app.py

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 xml.etree import ElementTree as XET
from concurrent.futures import ThreadPoolExecutor, as_completed
import time
import matplotlib.pyplot as plt
import plotly.express as px

# 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 and Title
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,
)
st.markdown(
    f"""
    <div style="display: flex; flex-direction: column; align-items: center;">
        <img src="https://huggingface.co/spaces/YashMK89/GEE_Calculator/resolve/main/SATRANG.png" style="width: 30%;">
        <h3 style="text-align: center; margin: 0;">( Spatial and Temporal Aggregation for Remote-sensing Analysis of GEE Data )</h3>
    </div>
    <hr>
    """,
    unsafe_allow_html=True,
)

# Authenticate and initialize Earth Engine
earthengine_credentials = os.environ.get("EE_Authentication")
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')

# Helper function to get reducer
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())

# 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 = XET.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.")

# # 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 custom formula with dynamic scale handling
def calculate_custom_formula(image, geometry, selected_bands, custom_formula, reducer_choice, dataset_id, user_scale=None):
    try:
        # Determine the scale: Use user-defined scale if provided, otherwise use dataset's native resolution
        default_scale = ee.ImageCollection(dataset_id).first().select(0).projection().nominalScale().getInfo()
        scale = user_scale if user_scale is not None else default_scale
        
        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  # Use the determined scale here
            ).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))

# 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_daily(collection):
    """
    Aggregates data on a daily basis.
    """
    def set_day_start(image):
        date = ee.Date(image.get('system:time_start'))
        day_start = date.format('YYYY-MM-dd')
        return image.set('day_start', day_start)
    
    collection = collection.map(set_day_start)
    grouped_by_day = collection.aggregate_array('day_start').distinct()
    
    def calculate_daily_mean(day_start):
        daily_collection = collection.filter(ee.Filter.eq('day_start', day_start))
        daily_mean = daily_collection.mean()
        return daily_mean.set('day_start', day_start)
    
    daily_images = ee.List(grouped_by_day.map(calculate_daily_mean))
    return ee.ImageCollection(daily_images)


def aggregate_data_weekly(collection, start_date_str, end_date_str):
    """
    Aggregates data on a weekly basis, starting from the exact start date provided by the user.
    """
    start_date = ee.Date(start_date_str)
    end_date = ee.Date(end_date_str)
    
    # Calculate the number of weeks between the start and end dates
    days_diff = end_date.difference(start_date, 'day')
    num_weeks = days_diff.divide(7).ceil().getInfo()  # Total number of weeks
    
    weekly_images = []
    for week in range(num_weeks):
        week_start = start_date.advance(week * 7, 'day')  # Start of the week
        week_end = week_start.advance(7, 'day')  # End of the week
        
        weekly_collection = collection.filterDate(week_start, week_end)
        if weekly_collection.size().getInfo() > 0:
            weekly_mean = weekly_collection.mean()
            weekly_mean = weekly_mean.set('week_start', week_start.format('YYYY-MM-dd'))
            weekly_images.append(weekly_mean)
    
    return ee.ImageCollection.fromImages(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)

def calculate_cloud_percentage(image, cloud_band='QA60'):
    """
    Calculate the percentage of cloud-covered pixels in an image using the QA60 bitmask.
    Assumes the presence of the QA60 cloud mask band.
    """
    # Decode the QA60 bitmask
    qa60 = image.select(cloud_band)
    opaque_clouds = qa60.bitwiseAnd(1 << 10)  # Bit 10: Opaque clouds
    cirrus_clouds = qa60.bitwiseAnd(1 << 11)  # Bit 11: Cirrus clouds

    # Combine both cloud types into a single cloud mask
    cloud_mask = opaque_clouds.Or(cirrus_clouds)

    # Count total pixels and cloudy pixels
    total_pixels = qa60.reduceRegion(
        reducer=ee.Reducer.count(),
        geometry=image.geometry(),
        scale=60,  # QA60 resolution is 60 meters
        maxPixels=1e13
    ).get(cloud_band)
    cloudy_pixels = cloud_mask.reduceRegion(
        reducer=ee.Reducer.sum(),
        geometry=image.geometry(),
        scale=60,  # QA60 resolution is 60 meters
        maxPixels=1e13
    ).get(cloud_band)

