update app.py

app.py

@@ -1,803 +1,3 @@
-# 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 re
-# from shapely.geometry import base
-# from lxml import etree
-# from xml.etree import ElementTree as ET
-
-# # 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,
-# )
-
-# # Title
-# st.markdown(
-#     f"""
-#     <h1 style="text-align: center;">Precision Analysis for Vegetation, Water, and Air Quality</h1>
-#     """,
-#     unsafe_allow_html=True,
-# )
-# st.write("<h2><div style='text-align: center;'>User  Inputs</div></h2>", 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 the Sentinel dataset options from JSON file
-# with open("sentinel_datasets.json") as f:
-#     data = json.load(f)
-
-# # Display the title for the Streamlit app
-# st.title("Sentinel Dataset")
-
-# # Select dataset category (main selection)
-# main_selection = st.selectbox("Select Sentinel Dataset Category", list(data.keys()))
-
-# # If a category is selected, display the sub-options (specific datasets)
-# if main_selection:
-#     sub_options = data[main_selection]["sub_options"]
-#     sub_selection = st.selectbox("Select Specific Dataset ID", list(sub_options.keys()))
-
-#     # Display the selected dataset ID based on user input
-#     if sub_selection:
-#         st.write(f"You selected: {main_selection} -> {sub_selection}")
-#         st.write(f"Dataset ID: {sub_options[sub_selection]}")
-
-#         # Fetch the correct dataset ID from the sub-selection
-#         dataset_id = sub_options[sub_selection]
-
-# # Earth Engine Index Calculator Section
-# st.header("Earth Engine Index Calculator")
-
-# 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 (case-insensitive)
-# if index_choice.lower() == 'ndvi':
-#     st.write("Formula for NDVI: NDVI = (B8 - B4) / (B8 + B4)")
-# elif index_choice.lower() == 'ndwi':
-#     st.write("Formula for NDWI: NDWI = (B3 - B8) / (B3 + B8)")
-# elif index_choice.lower() == 'average no₂':
-#     st.write("Formula for Average NO₂: Average NO₂ = Mean(NO2 band)")
-# elif index_choice.lower() == 'custom formula':
-#     custom_formula = st.text_input("Enter Custom Formula (e.g., B5,B4 for two bands or B3 for one band)")
-#     # Check if custom formula is empty and show warning
-#     if not custom_formula:
-#         st.warning("Please enter a custom formula before proceeding.")
-#     else:
-#         # Check if the input contains a comma (indicating two bands)
-#         if ',' in custom_formula:
-#             # Split the input into two bands and strip whitespace
-#             band1, band2 = [b.strip() for b in custom_formula.split(',', 1)]
-#             st.write(f"Custom Formula: ({band1} - {band2}) / ({band1} + {band2})")
-#         else:
-#             # Single band case
-#             band = custom_formula.strip()
-#             st.write(f"Custom Formula: {band}")
-
-# # Function to get the corresponding reducer based on user input
-# def get_reducer(reducer_name):
-#     """
-#     Map user-friendly reducer names to Earth Engine reducer objects.
-#     """
-#     reducers = {
-#         'mean': ee.Reducer.mean(),
-#         'sum': ee.Reducer.sum(),
-#         'median': ee.Reducer.median(),
-#         'min': ee.Reducer.min(),
-#         'max': ee.Reducer.max(),
-#         'count': ee.Reducer.count(),
-#     }
-    
-#     # Default to 'mean' if the reducer_name is not recognized
-#     return reducers.get(reducer_name.lower(), ee.Reducer.mean())
-
-# # Streamlit selectbox for reducer choice
-# reducer_choice = st.selectbox(
-#     "Select Reducer",
-#     ['mean', 'sum', 'median', 'min', 'max', 'count'],
-#     index=0  # Default to 'mean'
-# )
-
-# def convert_to_ee_geometry(geometry):
-#     # Handle Shapely geometry
-#     if isinstance(geometry, base.BaseGeometry):
-#         if geometry.is_valid:
-#             geojson = geometry.__geo_interface__
-#             print("Shapely GeoJSON:", geojson)  # Debugging: Inspect the GeoJSON structure
-#             return ee.Geometry(geojson)
-#         else:
-#             raise ValueError("Invalid geometry: The polygon geometry is not valid.")
