Update app.py

app.py

@@ -1,783 +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')
-
-# # Imagery base selection
-# imagery_base = st.selectbox("Select Imagery Base", ["Sentinel", "Landsat", "MODIS", "Custom Input"], index=0)
-
-# # Load the appropriate dataset based on imagery base
-# if imagery_base == "Sentinel":
-#     dataset_file = "sentinel_datasets.json"
-#     with open(dataset_file) as f:
-#         data = json.load(f)
-# elif imagery_base == "Landsat":
-#     dataset_file = "landsat_datasets.json"
-#     with open(dataset_file) as f:
-#         data = json.load(f)
-# elif imagery_base == "MODIS":
-#     dataset_file = "modis_datasets.json"
-#     with open(dataset_file) as f:
-#         data = json.load(f)
-# elif imagery_base == "Custom Input":
-#     custom_dataset_id = st.text_input("Enter Custom Earth Engine Dataset ID (e.g., ee.ImageCollection('AHN/AHN4'))", value="")
-#     if custom_dataset_id:
-#         try:
-#             # Remove potential "ee.ImageCollection()" wrapper for simplicity
-#             if custom_dataset_id.startswith("ee.ImageCollection("):
-#                 custom_dataset_id = custom_dataset_id.replace("ee.ImageCollection('", "").replace("')", "")
-#             # Fetch dataset info from GEE
-#             collection = ee.ImageCollection(custom_dataset_id)
-#             band_names = collection.first().bandNames().getInfo()
-#             data = {
-#                 f"Custom Dataset: {custom_dataset_id}": {
-#                     "sub_options": {custom_dataset_id: f"Custom Dataset ({custom_dataset_id})"},
-#                     "bands": {custom_dataset_id: band_names}
-#                 }
-#             }
-#             st.write(f"Fetched bands for {custom_dataset_id}: {', '.join(band_names)}")
-#         except Exception as e:
-#             st.error(f"Error fetching dataset: {str(e)}. Please check the dataset ID and ensure it's valid in Google Earth Engine.")
-#             data = {}
-#     else:
-#         st.warning("Please enter a custom dataset ID to proceed.")
-#         data = {}
-
-# # Display the title for the Streamlit app
-# st.title(f"{imagery_base} Dataset")
-
-# # Select dataset category (main selection)
-# if data:
-#     main_selection = st.selectbox(f"Select {imagery_base} Dataset Category", list(data.keys()))
-# else:
-#     main_selection = None
-
-# # Initialize sub_selection and dataset_id as None
-# sub_selection = None
-# dataset_id = None
-
-# # 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(f"Select Specific {imagery_base} Dataset ID", list(sub_options.keys()))
-
-#     # Display the selected dataset ID and its availability based on user input
-#     if sub_selection:
-#         st.write(f"You selected: {main_selection} -> {sub_options[sub_selection]}")
-#         st.write(f"Dataset ID: {sub_selection}")
-#         dataset_id = sub_selection  # Use the key directly as the dataset ID
-
-#         # Fetch and display dataset availability in green text
-#         try:
-#             # Create an Earth Engine ImageCollection object for the selected dataset
-#             collection = ee.ImageCollection(dataset_id)
-            
-#             # Get the date range of the collection
-#             range_info = collection.reduceColumns(
-#                 reducer=ee.Reducer.minMax(),
-#                 selectors=['system:time_start']
-#             ).getInfo()
-            
-#             # Extract min and max timestamps (in milliseconds) and convert to readable dates
-#             min_time = range_info.get('min', None)
-#             max_time = range_info.get('max', None)
-            
-#             if min_time and max_time:
-#                 start_date = datetime.fromtimestamp(min_time / 1000).strftime('%Y-%m-%d')
-#                 end_date = datetime.fromtimestamp(max_time / 1000).strftime('%Y-%m-%d')
-#                 st.markdown(
-#                     f"<strong>Dataset Availability:</strong> From <span style='color: #fc0101;'>{start_date}</span> to <span style='color: #fc0101;'>{end_date}</span>",
-#                     unsafe_allow_html=True
-#                 )
-#             else:
-#                 st.markdown(
-#                     f"<span style='color: #fc0101;'><strong>Dataset Availability:</strong> Date range not available.</span>",
-#                     unsafe_allow_html=True
-#                 )
-                
-#         except Exception as e:
-#             st.error(f"Error fetching dataset availability: {str(e)}")
-
-# # Earth Engine Index Calculator Section
-# st.header("Earth Engine Index Calculator")
-
-# # Load band information based on selected dataset
-# if main_selection and sub_selection:
-#     dataset_bands = data[main_selection]["bands"].get(sub_selection, [])
-#     st.write(f"Available Bands for {sub_options[sub_selection]}: {', '.join(dataset_bands)}")
-
-#     # Allow user to select 1 or 2 bands
-#     selected_bands = st.multiselect(
-#         "Select 1 or 2 Bands for Calculation",
-#         options=dataset_bands,
-#         default=[dataset_bands[0]] if dataset_bands else [],
-#         help=f"Select 1 or 2 bands from: {', '.join(dataset_bands)}"
-#     )
-
-#     # Ensure minimum 1 and maximum 2 bands are selected
-#     if len(selected_bands) < 1:
-#         st.warning("Please select at least one band.")
