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| import streamlit as st | |
| import json | |
| import ee | |
| import os | |
| import pandas as pd | |
| import geopandas as gpd | |
| from datetime import datetime | |
| import leafmap.foliumap as leafmap | |
| import time | |
| import re | |
| # Set up the page layout | |
| st.set_page_config(layout="wide") | |
| # Custom button styling | |
| m = st.markdown( | |
| """ | |
| <style> | |
| div.stButton > button:first-child { | |
| background-color: #006400; | |
| color:#ffffff; | |
| } | |
| </style>""", | |
| unsafe_allow_html=True, | |
| ) | |
| # Logo | |
| st.write( | |
| f""" | |
| <div style="display: flex; justify-content: space-between; align-items: center;"> | |
| <img src="https://huggingface.co/spaces/YashMK89/GEE_Calculator/resolve/main/ISRO_Logo.png" style="width: 20%; margin-right: auto;"> | |
| <img src="https://huggingface.co/spaces/YashMK89/GEE_Calculator/resolve/main/SAC_Logo.png" style="width: 20%; margin-left: auto;"> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| # Authenticate and initialize Earth Engine | |
| earthengine_credentials = os.environ.get("EE_Authentication") | |
| # Initialize Earth Engine with secret credentials | |
| os.makedirs(os.path.expanduser("~/.config/earthengine/"), exist_ok=True) | |
| with open(os.path.expanduser("~/.config/earthengine/credentials"), "w") as f: | |
| f.write(earthengine_credentials) | |
| ee.Initialize(project='ee-yashsacisro24') | |
| # Load Sentinel dataset options from JSON file | |
| with open("sentinel_datasets.json") as f: | |
| data = json.load(f) | |
| # Display the title and dataset selection | |
| st.title("Sentinel Dataset") | |
| # Select dataset category and subcategory | |
| main_selection = st.selectbox("Select Sentinel Dataset Category", list(data.keys())) | |
| if main_selection: | |
| sub_options = data[main_selection]["sub_options"] | |
| sub_selection = st.selectbox("Select Specific Dataset ID", list(sub_options.keys())) | |
| # Earth Engine Index Calculator Section | |
| st.header("Earth Engine Index Calculator") | |
| # Choose Index or Custom Formula | |
| index_choice = st.selectbox("Select an Index or Enter Custom Formula", ['NDVI', 'NDWI', 'Average NO₂', 'Custom Formula']) | |
| # Initialize custom_formula variable | |
| custom_formula = "" | |
| # Display corresponding formula based on the index selected | |
| if index_choice == 'NDVI': | |
| st.write("Formula for NDVI: NDVI = (B8 - B4) / (B8 + B4)") | |
| elif index_choice == 'NDWI': | |
| st.write("Formula for NDWI: NDWI = (B3 - B8) / (B3 + B8)") | |
| elif index_choice == 'Average NO₂': | |
| st.write("Formula for Average NO₂: Average NO₂ = Mean(NO2 band)") | |
| elif index_choice == 'Custom Formula': | |
| custom_formula = st.text_input("Enter Custom Formula (e.g., 'B5 - B4 / B5 + B4')") | |
| st.write(f"Custom Formula: {custom_formula}") # Display the custom formula after the user inputs it | |
| # Function to read points from CSV | |
| def read_csv(file_path): | |
| df = pd.read_csv(file_path) | |
| return df | |
| # Function to read points from GeoJSON | |
| def read_geojson(file_path): | |
| gdf = gpd.read_file(file_path) | |
| return gdf | |
| # Function to read points from KML | |
| def read_kml(file_path): | |
| gdf = gpd.read_file(file_path, driver='KML') | |
| return gdf | |
| # Ask user whether they want to process 'Point' or 'Polygon' data | |
| shape_type = st.selectbox("Do you want to process 'Point' or 'Polygon' data?", ["Point", "Polygon"]) | |
| # Ask user to upload a file based on shape type | |
| file_upload = st.file_uploader(f"Upload your {shape_type} data (CSV, GeoJSON, KML)", type=["csv", "geojson", "kml"]) | |
| # Date Input for Start and End Dates | |
| start_date = st.date_input("Start Date", value=pd.to_datetime('2020-01-01')) | |
| end_date = st.date_input("End Date", value=pd.to_datetime('2020-12-31')) | |
| # Convert start_date and end_date to string format for Earth Engine | |
| start_date_str = start_date.strftime('%Y-%m-%d') | |
| end_date_str = end_date.strftime('%Y-%m-%d') | |
| # Initialize session state for storing results if not already done | |
| if 'results' not in st.session_state: | |
| st.session_state.results = [] | |