    # Calculate cloud percentage
    if total_pixels == 0:
        return 0  # Avoid division by zero
    return ee.Number(cloudy_pixels).divide(ee.Number(total_pixels)).multiply(100)

# # Preprocessing function with cloud filtering
# def preprocess_collection(collection, cloud_threshold):
#     """
#     Apply cloud filtering to the image collection using the QA60 bitmask.
#     - Tile-based filtering: Exclude tiles with cloud coverage exceeding the selected threshold.
#     - Pixel-based filtering: Mask out individual cloudy pixels.
#     """
#     def filter_tile(image):
#         # Calculate cloud percentage for the tile
#         cloud_percentage = calculate_cloud_percentage(image, cloud_band='QA60')
#         # Keep the tile only if cloud percentage is below the threshold
#         return image.set('cloud_percentage', cloud_percentage).updateMask(cloud_percentage.lt(cloud_threshold))

#     def mask_cloudy_pixels(image):
#         # Decode the QA60 bitmask
#         qa60 = image.select('QA60')
#         opaque_clouds = qa60.bitwiseAnd(1 << 10)  # Bit 10: Opaque clouds
#         cirrus_clouds = qa60.bitwiseAnd(1 << 11)  # Bit 11: Cirrus clouds

#         # Combine both cloud types into a single cloud mask
#         cloud_mask = opaque_clouds.Or(cirrus_clouds)

#         # Mask out cloudy pixels
#         clear_pixels = cloud_mask.Not()  # Invert the mask to keep clear pixels
#         return image.updateMask(clear_pixels)

#     # Step 1: Apply tile-based filtering
#     filtered_collection = collection.map(filter_tile)
#     # Step 2: Apply pixel-based filtering
#     masked_collection = filtered_collection.map(mask_cloudy_pixels)
#     return masked_collection

# Preprocessing function with separate tile-based and pixel-based cloud filtering
def preprocess_collection(collection, tile_cloud_threshold, pixel_cloud_threshold):
    """
    Apply cloud filtering to the image collection using the QA60 bitmask.
    - Tile-based filtering: Exclude tiles with cloud coverage exceeding the selected threshold.
    - Pixel-based filtering: Mask out individual cloudy pixels exceeding the selected threshold.
    """
    def filter_tile(image):
        # Calculate cloud percentage for the tile
        cloud_percentage = calculate_cloud_percentage(image, cloud_band='QA60')
        # Keep the tile only if cloud percentage is below the threshold
        return image.set('cloud_percentage', cloud_percentage).updateMask(cloud_percentage.lt(tile_cloud_threshold))

    def mask_cloudy_pixels(image):
        # Decode the QA60 bitmask
        qa60 = image.select('QA60')
        opaque_clouds = qa60.bitwiseAnd(1 << 10)  # Bit 10: Opaque clouds
        cirrus_clouds = qa60.bitwiseAnd(1 << 11)  # Bit 11: Cirrus clouds
        # Combine both cloud types into a single cloud mask
        cloud_mask = opaque_clouds.Or(cirrus_clouds)
        # Mask out cloudy pixels based on pixel threshold
        clear_pixels = cloud_mask.Not()  # Invert the mask to keep clear pixels
        return image.updateMask(clear_pixels)

    # Step 1: Apply tile-based filtering
    filtered_collection = collection.map(filter_tile)
    # Step 2: Apply pixel-based filtering
    masked_collection = filtered_collection.map(mask_cloudy_pixels)
    return masked_collection