-#         st.stop()
-
-#     # Show custom formula input if bands are selected
-#     if selected_bands:
-#         # Provide a default formula based on the number of selected bands
-#         if len(selected_bands) == 1:
-#             default_formula = f"{selected_bands[0]}"
-#             example = f"'{selected_bands[0]} * 2' or '{selected_bands[0]} + 1'"
-#         else:  # len(selected_bands) == 2
-#             default_formula = f"({selected_bands[0]} - {selected_bands[1]}) / ({selected_bands[0]} + {selected_bands[1]})"
-#             example = f"'{selected_bands[0]} * {selected_bands[1]} / 2' or '({selected_bands[0]} - {selected_bands[1]}) / ({selected_bands[0]} + {selected_bands[1]})'"
-
-#         custom_formula = st.text_input(
-#             "Enter Custom Formula (e.g (B8 - B4) / (B8 + B4) , B4*B3/2)",
-#             value=default_formula,
-#             help=f"Use only these bands: {', '.join(selected_bands)}. Examples: {example}"
-#         )
-
-#         # Validate the formula
-#         def validate_formula(formula, selected_bands):
-#             allowed_chars = set(" +-*/()0123456789.")
-#             terms = re.findall(r'[a-zA-Z][a-zA-Z0-9_]*', formula)
-#             invalid_terms = [term for term in terms if term not in selected_bands]
-#             if invalid_terms:
-#                 return False, f"Invalid terms in formula: {', '.join(invalid_terms)}. Use only {', '.join(selected_bands)}."
-#             if not all(char in allowed_chars or char in ''.join(selected_bands) for char in formula):
-#                 return False, "Formula contains invalid characters. Use only bands, numbers, and operators (+, -, *, /, ())"
-#             return True, ""
-
-#         is_valid, error_message = validate_formula(custom_formula, selected_bands)
-#         if not is_valid:
-#             st.error(error_message)
-#             st.stop()
-#         elif not custom_formula:
-#             st.warning("Please enter a custom formula to proceed.")
-#             st.stop()
-
-#         # Display the validated formula
-#         st.write(f"Custom Formula: {custom_formula}")
-
-# # Function to get the corresponding reducer based on user input
-# def get_reducer(reducer_name):
-#     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(),
-#     }
-#     return reducers.get(reducer_name.lower(), ee.Reducer.mean())
-
-# # Streamlit selectbox for reducer choice
-# reducer_choice = st.selectbox(
-#     "Select Reducer (e.g, mean , sum , median , min , max , count)",
-#     ['mean', 'sum', 'median', 'min', 'max', 'count'],
-#     index=0  # Default to 'mean'
-# )
-
-# # Function to convert geometry to Earth Engine format
-# def convert_to_ee_geometry(geometry):
-#     if isinstance(geometry, base.BaseGeometry):
-#         if geometry.is_valid:
-#             geojson = geometry.__geo_interface__
-#             return ee.Geometry(geojson)
-#         else:
-#             raise ValueError("Invalid geometry: The polygon geometry is not valid.")
-#     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:
-#                 return ee.Geometry(geometry)
-#             else:
-#                 raise ValueError("GeoJSON format is invalid.")
-#         except Exception as e:
-#             raise ValueError(f"Error parsing GeoJSON: {e}")
-#     elif isinstance(geometry, str) and geometry.lower().endswith(".kml"):
-#         try:
-#             tree = ET.parse(geometry)
-#             kml_root = tree.getroot()
-#             kml_namespace = {'kml': 'http://www.opengis.net/kml/2.2'}
-#             coordinates = kml_root.findall(".//kml:coordinates", kml_namespace)
-#             if coordinates:
-#                 coords_text = coordinates[0].text.strip()
-#                 coords = coords_text.split()
-#                 coords = [tuple(map(float, coord.split(','))) for coord in coords]
-#                 geojson = {"type": "Polygon", "coordinates": [coords]}
-#                 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.")