| if 'last_params' not in st.session_state: | |
| st.session_state.last_params = {} | |
| if 'map_data' not in st.session_state: | |
| st.session_state.map_data = None # Initialize map_data | |
| # Function to check if parameters have changed | |
| def parameters_changed(): | |
| return ( | |
| st.session_state.last_params.get('main_selection') != main_selection or | |
| st.session_state.last_params.get('sub_selection') != sub_selection or | |
| st.session_state.last_params.get('index_choice') != index_choice or | |
| st.session_state.last_params.get('start_date_str') != start_date_str or | |
| st.session_state.last_params.get('end_date_str') != end_date_str | |
| ) | |
| # If parameters have changed, reset the results | |
| if parameters_changed(): | |
| st.session_state.results = [] # Clear the previous results | |
| # Update the last parameters to the current ones | |
| st.session_state.last_params = { | |
| 'main_selection': main_selection, | |
| 'sub_selection': sub_selection, | |
| 'index_choice': index_choice, | |
| 'start_date_str': start_date_str, | |
| 'end_date_str': end_date_str | |
| } | |
| # Function to perform index calculations | |
| def calculate_ndvi(image, geometry): | |
| ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI') | |
| result = ndvi.reduceRegion( | |
| reducer=ee.Reducer.mean(), | |
| geometry=geometry, | |
| scale=30 | |
| ) | |
| return result.get('NDVI') | |
| def calculate_ndwi(image, geometry): | |
| ndwi = image.normalizedDifference(['B3', 'B8']).rename('NDWI') | |
| result = ndwi.reduceRegion( | |
| reducer=ee.Reducer.mean(), | |
| geometry=geometry, | |
| scale=30 | |
| ) | |
| return result.get('NDWI') | |
| def calculate_avg_no2_sentinel5p(image, geometry): | |
| no2 = image.select('NO2').reduceRegion( | |
| reducer=ee.Reducer.mean(), | |
| geometry=geometry, | |
| scale=1000 | |
| ).get('NO2') | |
| return no2 | |
| def calculate_custom_formula(image, geometry, formula): | |
| result = image.expression(formula).rename('Custom Index').reduceRegion( | |
| reducer=ee.Reducer.mean(), | |
| geometry=geometry, | |
| scale=30 | |
| ) | |
| return result.get('Custom Index') | |
| # Function to sanitize the description for export | |
| def sanitize_description(description): | |
| # Replace spaces and other invalid characters with underscores | |
| sanitized = re.sub(r'[^a-zA-Z0-9._-]', '_', description) | |
| # Ensure the description is no longer than 100 characters | |
| sanitized = sanitized[:100] | |
| return sanitized | |
| # Process each point | |
| if file_upload: | |
| locations_df = None # Initialize locations_df to None | |
| polygons_df = None # Initialize polygons_df to None | |
| file_extension = os.path.splitext(file_upload.name)[1].lower() | |
| # Read file based on shape type | |
| if shape_type == 'Point': | |
| if file_extension == '.csv': | |
| locations_df = read_csv(file_upload) | |
| elif file_extension == '.geojson': | |
| locations_df = read_geojson(file_upload) | |
| elif file_extension == '.kml': | |
| locations_df = read_kml(file_upload) | |
| else: | |
| st.error("Unsupported file type. Please upload a CSV, GeoJSON, or KML file for points.") | |
| elif shape_type == 'Polygon': | |
| if file_extension == '.geojson': | |
| polygons_df = read_geojson(file_upload) | |
| elif file_extension == '.kml': | |
| polygons_df = read_kml(file_upload) | |
| else: | |
| st.error("Unsupported file type. Please upload a GeoJSON or KML file for polygons.") | |
| if locations_df is not None: | |
| # Display a preview of the points data | |
| st.write("Preview of the uploaded points data:") | |
| st.dataframe(locations_df.head()) | |
| # Create a LeafMap object to display the points | |
| m = leafmap.Map(center=[locations_df['latitude'].mean(), locations_df['longitude'].mean()], zoom=10) | |
| # Add points to the map using a loop | |
| for _, row in locations_df.iterrows(): | |
| latitude = row['latitude'] | |
| longitude = row['longitude'] | |
| # Check if latitude or longitude are NaN and skip if they are | |
| if pd.isna(latitude) or pd.isna(longitude): | |
| continue # Skip this row and move to the next one | |
| m.add_marker(location=[latitude, longitude], popup=row.get('name', 'No Name')) | |
| # Display map | |