-    
-#     # Handle GeoJSON input (string or dictionary)
-#     elif isinstance(geometry, dict) or isinstance(geometry, str):
-#         try:
-#             if isinstance(geometry, str):
-#                 geometry = json.loads(geometry)
-#             if 'type' in geometry and 'coordinates' in geometry:
-#                 print("GeoJSON Geometry:", geometry)  # Debugging: Inspect the GeoJSON structure
-#                 return ee.Geometry(geometry)
-#             else:
-#                 raise ValueError("GeoJSON format is invalid.")
-#         except Exception as e:
-#             raise ValueError(f"Error parsing GeoJSON: {e}")
-
-#     # Handle KML input (string or file path)
-#     elif isinstance(geometry, str) and geometry.lower().endswith(".kml"):
-#         try:
-#             # Parse the KML file
-#             tree = ET.parse(geometry)
-#             kml_root = tree.getroot()
-
-#             # Extract coordinates from KML geometry (assuming it's a Polygon or MultiPolygon)
-#             # KML coordinates are usually within the <coordinates> tag
-#             kml_namespace = {'kml': 'http://www.opengis.net/kml/2.2'}
-#             coordinates = kml_root.findall(".//kml:coordinates", kml_namespace)
-            
-#             if coordinates:
-#                 # Extract and format coordinates
-#                 coords_text = coordinates[0].text.strip()
-#                 coords = coords_text.split()
-#                 # Convert KML coordinates (comma-separated) into a list of tuples
-#                 coords = [tuple(map(float, coord.split(','))) for coord in coords]
-#                 geojson = {
-#                     "type": "Polygon",  # Make sure the GeoJSON type is Polygon
-#                     "coordinates": [coords]  # Wrap the coordinates in a list (required by GeoJSON format)
-#                 }
-
-#                 # Debugging: Inspect the KML-to-GeoJSON structure
-#                 print("KML GeoJSON:", geojson)
-                
-#                 return ee.Geometry(geojson)
-#             else:
-#                 raise ValueError("KML does not contain valid coordinates.")
-#         except Exception as e:
-#             raise ValueError(f"Error parsing KML: {e}")
-
-#     else:
-#         raise ValueError("Unsupported geometry input type. Supported types are Shapely, GeoJSON, and KML.")
-
-# # 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
-
-
-# # Date Input for Start and End Dates
-# start_date = st.date_input("Start Date", value=pd.to_datetime('2024-11-01'))
-# end_date = st.date_input("End Date", value=pd.to_datetime('2024-12-01'))
-
-# # 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')
-
-# # Aggregation period selection
-# aggregation_period = st.selectbox("Select Aggregation Period", ["Daily", "Weekly", "Monthly", "Yearly"], index=0)
-
-# # Ask user whether they want to process 'Point' or 'Polygon' data (case-insensitive)
-# 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 (case-insensitive)
-# file_upload = st.file_uploader(f"Upload your {shape_type} data (CSV, GeoJSON, KML)", type=["csv", "geojson", "kml"])
-
-# if file_upload is not None:
-#     # Read the user-uploaded file
-#     if shape_type.lower() == "point":
-#         # Handle different file types for Point data
-#         if file_upload.name.endswith('.csv'):
-#             locations_df = pd.read_csv(file_upload)
-#         elif file_upload.name.endswith('.geojson'):
-#             locations_df = gpd.read_file(file_upload)
-#         elif file_upload.name.endswith('.kml'):
-#             locations_df = gpd.read_file(file_upload)
-#         else:
-#             st.error("Unsupported file format. Please upload CSV, GeoJSON, or KML.")
-#             locations_df = pd.DataFrame()
-
-#          # Check if the file contains polygons when the user selected "Point"
-#         if 'geometry' in locations_df.columns:
-#             # Check if the geometry type is Polygon or MultiPolygon
-#             if locations_df.geometry.geom_type.isin(['Polygon', 'MultiPolygon']).any():
-#                 st.warning("The uploaded file contains polygon data. Please select 'Polygon' for processing.")