-
-# # 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 (e.g, Custom(Start Date to End Date) , Weekly , Monthly , Yearly)",
-#     ["Custom (Start Date to End Date)", "Weekly", "Monthly", "Yearly"],
-#     index=0
-# )
-
-# # 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"])
-
-# # Additional options based on shape type
-# kernel_size = None
-# include_boundary = None
-# if shape_type.lower() == "point":
-#     kernel_size = st.selectbox(
-#         "Select Calculation Area(e.g, Point , 3x3 Kernel , 5x5 Kernel)",
-#         ["Point", "3x3 Kernel", "5x5 Kernel"],
-#         index=0,
-#         help="Choose 'Point' for exact point calculation, or a kernel size for area averaging."
-#     )
-# elif shape_type.lower() == "polygon":
-#     include_boundary = st.checkbox(
-#         "Include Boundary Pixels",
-#         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
-#     if shape_type.lower() == "point":
-#         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()
-
-#         if 'geometry' in locations_df.columns:
-#             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()
-
-#         with st.spinner('Processing Map...'):
-#             if locations_df is not None and not locations_df.empty:
-#                 if 'geometry' in locations_df.columns:
-#                     locations_df['latitude'] = locations_df['geometry'].y
-#                     locations_df['longitude'] = locations_df['geometry'].x
-
-#                 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:
-#                     st.write("Preview of the uploaded points data:")
-#                     st.dataframe(locations_df.head())
-#                     m = leafmap.Map(center=[locations_df['latitude'].mean(), locations_df['longitude'].mean()], zoom=10)
-#                     for _, row in locations_df.iterrows():
-#                         latitude = row['latitude']
-#                         longitude = row['longitude']
-#                         if pd.isna(latitude) or pd.isna(longitude):
-#                             continue
-#                         m.add_marker(location=[latitude, longitude], popup=row.get('name', 'No Name'))
-#                     st.write("Map of Uploaded Points:")
-#                     m.to_streamlit()
-#                     st.session_state.map_data = m
-
-#     elif shape_type.lower() == "polygon":
-#         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()
-
-#         if 'geometry' in locations_df.columns:
-#             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()
-
-#         with st.spinner('Processing Map...'):
-#             if locations_df is not None and not locations_df.empty:
-#                 if 'geometry' not in locations_df.columns:
-#                     st.error("Uploaded file is missing required 'geometry' column.")
-#                 else:
-#                     st.write("Preview of the uploaded polygons data:")
-#                     st.dataframe(locations_df.head())
-#                     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)
-#                     for _, row in locations_df.iterrows():
-#                         polygon = row['geometry']
-#                         if polygon.is_valid:
-#                             gdf = gpd.GeoDataFrame([row], geometry=[polygon], crs=locations_df.crs)
-#                             m.add_gdf(gdf=gdf, layer_name=row.get('name', 'Unnamed Polygon'))
-#                     st.write("Map of Uploaded Polygons:")
-#                     m.to_streamlit()
-#                     st.session_state.map_data = m
-
-# # Initialize session state for storing results
-# 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
-# if 'show_example' not in st.session_state:
-#     st.session_state.show_example = True
-
-# # 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('selected_bands') != selected_bands or
-#         st.session_state.last_params.get('custom_formula') != custom_formula 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 or
-#         st.session_state.last_params.get('kernel_size') != kernel_size or
-#         st.session_state.last_params.get('include_boundary') != include_boundary
-#     )
-
-# # If parameters have changed, reset the results
-# if parameters_changed():
-#     st.session_state.results = []
-#     st.session_state.last_params = {
-#         'main_selection': main_selection,
-#         'dataset_id': dataset_id,
-#         'selected_bands': selected_bands,
-#         'custom_formula': custom_formula,
-#         'start_date_str': start_date_str,
-#         'end_date_str': end_date_str,
-#         'shape_type': shape_type,
-#         'file_upload': file_upload,
-#         'kernel_size': kernel_size,
-#         'include_boundary': include_boundary
-#     }
-
-# # Function to calculate custom formula
-# def calculate_custom_formula(image, geometry, selected_bands, custom_formula, reducer_choice, scale=30):
-#     try:
-#         band_values = {}
-#         band_names = image.bandNames().getInfo()
-        
-#         for band in selected_bands:
-#             if band not in band_names:
-#                 raise ValueError(f"Band '{band}' not found in the dataset.")