# Worker function for processing a single geometry
def process_single_geometry(row, start_date_str, end_date_str, dataset_id, selected_bands, reducer_choice, shape_type, aggregation_period, custom_formula, original_lat_col, original_lon_col, kernel_size=None, include_boundary=None):
    if shape_type.lower() == "point":
        latitude = row.get('latitude')
        longitude = row.get('longitude')
        if pd.isna(latitude) or pd.isna(longitude):
            return None  # Skip invalid points
        location_name = row.get('name', f"Location_{row.name}")
        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])
    elif shape_type.lower() == "polygon":
        polygon_geometry = row.get('geometry')
        location_name = row.get('name', f"Polygon_{row.name}")
        try:
            roi = convert_to_ee_geometry(polygon_geometry)
            if not include_boundary:
                roi = roi.buffer(-30).bounds()
        except ValueError:
            return None  # Skip invalid polygons

    # Filter and aggregate the image collection
    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() == 'daily':
        collection = aggregate_data_daily(collection)
    elif aggregation_period.lower() == 'weekly':
        collection = aggregate_data_weekly(collection, start_date_str, end_date_str)
    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()
    aggregated_results = []
    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() == 'daily':
            timestamp = image.get('day_start')
            period_label = 'Date'
            date = ee.String(timestamp).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_custom_formula(image, roi, selected_bands, custom_formula, reducer_choice, scale=scale)
        try:
            index_value = index_image.reduceRegion(
                reducer=get_reducer(reducer_choice),
                geometry=roi,
                scale=scale
            ).get('custom_result')
            calculated_value = index_value.getInfo()
            if isinstance(calculated_value, (int, float)):
                result = {
                    'Location Name': location_name,
                    period_label: date,
                    'Start Date': start_date_str,
                    'End Date': end_date_str,
                    'Calculated Value': calculated_value
                }
                if shape_type.lower() == 'point':
                    result[original_lat_col] = latitude  # Use original column name
                    result[original_lon_col] = longitude  # Use original column name
                aggregated_results.append(result)
        except Exception as e:
            st.error(f"Error retrieving value for {location_name}: {e}")
    return aggregated_results


# # Main processing function
# def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id, selected_bands, reducer_choice, shape_type, aggregation_period, original_lat_col, original_lon_col, custom_formula="", kernel_size=None, include_boundary=None, cloud_threshold=0):
#     aggregated_results = []
#     total_steps = len(locations_df)
#     progress_bar = st.progress(0)
#     progress_text = st.empty()
#     start_time = time.time()  # Start timing the process
    
#     # Preprocess the image collection with cloud filtering
#     raw_collection = ee.ImageCollection(dataset_id) \
#         .filterDate(ee.Date(start_date_str), ee.Date(end_date_str))

#     # Print the size of the original collection
#     st.write(f"Original Collection Size: {raw_collection.size().getInfo()}")
    
#     # Apply cloud filtering if threshold > 0
#     if cloud_threshold > 0:
#         raw_collection = preprocess_collection(raw_collection, cloud_threshold)

#         # Print the size of the preprocessed collection
#         st.write(f"Preprocessed Collection Size: {raw_collection.size().getInfo()}")
    
#     with ThreadPoolExecutor(max_workers=10) as executor:
#         futures = []
#         for idx, row in locations_df.iterrows():
#             future = executor.submit(
#                 process_single_geometry,
#                 row,
#                 start_date_str,
#                 end_date_str,
#                 dataset_id,
#                 selected_bands,
#                 reducer_choice,
#                 shape_type,
#                 aggregation_period,
#                 custom_formula,
#                 original_lat_col,
#                 original_lon_col,
#                 kernel_size,
#                 include_boundary
#             )
#             futures.append(future)
#         completed = 0
#         for future in as_completed(futures):
#             result = future.result()
#             if result:
#                 aggregated_results.extend(result)
#             completed += 1
#             progress_percentage = completed / total_steps
#             progress_bar.progress(progress_percentage)
#             progress_text.markdown(f"Processing: {int(progress_percentage * 100)}%")
    
#     # End timing the process
#     end_time = time.time()
#     processing_time = end_time - start_time  # Calculate total processing time
    