-#                 st.stop()  # Stop further processing if polygons are detected
-
-#         # Processing the point data
-#         with st.spinner('Processing Map...'):
-#             if locations_df is not None and not locations_df.empty:
-#                 # For GeoJSON data, the coordinates are in the geometry column
-#                 if 'geometry' in locations_df.columns:
-#                     # Extract latitude and longitude from the geometry column
-#                     locations_df['latitude'] = locations_df['geometry'].y
-#                     locations_df['longitude'] = locations_df['geometry'].x
-
-#                 # Ensure the necessary columns exist in the dataframe
-#                 if 'latitude' not in locations_df.columns or 'longitude' not in locations_df.columns:
-#                     st.error("Uploaded file is missing required 'latitude' or 'longitude' columns.")
-#                 else:
-#                     # 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
-
-#     elif shape_type.lower() == "polygon":
-#         # Handle different file types for Polygon data:
-#         if file_upload.name.endswith('.csv'):
-#             locations_df = pd.read_csv(file_upload)
-#         elif file_upload.name.endswith('.geojson'):
-#             locations_df = gpd.read_file(file_upload)
-#         elif file_upload.name.endswith('.kml'):
-#             locations_df = gpd.read_file(file_upload)
-#         else:
-#             st.error("Unsupported file format. Please upload CSV, GeoJSON, or KML.")
-#             locations_df = pd.DataFrame()
-
-#         # Check if the file contains points when the user selected "Polygon"
-#         if 'geometry' in locations_df.columns:
-#             # Check if the geometry type is Point or MultiPoint
-#             if locations_df.geometry.geom_type.isin(['Point', 'MultiPoint']).any():
-#                 st.warning("The uploaded file contains point data. Please select 'Point' for processing.")
-#                 st.stop()  # Stop further processing if point data is detected
-
-#         # Processing the polygon data
-#         with st.spinner('Processing Map...'):
-#             if locations_df is not None and not locations_df.empty:
-#                 # Ensure the 'geometry' column exists in the dataframe
-#                 if 'geometry' not in locations_df.columns:
-#                     st.error("Uploaded file is missing required 'geometry' column.")
-#                 else:
-#                     # Display a preview of the polygons data
-#                     st.write("Preview of the uploaded polygons data:")
-#                     st.dataframe(locations_df.head())
-
-#                     # Create a LeafMap object to display the polygons
-#                     # Calculate the centroid of the polygons for the map center
-#                     centroid_lat = locations_df.geometry.centroid.y.mean()
-#                     centroid_lon = locations_df.geometry.centroid.x.mean()
-
-#                     m = leafmap.Map(center=[centroid_lat, centroid_lon], zoom=10)
-
-#                     # Add polygons to the map using a loop
-#                     for _, row in locations_df.iterrows():
-#                         polygon = row['geometry']
-#                         if polygon.is_valid:  # Check if polygon is valid
-#                             # Create a GeoDataFrame for this polygon
-#                             gdf = gpd.GeoDataFrame([row], geometry=[polygon], crs=locations_df.crs)
-#                             m.add_gdf(gdf=gdf, layer_name=row.get('name', 'Unnamed Polygon'))
-
-#                     # Display map
-#                     st.write("Map of Uploaded Polygons:")
-#                     m.to_streamlit()
-
-#                     # Store the map in session_state
-#                     st.session_state.map_data = m
-
-# # 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('dataset_id') != dataset_id 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 or
-#         st.session_state.last_params.get('shape_type') != shape_type or
-#         st.session_state.last_params.get('file_upload') != file_upload
-#     )
-
-# # If parameters have changed, reset the results
-# if parameters_changed():
-#     st.session_state.results = []  # Clear the previous results
-#     st.session_state.last_params = {
-#         'main_selection': main_selection,
-#         'dataset_id': dataset_id,
-#         'index_choice': index_choice,
-#         'start_date_str': start_date_str,
-#         'end_date_str': end_date_str,
-#         'shape_type': shape_type,
-#         'file_upload': file_upload
-#     }
-
-# # Function to calculate NDVI with the selected reducer
-# def calculate_ndvi(image, geometry, reducer_choice):
-#     ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
-#     return ndvi
-
-# # Function to calculate NDWI
-# def calculate_ndwi(image, geometry, reducer_choice):
-#     ndwi = image.normalizedDifference(['B3', 'B8']).rename('NDWI')
-#     return ndwi
-
-# def calculate_custom_formula(image, geometry, custom_formula, reducer_choice, scale=30):
-#     try:
-#         if "," in custom_formula:
-#             band1, band2 = [b.strip() for b in custom_formula.split(",")]
-#             band_names = image.bandNames().getInfo()
-#             if band1 not in band_names or band2 not in band_names:
-#                 raise ValueError(f"One or both bands ({band1}, {band2}) do not exist in the image.")