| st.write("Map of Uploaded Points:") | |
| m.to_streamlit() | |
| # Store the map in session_state | |
| st.session_state.map_data = m | |
| # Process each point for index calculation | |
| for idx, row in locations_df.iterrows(): | |
| latitude = row['latitude'] | |
| longitude = row['longitude'] | |
| location_name = row.get('name', f"Location_{idx}") | |
| # Skip processing if latitude or longitude is NaN | |
| if pd.isna(latitude) or pd.isna(longitude): | |
| continue # Skip this row and move to the next one | |
| # Define the region of interest (ROI) | |
| roi = ee.Geometry.Point([longitude, latitude]) | |
| # Load Sentinel-2 image collection | |
| collection = ee.ImageCollection(sub_options[sub_selection]) \ | |
| .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \ | |
| .filterBounds(roi) | |
| # Check if the collection has images for the selected date range | |
| image_count = collection.size().getInfo() | |
| if image_count == 0: | |
| st.warning(f"No images found for {location_name}.") | |
| else: | |
| st.write(f"Found {image_count} images for {location_name}.") | |
| image = collection.first() | |
| # Perform the calculation based on user selection | |
| result = None | |
| if index_choice == 'NDVI': | |
| result = calculate_ndvi(image, roi) | |
| elif index_choice == 'NDWI': | |
| result = calculate_ndwi(image, roi) | |
| elif index_choice == 'Average NO₂': | |
| if 'NO2' in image.bandNames().getInfo(): | |
| result = calculate_avg_no2_sentinel5p(image, roi) | |
| else: | |
| st.warning(f"No NO2 band found for {location_name}. Please use Sentinel-5P for NO₂ data.") | |
| elif index_choice == 'Custom Formula' and custom_formula: | |
| result = calculate_custom_formula(image, roi, custom_formula) | |
| if result is not None: | |
| # Only store the numeric value (not the dictionary structure) | |
| calculated_value = result.getInfo() # Get the numeric value | |
| # Store the result in session state | |
| st.session_state.results.append({ | |
| 'Location Name': location_name, | |
| 'Latitude': latitude, | |
| 'Longitude': longitude, | |
| 'Calculated Value': calculated_value | |
| }) | |
| # Sanitize the location name for use in the export task | |
| sanitized_location_name = sanitize_description(location_name) | |
| # Now allow user to download the image | |
| st.write(f"Click below to download the image for {sanitized_location_name}:") | |
| # Export image as GeoTIFF | |
| export_image = image.clip(roi) # Clip the image to the region of interest (ROI) | |
| export_task = ee.batch.Export.image.toDrive( | |
| image=export_image, | |
| description=sanitized_location_name, | |
| folder='Sentinel_Images', | |
| fileNamePrefix=f'{sanitized_location_name}_image', | |
| region=roi, | |
| scale=30, | |
| fileFormat='GeoTIFF', | |
| crs='EPSG:4326' | |
| ) | |
| export_task.start() | |
| # Show export status | |
| st.info(f"Exporting image for {sanitized_location_name}. This might take some time...") | |
| # Poll for the task status (to check if export is finished) | |
| while export_task.active(): | |
| time.sleep(5) | |
| st.write("Export in progress...") | |
| st.write(f"Export complete! The image is ready for download.") | |
| # Generate a link to the exported image in Google Drive | |
| # Replace 'your_google_drive_folder_link' with the actual folder ID in your Drive | |
| st.write(f"Click below to download the GeoTIFF image for {sanitized_location_name}:") | |
| st.download_button( | |
| label=f"Download {sanitized_location_name} Image", | |
| data=export_image.getDownloadURL(), | |
| file_name=f'{sanitized_location_name}_image.tif' | |
| ) | |
| # After processing, show the results | |
| if st.session_state.results: | |
| # Convert the results to a DataFrame for better visualization | |
| result_df = pd.DataFrame(st.session_state.results) | |
| # Show the results in a table format | |
| st.write("Processed Results Table:") | |
| st.dataframe(result_df[['Location Name', 'Latitude', 'Longitude', 'Calculated Value']]) | |
| # Allow downloading of the results as CSV | |
| st.download_button( | |
| label="Download results as CSV", | |
| data=result_df.to_csv(index=False).encode('utf-8'), | |
| file_name="calculated_results.csv", | |
| mime='text/csv' | |
| ) | |