#     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[original_lat_col] = 'first'
#                 agg_dict[original_lon_col] = 'first'
#             aggregated_output = result_df.groupby('Location Name').agg(agg_dict).reset_index()
#             aggregated_output['Date Range'] = aggregated_output['Start Date'] + " to " + aggregated_output['End Date']
#             aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
#             return aggregated_output.to_dict(orient='records'), processing_time
#         else:
#             return result_df.to_dict(orient='records'), processing_time 
#     return [], processing_time  

# Aggregation logic for custom date range
def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id, selected_bands, reducer_choice, shape_type, aggregation_period, original_lat_col, original_lon_col, custom_formula="", kernel_size=None, include_boundary=None, tile_cloud_threshold=0, pixel_cloud_threshold=0):
    aggregated_results = []
    total_steps = len(locations_df)
    progress_bar = st.progress(0)
    progress_text = st.empty()
    start_time = time.time()  # Start timing the process

    # Preprocess the image collection with cloud filtering
    raw_collection = ee.ImageCollection(dataset_id) \
        .filterDate(ee.Date(start_date_str), ee.Date(end_date_str))
    
    # Print the size of the original collection
    st.write(f"Original Collection Size: {raw_collection.size().getInfo()}")

    # Apply cloud filtering if thresholds > 0
    if tile_cloud_threshold > 0 or pixel_cloud_threshold > 0:
        raw_collection = preprocess_collection(raw_collection, tile_cloud_threshold, pixel_cloud_threshold)
        # Print the size of the preprocessed collection
        st.write(f"Preprocessed Collection Size: {raw_collection.size().getInfo()}")

    with ThreadPoolExecutor(max_workers=10) as executor:
        futures = []
        for idx, row in locations_df.iterrows():
            future = executor.submit(
                process_single_geometry,
                row,
                start_date_str,
                end_date_str,
                dataset_id,
                selected_bands,
                reducer_choice,
                shape_type,
                aggregation_period,
                custom_formula,
                original_lat_col,
                original_lon_col,
                kernel_size,
                include_boundary
            )
            futures.append(future)
        completed = 0
        for future in as_completed(futures):
            result = future.result()
            if result:
                aggregated_results.extend(result)
            completed += 1
            progress_percentage = completed / total_steps
            progress_bar.progress(progress_percentage)
            progress_text.markdown(f"Processing: {int(progress_percentage * 100)}%")

    # End timing the process
    end_time = time.time()
    processing_time = end_time - start_time  # Calculate total processing time

    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'  # Fixed column name to "Calculated Value"
            }
            if shape_type.lower() == 'point':
                agg_dict[original_lat_col] = 'first'
                agg_dict[original_lon_col] = 'first'
            aggregated_output = result_df.groupby('Location Name').agg(agg_dict).reset_index()
            aggregated_output['Date Range'] = aggregated_output['Start Date'] + " to " + aggregated_output['End Date']
            aggregated_output.rename(columns={'Calculated Value': 'Calculated Value'}, inplace=True)  # Ensure correct naming
            return aggregated_output.to_dict(orient='records'), processing_time
        else:
            return result_df.to_dict(orient='records'), processing_time 
    return [], processing_time  

# Streamlit App Logic
st.markdown("<h5>Image Collection</h5>", unsafe_allow_html=True)
imagery_base = st.selectbox("Select Imagery Base", ["Sentinel", "Landsat", "MODIS", "VIIRS", "Custom Input"], index=0)