-#             band_values[band] = image.select(band)
-
-#         reducer = get_reducer(reducer_choice)
-#         reduced_values = {}
-#         for band in selected_bands:
-#             value = band_values[band].reduceRegion(
-#                 reducer=reducer,
-#                 geometry=geometry,
-#                 scale=scale
-#             ).get(band).getInfo()
-#             reduced_values[band] = float(value if value is not None else 0)
-
-#         formula = custom_formula
-#         for band in selected_bands:
-#             formula = formula.replace(band, str(reduced_values[band]))
-
-#         result = eval(formula, {"__builtins__": {}}, reduced_values)
-#         if not isinstance(result, (int, float)):
-#             raise ValueError("Formula did not result in a numeric value.")
-        
-#         return ee.Image.constant(result).rename('custom_result')
-    
-#     except ZeroDivisionError:
-#         st.error("Error: Division by zero in the formula.")
-#         return ee.Image(0).rename('custom_result').set('error', 'Division by zero')
-#     except SyntaxError:
-#         st.error(f"Error: Invalid syntax in formula '{custom_formula}'.")
-#         return ee.Image(0).rename('custom_result').set('error', 'Invalid syntax')
-#     except ValueError as e:
-#         st.error(f"Error: {str(e)}")
-#         return ee.Image(0).rename('custom_result').set('error', str(e))
-#     except Exception as e:
-#         st.error(f"Unexpected error: {e}")
-#         return ee.Image(0).rename('custom_result').set('error', str(e))
-
-# # Function to calculate index for a period
-# def calculate_index_for_period(image, roi, selected_bands, custom_formula, reducer_choice):
-#     return calculate_custom_formula(image, roi, selected_bands, custom_formula, reducer_choice)
-
-# # Aggregation functions
-# def aggregate_data_custom(collection):
-#     collection = collection.map(lambda image: image.set('day', ee.Date(image.get('system:time_start')).format('YYYY-MM-dd')))
-#     grouped_by_day = collection.aggregate_array('day').distinct()
-#     def calculate_daily_mean(day):
-#         daily_collection = collection.filter(ee.Filter.eq('day', day))
-#         daily_mean = daily_collection.mean()
-#         return daily_mean.set('day', day)
-#     daily_images = ee.List(grouped_by_day.map(calculate_daily_mean))
-#     return ee.ImageCollection(daily_images)
-
-# def aggregate_data_weekly(collection):
-#     def set_week_start(image):
-#         date = ee.Date(image.get('system:time_start'))
-#         days_since_week_start = date.getRelative('day', 'week')
-#         offset = ee.Number(days_since_week_start).multiply(-1)
-#         week_start = date.advance(offset, 'day')
-#         return image.set('week_start', week_start.format('YYYY-MM-dd'))
-#     collection = collection.map(set_week_start)
-#     grouped_by_week = collection.aggregate_array('week_start').distinct()
-#     def calculate_weekly_mean(week_start):
-#         weekly_collection = collection.filter(ee.Filter.eq('week_start', week_start))
-#         weekly_mean = weekly_collection.mean()
-#         return weekly_mean.set('week_start', week_start)
-#     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):
-#     collection = collection.filterDate(start_date, end_date)
-#     collection = collection.map(lambda image: image.set('month', ee.Date(image.get('system:time_start')).format('YYYY-MM')))
-#     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)
-#     monthly_images = ee.List(grouped_by_month.map(calculate_monthly_mean))
-#     return ee.ImageCollection(monthly_images)
-
-# def aggregate_data_yearly(collection):
-#     collection = collection.map(lambda image: image.set('year', ee.Date(image.get('system:time_start')).format('YYYY')))
-#     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)
-#     yearly_images = ee.List(grouped_by_year.map(calculate_yearly_mean))
-#     return ee.ImageCollection(yearly_images)
-
-# # Process aggregation function
-# def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id, selected_bands, reducer_choice, shape_type, aggregation_period, custom_formula="", kernel_size=None, include_boundary=None):
-#     aggregated_results = []
-    
-#     if 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}")
-                
-#                 if kernel_size == "3x3 Kernel":
-#                     buffer_size = 45  # 90m x 90m
-#                     roi = ee.Geometry.Point([longitude, latitude]).buffer(buffer_size).bounds()
-#                 elif kernel_size == "5x5 Kernel":
-#                     buffer_size = 75  # 150m x 150m
-#                     roi = ee.Geometry.Point([longitude, latitude]).buffer(buffer_size).bounds()
-#                 else:  # Point
-#                     roi = ee.Geometry.Point([longitude, latitude])
-                
-#                 collection = ee.ImageCollection(dataset_id) \
-#                     .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
-#                     .filterBounds(roi)
-                
-#                 if aggregation_period.lower() == 'custom (start date to end date)':
-#                     collection = aggregate_data_custom(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)
-                
-#                 image_list = collection.toList(collection.size())
-#                 processed_weeks = set()
-#                 for i in range(image_list.size().getInfo()):
