nighttime Lights', overlay=True, control=True ).add_to(folium_map) elif selected_dataset == "precipitation": dataset_precipitation = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') \ .filter(ee.Filter.date(start_date_str, end_date_str)) precipitation = dataset_precipitation.mosaic().clip(ee_object) precip_vis = { 'min': 0, 'max': 300, 'palette': ['blue', 'cyan', 'lime', 'yellow', 'red'] } precip_map_id = precipitation.getMapId(precip_vis) folium.TileLayer( tiles=precip_map_id['tile_fetcher'].url_format, attr='Google Earth Engine', name='Precipitation', overlay=True, control=True ).add_to(folium_map) elif selected_dataset == "GlobalSurfaceWater": st.warning("GlobalSurfaceWater dataset is not configured.") elif selected_dataset == "WorldPop": dataset = ee.ImageCollection('WorldPop/GP/100m/pop') \ .filterBounds(ee_object) \ .filter(ee.Filter.date(start_date_str, end_date_str)) population = dataset.mean().clip(ee_object) pop_vis = { 'min': 0, 'max': 1000, 'palette': ['blue', 'green', 'yellow', 'red'] } pop_map_id = population.getMapId(pop_vis) folium.TileLayer( tiles=pop_map_id['tile_fetcher'].url_format, attr='Google Earth Engine', name='World Population', overlay=True, control=True ).add_to(folium_map) elif selected_dataset == "COPERNICUS": dataset = ee.ImageCollection('COPERNICUS/S2') \ .filterBounds(ee_object) \ .filter(ee.Filter.date(start_date_str, end_date_str)) sentinel = dataset.median().clip(ee_object) vis_params = { 'bands': ['B4', 'B3', 'B2'], # RGB bands for visualization 'min': 0, 'max': 3000, 'gamma': 1.4 } copernicus_map_id = sentinel.getMapId(vis_params) folium.TileLayer( tiles=copernicus_map_id['tile_fetcher'].url_format, attr='Google Earth Engine', name='Copernicus Sentinel-2', overlay=True, control=True ).add_to(folium_map) folium_map.add_child(folium.LayerControl()) except Exception as e: st.error(f"Error processing dataset: {e}") st.write(f"Exception: {e}") # Convert Folium map to HTML folium_map_html = folium_map._repr_html_() st.write(folium_map_html) # Debug line return folium_map_html # Function to handle submit button logic def handle_submit(selected_dataset, selected_date_range_From, selected_date_range_To, selected_shape): # Construct shapefile path based on selected shape shapefile_name = f'{selected_shape}.shp' shapefile_path = os.path.join(SHAPEFILE_DIR, shapefile_name) if os.path.exists(shapefile_path): try: # Read shapefile roi_gdf = gpd.read_file(shapefile_path) roi_geojson = roi_gdf.to_crs("EPSG:4326").to_json() except Exception as e: st.error(f"Error reading shapefile: {e}") return # Convert GeoJSON to Earth Engine object try: ee_object = geemap.geojson_to_ee(json.loads(roi_geojson)) except Exception as e: st.error(f"Error converting GeoJSON to Earth Engine object: {e}") return # Create and display Folium map for satellite data st.markdown("### Satellite Data Map") folium_map_html = create_folium_map( selected_dataset, ee_object, selected_date_range_From.strftime('%Y-%m-%d'), selected_date_range_To.strftime('%Y-%m-%d') ) # Use Streamlit to render the HTML html(folium_map_html, height=600) else: st.error("Selected shapefile does not exist.") # Streamlit application def main(): st.set_page_config(layout="wide") st.title("Satellite Data Visualization, Interactive Map") # Initialize GEE when the app starts initialize_gee() # Sidebar with general information st.sidebar.title("Controls") submit_control() # Dataset selection def submit_control(): selected_dataset = st.sidebar.selectbox( "Select Dataset", ["Modis", "dataset_nighttime", 'precipitation', 'GlobalSurfaceWater', 'WorldPop', 'COPERNICUS'] ) # Date range selection selected_date_range_From = st.sidebar.date_input("From", value=pd.to_datetime("2015-07-01")) selected_date_range_To = st.sidebar.date_input("To", value=pd.to_datetime("2023-09-30")) # Shape options shape_options = [ 'District_Boundary', 'hydro_basins', 'karachi', 'National_Constituency_with_Projected_2010_Population', 'Provincial_Boundary', 'Provincial_Constituency', 'Tehsil_Boundary', 'Union_Council' ] # Shape selection selected_shape = st.sidebar.selectbox("Select Shape", shape_options) # Add a submit button if st.sidebar.button("Submit"): # Store the current inputs in session state st.session_state.selected_dataset = selected_dataset st.session_state.selected_date_range_From = selected_date_range_From st.session_state.selected_date_range_To = selected_date_range_To st.session_state.selected_shape = selected_shape # Check if the form has been submitted if 'selected_dataset' in st.session_state: handle_submit( st.session_state.selected_dataset, st.session_state.selected_date_range_From, st.session_state.selected_date_range_To, st.session_state.selected_shape ) if __name__ == "__main__": main()