-#             result = image.normalizedDifference([band1, band2]).rename('custom_formula')
-#         else:
-#             band = custom_formula.strip()
-#             band_names = image.bandNames().getInfo()
-#             if band not in band_names:
-#                 raise ValueError(f"The band '{band}' does not exist in the image.")
-#             result = image.select(band).rename('custom_formula')
-#         return result
-#     except Exception as e:
-#         return ee.Image(0).rename('custom_formula').set('error', str(e))
-        
-# # Modify aggregation functions to return the correct time period and aggregated results
-# def aggregate_data_daily(collection):
-#     # Extract day from the image date (using the exact date)
-#     collection = collection.map(lambda image: image.set('day', ee.Date(image.get('system:time_start')).format('YYYY-MM-dd')))
-    
-#     # Group images by day (distinct days)
-#     grouped_by_day = collection.aggregate_array('day').distinct()
-
-#     def calculate_daily_mean(day):
-#         # Filter the collection by the specific day
-#         daily_collection = collection.filter(ee.Filter.eq('day', day))
-#         daily_mean = daily_collection.mean()  # Calculate mean for the day
-#         return daily_mean.set('day', day)
-
-#     # Calculate the daily mean for each day
-#     daily_images = ee.List(grouped_by_day.map(calculate_daily_mean))
-    
-#     return ee.ImageCollection(daily_images)
-
-# def aggregate_data_weekly(collection):
-#     # Extract the start date of the week from the image date
-#     collection = collection.map(lambda image: image.set(
-#         'week_start', ee.Date(image.get('system:time_start'))
-#             .advance(-ee.Date(image.get('system:time_start')).getRelative('day', 'week'), 'day')
-#     ))
-#     # Group images by week start date
-#     grouped_by_week = collection.aggregate_array('week_start').distinct()
-    
-#     def calculate_weekly_mean(week_start):
-#         # Filter the collection by the specific week start date
-#         weekly_collection = collection.filter(ee.Filter.eq('week_start', week_start))
-#         weekly_mean = weekly_collection.mean()  # Calculate mean for the week
-#         return weekly_mean.set('week_start', week_start)
-    
-#     # Calculate the weekly mean for each week
-#     weekly_images = ee.List(grouped_by_week.map(calculate_weekly_mean))
-#     return ee.ImageCollection(weekly_images)
-    
-# def aggregate_data_monthly(collection, start_date, end_date):
-#     # Filter the collection for the specific date range
-#     collection = collection.filterDate(start_date, end_date)
-    
-#     # Extract month and year from the image date
-#     collection = collection.map(lambda image: image.set('month', ee.Date(image.get('system:time_start')).format('YYYY-MM')))
-    
-#     # Group images by month
-#     grouped_by_month = collection.aggregate_array('month').distinct()
-
-#     def calculate_monthly_mean(month):
-#         monthly_collection = collection.filter(ee.Filter.eq('month', month))
-#         monthly_mean = monthly_collection.mean()
-#         return monthly_mean.set('month', month)
-
-#     # Calculate the monthly mean for each month
-#     monthly_images = ee.List(grouped_by_month.map(calculate_monthly_mean))
-    
-#     return ee.ImageCollection(monthly_images)