# Initialize data as an empty dictionary
data = {}
if imagery_base == "Sentinel":
    dataset_file = "sentinel_datasets.json"
    try:
        with open(dataset_file) as f:
            data = json.load(f)
    except FileNotFoundError:
        st.error(f"Dataset file '{dataset_file}' not found.")
        data = {}
elif imagery_base == "Landsat":
    dataset_file = "landsat_datasets.json"
    try:
        with open(dataset_file) as f:
            data = json.load(f)
    except FileNotFoundError:
        st.error(f"Dataset file '{dataset_file}' not found.")
        data = {}
elif imagery_base == "MODIS":
    dataset_file = "modis_datasets.json"
    try:
        with open(dataset_file) as f:
            data = json.load(f)
    except FileNotFoundError:
        st.error(f"Dataset file '{dataset_file}' not found.")
        data = {}
elif imagery_base == "VIIRS":  # New VIIRS Option
    dataset_file = "viirs_datasets.json"
    try:
        with open(dataset_file) as f:
            data = json.load(f)
    except FileNotFoundError:
        st.error(f"Dataset file '{dataset_file}' not found.")
        data = {}
elif imagery_base == "Custom Input":
    custom_dataset_id = st.text_input("Enter Custom Earth Engine Dataset ID (e.g., AHN/AHN4)", value="")
    if custom_dataset_id:
        try:
            if custom_dataset_id.startswith("ee.ImageCollection("):
                custom_dataset_id = custom_dataset_id.replace("ee.ImageCollection('", "").replace("')", "")
            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 = {}
if not data:
    st.error("No valid dataset available. Please check your inputs.")
    st.stop()

st.markdown("<hr><h5><b>{}</b></h5>".format(imagery_base), unsafe_allow_html=True)
main_selection = st.selectbox(f"Select {imagery_base} Dataset Category", list(data.keys()))
sub_selection = None
dataset_id = None
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()))
    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

st.markdown("<hr><h5><b>Earth Engine Index Calculator</b></h5>", unsafe_allow_html=True)
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)}")
    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)}"
    )
    if len(selected_bands) < 1:
        st.warning("Please select at least one band.")
        st.stop()
    if 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:
            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}"
        )
        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()
        st.write(f"Custom Formula: {custom_formula}")

reducer_choice = st.selectbox(
    "Select Reducer (e.g, mean , sum , median , min , max , count)",
    ['mean', 'sum', 'median', 'min', 'max', 'count'],
    index=0
)

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'))
start_date_str = start_date.strftime('%Y-%m-%d')
end_date_str = end_date.strftime('%Y-%m-%d')

# st.markdown("<h5>Cloud Filtering</h5>", unsafe_allow_html=True)
# cloud_threshold = st.slider(
#     "Select Maximum Cloud Coverage Threshold (%)",
#     min_value=0,
#     max_value=50,
#     value=20,
#     step=5,
#     help="Tiles with cloud coverage exceeding this threshold will be excluded. Individual cloudy pixels will also be masked."
# )

# Cloud Filtering Section
if imagery_base == "Sentinel" and "Sentinel-2" in sub_options[sub_selection]:
    st.markdown("<h5>Cloud Filtering</h5>", unsafe_allow_html=True)

    # Separate thresholds for tile-based and pixel-based cloud filtering
    tile_cloud_threshold = st.slider(
        "Select Maximum Tile-Based Cloud Coverage Threshold (%)",
        min_value=0,
        max_value=100,
        value=20,
        step=5,
        help="Tiles with cloud coverage exceeding this threshold will be excluded."
    )

    pixel_cloud_threshold = st.slider(
        "Select Maximum Pixel-Based Cloud Coverage Threshold (%)",
        min_value=0,
        max_value=100,
        value=10,
        step=5,
        help="Individual pixels with cloud coverage exceeding this threshold will be masked."
    )
aggregation_period = st.selectbox(
    "Select Aggregation Period (e.g, Custom(Start Date to End Date) , Daily , Weekly , Monthly , Yearly)",
    ["Custom (Start Date to End Date)", "Daily", "Weekly", "Monthly", "Yearly"],
    index=0
)

shape_type = st.selectbox("Do you want to process 'Point' or 'Polygon' data?", ["Point", "Polygon"])

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."
    )