-#                     image = ee.Image(image_list.get(i))
-                    
-#                     if aggregation_period.lower() == 'custom (start date to end date)':
-#                         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()
-#                         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, selected_bands, custom_formula, reducer_choice)
-                    
-#                     try:
-#                         index_value = index_image.reduceRegion(
-#                             reducer=get_reducer(reducer_choice),
-#                             geometry=roi,
-#                             scale=30
-#                         ).get('custom_result')
-                        
-#                         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)
-#                     if not include_boundary:
-#                         roi = roi.buffer(-30).bounds()
-#                 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)
-                
-#                 if aggregation_period.lower() == 'custom (start date to end date)':
-#                     collection = aggregate_data_custom(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)
-                
-#                 image_list = collection.toList(collection.size())
-#                 processed_weeks = set()
-#                 for i in range(image_list.size().getInfo()):
-#                     image = ee.Image(image_list.get(i))
-                    
-#                     if aggregation_period.lower() == 'custom (start date to end date)':
-#                         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()
-#                         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, selected_bands, custom_formula, reducer_choice)
-                    
-#                     try:
-#                         index_value = index_image.reduceRegion(
-#                             reducer=get_reducer(reducer_choice),
-#                             geometry=roi,
-#                             scale=30
-#                         ).get('custom_result')
-                        
-#                         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() == 'custom (start date to end date)':
-#             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 []
-
-# # Button to trigger calculation
-# if st.button(f"Calculate {custom_formula}"):
-#     if file_upload is not None:
-#         if shape_type.lower() in ["point", "polygon"]:
-#             results = process_aggregation(
-#                 locations_df,
-#                 start_date_str,
-#                 end_date_str,
-#                 dataset_id,
-#                 selected_bands,
-#                 reducer_choice,
-#                 shape_type,
-#                 aggregation_period,
-#                 custom_formula,
-#                 kernel_size=kernel_size,
-#                 include_boundary=include_boundary
-#             )
-#             if results:
-#                 result_df = pd.DataFrame(results)
-#                 st.write(f"Processed Results Table ({aggregation_period}) for Formula: {custom_formula}")
-#                 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'
-#                 )
-#                 # Show an example calculation
-#                 if st.session_state.show_example and results:
-#                     example_result = results[0]
-#                     example_image = ee.ImageCollection(dataset_id).filterDate(start_date_str, end_date_str).first()
-#                     example_roi = (
-#                         ee.Geometry.Point([example_result['Longitude'], example_result['Latitude']]) 
-#                         if shape_type.lower() == 'point' 
-#                         else convert_to_ee_geometry(locations_df['geometry'].iloc[0])
-#                     )
-#                     example_values = {}
-#                     for band in selected_bands:
-#                         value = example_image.select(band).reduceRegion(
-#                             reducer=get_reducer(reducer_choice),
-#                             geometry=example_roi,
-#                             scale=30
-#                         ).get(band).getInfo()
-#                         example_values[band] = float(value if value is not None else 0)
-#                     example_formula = custom_formula
-#                     for band in selected_bands:
-#                         example_formula = example_formula.replace(band, str(example_values[band]))
-#                     # st.write(f"Example Calculation: {custom_formula} -> {example_formula} = {example_result.get('Calculated Value', example_result.get('Aggregated Value'))}")
-#                     st.session_state.show_example = False
-#                 st.success('Processing complete!')
-#             else:
-#                 st.warning("No results were generated. Check your inputs or formula.")
-#         else:
-#             st.warning("Please upload a file to process.")
-#     else:
-#         st.warning("Please upload a file to proceed.")
-
-
-
 import streamlit as st
 import json
 import ee
@@ -1359,7 +579,6 @@ if file_upload is not None:
                     m.to_streamlit()
                     st.session_state.map_data = m
 
-# ... (Rest of the code until convert_to_ee_geometry) ...
 # Initialize session state for storing results
 if 'results' not in st.session_state:
     st.session_state.results = []