-    
-# def aggregate_data_yearly(collection):
-#     # Extract year from the image date
-#     collection = collection.map(lambda image: image.set('year', ee.Date(image.get('system:time_start')).format('YYYY')))
-    
-#     # Group images by year
-#     grouped_by_year = collection.aggregate_array('year').distinct()
-
-#     def calculate_yearly_mean(year):
-#         yearly_collection = collection.filter(ee.Filter.eq('year', year))
-#         yearly_mean = yearly_collection.mean()
-#         return yearly_mean.set('year', year)
-
-#     # Calculate the yearly mean for each year
-#     yearly_images = ee.List(grouped_by_year.map(calculate_yearly_mean))
-    
-#     return ee.ImageCollection(yearly_images)
-
-# # Function to calculate index based on the selected choice
-# def calculate_index_for_period(image, roi, index_choice, reducer_choice, custom_formula):
-#     if index_choice.lower() == 'ndvi':
-#         return calculate_ndvi(image, roi, reducer_choice)
-#     elif index_choice.lower() == 'ndwi':
-#         return calculate_ndwi(image, roi, reducer_choice)
-#     elif index_choice.lower() == 'average no₂':
-#         mean_no2 = image.select('NO2').mean().rename('Average NO₂')
-#         return mean_no2
-#     elif index_choice.lower() == 'custom formula':
-#         # Pass the custom formula here, not the index_choice
-#         return calculate_custom_formula(image, roi, custom_formula, reducer_choice)
-#     else:
-#         st.write("Please Select any one option...."+ index_choice.lower())
-
-# def aggregate_data_weekly(collection):
-#     def set_week_start(image):
-#         # Get the image timestamp
-#         date = ee.Date(image.get('system:time_start'))
-#         # Calculate days since the start of the week (0 = Monday, 6 = Sunday)
-#         days_since_week_start = date.getRelative('day', 'week')
-#         # Convert to ee.Number and negate it to get the offset to the week start
-#         offset = ee.Number(days_since_week_start).multiply(-1)
-#         # Advance the date by the negative offset to get the week start
-#         week_start = date.advance(offset, 'day')
-#         return image.set('week_start', week_start.format('YYYY-MM-dd'))  # Ensure string format
-    
-#     # Apply the week start calculation to each image
-#     collection = collection.map(set_week_start)
-    
-#     # Group images by week start date
-#     grouped_by_week = collection.aggregate_array('week_start').distinct()
-    
-#     def calculate_weekly_mean(week_start):
-#         # Filter the collection by the specific week start date
-#         weekly_collection = collection.filter(ee.Filter.eq('week_start', week_start))
-#         weekly_mean = weekly_collection.mean()  # Calculate mean for the week
-#         return weekly_mean.set('week_start', week_start)
-    
-#     # Calculate the weekly mean for each week
-#     weekly_images = ee.List(grouped_by_week.map(calculate_weekly_mean))
-#     return ee.ImageCollection(weekly_images)
-
-# def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id, index_choice, reducer_choice, shape_type, aggregation_period, custom_formula=""):
-#     aggregated_results = []
-    
-#     if index_choice.lower() == 'custom_formula' and not custom_formula:
-#         st.error("Custom formula cannot be empty. Please provide a formula.")