# Add Scale Input
st.markdown("<h5>Calculation Scale</h5>", unsafe_allow_html=True)
default_scale = ee.ImageCollection(dataset_id).first().select(0).projection().nominalScale().getInfo()
user_scale = st.number_input(
    "Enter Calculation Scale (meters) [Leave blank to use dataset's default scale]",
    min_value=1,
    value=default_scale,
    help=f"Default scale for this dataset is {default_scale} meters. Adjust if needed."
)

file_upload = st.file_uploader(f"Upload your {shape_type} data (CSV, GeoJSON, KML)", type=["csv", "geojson", "kml"])
locations_df = pd.DataFrame()
original_lat_col = None
original_lon_col = None

if file_upload is not None:
    if shape_type.lower() == "point":
        if file_upload.name.endswith('.csv'):
            # Read the CSV file
            locations_df = pd.read_csv(file_upload)
            # Show the first few rows to help user identify columns
            st.write("Preview of your uploaded data (first 5 rows):")
            st.dataframe(locations_df.head())
            # Get all column names from the uploaded file
            all_columns = locations_df.columns.tolist()
            # Let user select latitude and longitude columns from dropdown
            col1, col2 = st.columns(2)
            with col1:
                original_lat_col = st.selectbox(
                    "Select Latitude Column",
                    options=all_columns,
                    index=all_columns.index('latitude') if 'latitude' in all_columns else 0,
                    help="Select the column containing latitude values"
                )
            with col2:
                original_lon_col = st.selectbox(
                    "Select Longitude Column",
                    options=all_columns,
                    index=all_columns.index('longitude') if 'longitude' in all_columns else 0,
                    help="Select the column containing longitude values"
                )
            # Validate the selected columns contain numeric data
            if not pd.api.types.is_numeric_dtype(locations_df[original_lat_col]) or not pd.api.types.is_numeric_dtype(locations_df[original_lon_col]):
                st.error("Error: Selected Latitude and Longitude columns must contain numeric values")
                st.stop()
            # Rename the selected columns to standard names for processing
            locations_df = locations_df.rename(columns={
                original_lat_col: 'latitude',
                original_lon_col: 'longitude'
            })
        elif file_upload.name.endswith('.geojson'):
            locations_df = gpd.read_file(file_upload)
            if 'geometry' in locations_df.columns:
                locations_df['latitude'] = locations_df['geometry'].y
                locations_df['longitude'] = locations_df['geometry'].x
                original_lat_col = 'latitude'
                original_lon_col = 'longitude'
            else:
                st.error("GeoJSON file doesn't contain geometry column")
                st.stop()
        elif file_upload.name.endswith('.kml'):
            kml_string = file_upload.read().decode('utf-8')
            try:
                root = XET.fromstring(kml_string)
                ns = {'kml': 'http://www.opengis.net/kml/2.2'}
                points = []
                for placemark in root.findall('.//kml:Placemark', ns):
                    name = placemark.findtext('kml:name', default=f"Point_{len(points)}", namespaces=ns)
                    coords_elem = placemark.find('.//kml:Point/kml:coordinates', ns)
                    if coords_elem is not None:
                        coords_text = coords_elem.text.strip()
                        coords = [c.strip() for c in coords_text.split(',')]
                        if len(coords) >= 2:
                            lon, lat = float(coords[0]), float(coords[1])
                            points.append({'name': name, 'geometry': f"POINT ({lon} {lat})"})
                if not points:
                    st.error("No valid Point data found in the KML file.")
                else:
                    locations_df = gpd.GeoDataFrame(points, geometry=gpd.GeoSeries.from_wkt([p['geometry'] for p in points]), crs="EPSG:4326")
                    locations_df['latitude'] = locations_df['geometry'].y
                    locations_df['longitude'] = locations_df['geometry'].x
                    original_lat_col = 'latitude'
                    original_lon_col = 'longitude'
            except Exception as e:
                st.error(f"Error parsing KML file: {str(e)}")
        # Display map for points if we have valid data
        if not locations_df.empty and 'latitude' in locations_df.columns and 'longitude' in locations_df.columns:
            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()
    elif shape_type.lower() == "polygon":
        if file_upload.name.endswith('.csv'):
            st.error("CSV upload not supported for polygons. Please upload a GeoJSON or KML file.")
        elif file_upload.name.endswith('.geojson'):
            locations_df = gpd.read_file(file_upload)
            if 'geometry' not in locations_df.columns:
                st.error("GeoJSON file doesn't contain geometry column")
                st.stop()
        elif file_upload.name.endswith('.kml'):
            kml_string = file_upload.read().decode('utf-8')
            try:
                root = XET.fromstring(kml_string)
                ns = {'kml': 'http://www.opengis.net/kml/2.2'}
                polygons = []
                for placemark in root.findall('.//kml:Placemark', ns):
                    name = placemark.findtext('kml:name', default=f"Polygon_{len(polygons)}", namespaces=ns)
                    coords_elem = placemark.find('.//kml:Polygon//kml:coordinates', ns)
                    if coords_elem is not None:
                        coords_text = ' '.join(coords_elem.text.split())
                        coord_pairs = [pair.split(',')[:2] for pair in coords_text.split() if pair]
                        if len(coord_pairs) >= 4:
                            coords_str = " ".join([f"{float(lon)} {float(lat)}" for lon, lat in coord_pairs])
                            polygons.append({'name': name, 'geometry': f"POLYGON (({coords_str}))"})
                if not polygons:
                    st.error("No valid Polygon data found in the KML file.")
                else:
                    locations_df = gpd.GeoDataFrame(polygons, geometry=gpd.GeoSeries.from_wkt([p['geometry'] for p in polygons]), crs="EPSG:4326")
            except Exception as e:
                st.error(f"Error parsing KML file: {str(e)}")
        # Display map for polygons if we have valid data
        if not locations_df.empty and 'geometry' in locations_df.columns:
            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()