-#         return aggregated_results
-    
-#     total_steps = len(locations_df)
-#     progress_bar = st.progress(0)
-#     progress_text = st.empty()
-    
-#     with st.spinner('Processing data...'):
-#         if shape_type.lower() == "point":
-#             for idx, row in locations_df.iterrows():
-#                 latitude = row.get('latitude')
-#                 longitude = row.get('longitude')
-#                 if pd.isna(latitude) or pd.isna(longitude):
-#                     st.warning(f"Skipping location {idx} with missing latitude or longitude")
-#                     continue
-                
-#                 location_name = row.get('name', f"Location_{idx}")
-#                 roi = ee.Geometry.Point([longitude, latitude])
-                
-#                 collection = ee.ImageCollection(dataset_id) \
-#                     .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
-#                     .filterBounds(roi)
-                
-#                 # Aggregate data based on the selected period
-#                 if aggregation_period.lower() == 'daily':
-#                     collection = aggregate_data_daily(collection)
-#                 elif aggregation_period.lower() == 'weekly':
-#                     collection = aggregate_data_weekly(collection)
-#                 elif aggregation_period.lower() == 'monthly':
-#                     collection = aggregate_data_monthly(collection, start_date_str, end_date_str)
-#                 elif aggregation_period.lower() == 'yearly':
-#                     collection = aggregate_data_yearly(collection)
-                
-#                 # Process each image in the collection
-#                 image_list = collection.toList(collection.size())
-#                 processed_weeks = set()  # Track processed weeks to avoid duplicates
-#                 for i in range(image_list.size().getInfo()):
-#                     image = ee.Image(image_list.get(i))
-                    
-#                     if aggregation_period.lower() == 'daily':
-#                         timestamp = image.get('day')
-#                         period_label = 'Date'
-#                         date = ee.Date(timestamp).format('YYYY-MM-dd').getInfo()
-#                     elif aggregation_period.lower() == 'weekly':
-#                         timestamp = image.get('week_start')
-#                         period_label = 'Week'
-#                         date = ee.String(timestamp).getInfo()  # Already formatted as YYYY-MM-dd
-#                         # Skip if week is outside the date range or already processed
-#                         if (pd.to_datetime(date) < pd.to_datetime(start_date_str) or 
-#                             pd.to_datetime(date) > pd.to_datetime(end_date_str) or 
-#                             date in processed_weeks):
-#                             continue
-#                         processed_weeks.add(date)
-#                     elif aggregation_period.lower() == 'monthly':
-#                         timestamp = image.get('month')
-#                         period_label = 'Month'
-#                         date = ee.Date(timestamp).format('YYYY-MM').getInfo()
-#                     elif aggregation_period.lower() == 'yearly':
-#                         timestamp = image.get('year')
-#                         period_label = 'Year'
-#                         date = ee.Date(timestamp).format('YYYY').getInfo()
-                    
-#                     index_image = calculate_index_for_period(image, roi, index_choice, reducer_choice, custom_formula)
-                    
-#                     try:
-#                         index_value = index_image.reduceRegion(
-#                             reducer=get_reducer(reducer_choice),
-#                             geometry=roi,
-#                             scale=30
-#                         ).get(index_image.bandNames().get(0))
-                        
-#                         calculated_value = index_value.getInfo()
-                        
-#                         if isinstance(calculated_value, (int, float)):
-#                             aggregated_results.append({
-#                                 'Location Name': location_name,
-#                                 'Latitude': latitude,
-#                                 'Longitude': longitude,
-#                                 period_label: date,
-#                                 'Start Date': start_date_str,
-#                                 'End Date': end_date_str,
-#                                 'Calculated Value': calculated_value
-#                             })
-#                         else:
-#                             st.warning(f"Skipping invalid value for {location_name} on {date}")
-#                     except Exception as e:
-#                         st.error(f"Error retrieving value for {location_name}: {e}")
-                
-#                 progress_percentage = (idx + 1) / total_steps
-#                 progress_bar.progress(progress_percentage)
-#                 progress_text.markdown(f"Processing: {int(progress_percentage * 100)}%")
-        
-#         elif shape_type.lower() == "polygon":
-#             for idx, row in locations_df.iterrows():
-#                 polygon_name = row.get('name', f"Polygon_{idx}")
-#                 polygon_geometry = row.get('geometry')
-#                 location_name = polygon_name
-                
-#                 try:
-#                     roi = convert_to_ee_geometry(polygon_geometry)
-#                 except ValueError as e:
-#                     st.warning(f"Skipping invalid polygon {polygon_name}: {e}")
-#                     continue
-                
-#                 collection = ee.ImageCollection(dataset_id) \
-#                     .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
-#                     .filterBounds(roi)
-                
-#                 # Aggregate data based on the selected period
-#                 if aggregation_period.lower() == 'daily':
-#                     collection = aggregate_data_daily(collection)
-#                 elif aggregation_period.lower() == 'weekly':
-#                     collection = aggregate_data_weekly(collection)
-#                 elif aggregation_period.lower() == 'monthly':
-#                     collection = aggregate_data_monthly(collection, start_date_str, end_date_str)
-#                 elif aggregation_period.lower() == 'yearly':
-#                     collection = aggregate_data_yearly(collection)
-                
-#                 # Process each image in the collection
-#                 image_list = collection.toList(collection.size())
-#                 processed_weeks = set()  # Track processed weeks to avoid duplicates
-#                 for i in range(image_list.size().getInfo()):
-#                     image = ee.Image(image_list.get(i))
-                    
-#                     if aggregation_period.lower() == 'daily':
-#                         timestamp = image.get('day')
-#                         period_label = 'Date'
-#                         date = ee.Date(timestamp).format('YYYY-MM-dd').getInfo()
-#                     elif aggregation_period.lower() == 'weekly':
-#                         timestamp = image.get('week_start')
-#                         period_label = 'Week'
-#                         date = ee.String(timestamp).getInfo()  # Already formatted as YYYY-MM-dd
-#                         # Skip if week is outside the date range or already processed
-#                         if (pd.to_datetime(date) < pd.to_datetime(start_date_str) or 
-#                             pd.to_datetime(date) > pd.to_datetime(end_date_str) or 
-#                             date in processed_weeks):
-#                             continue
-#                         processed_weeks.add(date)
-#                     elif aggregation_period.lower() == 'monthly':
-#                         timestamp = image.get('month')
-#                         period_label = 'Month'
-#                         date = ee.Date(timestamp).format('YYYY-MM').getInfo()
-#                     elif aggregation_period.lower() == 'yearly':
-#                         timestamp = image.get('year')
-#                         period_label = 'Year'
-#                         date = ee.Date(timestamp).format('YYYY').getInfo()
-                    
-#                     index_image = calculate_index_for_period(image, roi, index_choice, reducer_choice, custom_formula)
-                    
-#                     try:
-#                         index_value = index_image.reduceRegion(
-#                             reducer=get_reducer(reducer_choice),
-#                             geometry=roi,
-#                             scale=30
-#                         ).get(index_image.bandNames().get(0))
-                        
-#                         calculated_value = index_value.getInfo()
-                        
-#                         if isinstance(calculated_value, (int, float)):
-#                             aggregated_results.append({
-#                                 'Location Name': location_name,
-#                                 period_label: date,
-#                                 'Start Date': start_date_str,
-#                                 'End Date': end_date_str,
-#                                 'Calculated Value': calculated_value
-#                             })
-#                         else:
-#                             st.warning(f"Skipping invalid value for {location_name} on {date}")
-#                     except Exception as e:
-#                         st.error(f"Error retrieving value for {location_name}: {e}")
-                
-#                 progress_percentage = (idx + 1) / total_steps
-#                 progress_bar.progress(progress_percentage)
-#                 progress_text.markdown(f"Processing: {int(progress_percentage * 100)}%")
-    
-#     # if aggregated_results:
-#     #     result_df = pd.DataFrame(aggregated_results)
-#     #     if aggregation_period.lower() == 'daily':
-#     #         aggregated_output = result_df.groupby('Location Name').agg({
-#     #             'Latitude': 'first' if shape_type.lower() == 'point' else None,
-#     #             'Longitude': 'first' if shape_type.lower() == 'point' else None,
-#     #             'Start Date': 'first',
-#     #             'End Date': 'first',
-#     #             'Calculated Value': 'mean'
-#     #         }).reset_index()
-#     #         # Remove None columns (for polygons)
-#     #         aggregated_output = aggregated_output[[col for col in aggregated_output.columns if col is not None]]
-#     #         aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
-#     #         return aggregated_output.to_dict(orient='records')
-#     #     else:
-#     #         return result_df.to_dict(orient='records')
-    
-#     # return []
-    
-#     if aggregated_results:
-#         result_df = pd.DataFrame(aggregated_results)
-#         if aggregation_period.lower() == 'daily':
-#             # Define aggregation dictionary based on shape_type
-#             agg_dict = {
-#                 'Start Date': 'first',
-#                 'End Date': 'first',
-#                 'Calculated Value': 'mean'
-#             }
-#             if shape_type.lower() == 'point':
-#                 agg_dict['Latitude'] = 'first'
-#                 agg_dict['Longitude'] = 'first'
-                
-#             aggregated_output = result_df.groupby('Location Name').agg(agg_dict).reset_index()
-#             aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
-#             return aggregated_output.to_dict(orient='records')
-#         else:
-#             return result_df.to_dict(orient='records')
-        
-#     return []
-    
-# # When the user clicks the process button, start the calculation
-# if st.button(f"Calculate ({index_choice})"):
-#     if file_upload is not None:
-#         if shape_type.lower() == "point":
-#             results = process_aggregation(
-#                 locations_df,
-#                 start_date_str,
-#                 end_date_str,
-#                 dataset_id,
-#                 index_choice,
-#                 reducer_choice,
-#                 shape_type,
-#                 aggregation_period,
-#                 custom_formula
-#             )
-#             if results:
-#                 result_df = pd.DataFrame(results)
-#                 st.write(f"Processed Results Table ({aggregation_period}):")
-#                 st.dataframe(result_df)
-#                 filename = f"{main_selection}_{dataset_id}_{start_date.strftime('%Y/%m/%d')}_{end_date.strftime('%Y/%m/%d')}_{aggregation_period.lower()}.csv"
-#                 st.download_button(
-#                     label="Download results as CSV",
-#                     data=result_df.to_csv(index=False).encode('utf-8'),
-#                     file_name=filename,
-#                     mime='text/csv'
-#                 )
-#                 st.spinner('')
-#                 st.success('Processing complete!')
-#             else:
-#                 st.warning("No results were generated.")
-        
-#         elif shape_type.lower() == "polygon":
-#             results = process_aggregation(
-#                 locations_df,
-#                 start_date_str,
-#                 end_date_str,
-#                 dataset_id,
-#                 index_choice,
-#                 reducer_choice,
-#                 shape_type,
-#                 aggregation_period,
-#                 custom_formula
-#             )
-#             if results:
-#                 result_df = pd.DataFrame(results)
-#                 st.write(f"Processed Results Table ({aggregation_period}):")
-#                 st.dataframe(result_df)
-#                 filename = f"{main_selection}_{dataset_id}_{start_date.strftime('%Y/%m/%d')}_{end_date.strftime('%Y/%m/%d')}_{aggregation_period.lower()}.csv"
-#                 st.download_button(
-#                     label="Download results as CSV",
-#                     data=result_df.to_csv(index=False).encode('utf-8'),
-#                     file_name=filename,
-#                     mime='text/csv'
-#                 )
-#                 st.spinner('')
-#                 st.success('Processing complete!')
-#             else:
-#                 st.warning("No results were generated.")
-        
-#         else:
-#             st.warning("Please upload a file.")
-
-
-
 import streamlit as st
 import json
 import ee
@@ -993,9 +193,6 @@ aggregation_period = st.selectbox("Select Aggregation Period", ["Daily", "Weekly
 # 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"])
-
 # Additional options based on shape type
 kernel_size = None
 include_boundary = None
@@ -1012,6 +209,10 @@ elif shape_type.lower() == "polygon":
         value=True,
         help="Check to include pixels on the polygon boundary; uncheck to exclude them."
     )
+    
+# 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"])
+
 
 if file_upload is not None:
     # Read the user-uploaded file
@@ -1430,7 +631,7 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
     return []
 
 # Button to trigger calculation
-if st.button("Calculate"):
+if st.button(f"Calculate({custom_formula})"):
     if file_upload is not None:
         if shape_type.lower() in ["point", "polygon"]:
             results = process_aggregation(