if st.button(f"Calculate {custom_formula}"):
    if not locations_df.empty:
        with st.spinner("Processing Data..."):
            try:
                results, processing_time = process_aggregation(
                    locations_df,
                    start_date_str,
                    end_date_str,
                    dataset_id,
                    selected_bands,
                    reducer_choice,
                    shape_type,
                    aggregation_period,
                    original_lat_col,
                    original_lon_col,
                    custom_formula,
                    kernel_size,
                    include_boundary,
                    tile_cloud_threshold=tile_cloud_threshold if "tile_cloud_threshold" in locals() else 0,
                    pixel_cloud_threshold=pixel_cloud_threshold if "pixel_cloud_threshold" in locals() else 0,
                    user_scale=user_scale
                )
                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'
                    )
                    st.success(f"Processing complete! Total processing time: {processing_time:.2f} seconds.")
                    
                    # Graph Visualization Section
                    st.markdown("<h5>Graph Visualization</h5>", unsafe_allow_html=True)
                    
                    # Dynamically identify the time column
                    if aggregation_period.lower() == 'custom (start date to end date)':
                        x_column = 'Date Range'  
                    elif 'Date' in result_df.columns:
                        x_column = 'Date'
                    elif 'Week' in result_df.columns:
                        x_column = 'Week'
                    elif 'Month' in result_df.columns:
                        x_column = 'Month'
                    elif 'Year' in result_df.columns:
                        x_column = 'Year'
                    else:
                        st.warning("No valid time column found for plotting.")
                        st.stop()
                        
                    y_column = 'Calculated Value'
                    
                    # # Line Chart
                    # st.subheader("Line Chart")
                    # st.line_chart(result_df.set_index(x_column)[y_column])
                    
                    # # Bar Chart
                    # st.subheader("Bar Chart")
                    # st.bar_chart(result_df.set_index(x_column)[y_column])
                    
                    # Advanced Plot (Plotly)
                    st.subheader("Advanced Interactive Plot (Plotly)")
                    fig = px.line(
                        result_df,
                        x=x_column,
                        y=y_column,
                        color='Location Name',
                        title=f"{custom_formula} Over Time"
                    )
                    st.plotly_chart(fig)
                
                else:
                    st.warning("No results were generated. Check your inputs or formula.")
                    st.info(f"Total processing time: {processing_time:.2f} seconds.")
            
            except Exception as e:
                st.error(f"An error occurred during processing: {str(e)}")
    else:
        st.warning("Please upload a valid file to proceed.")



# if st.button(f"Calculate {custom_formula}"):
#     if not locations_df.empty:
#         with st.spinner("Processing Data..."):
#             try:
#                 results, processing_time = process_aggregation(
#                     locations_df,
#                     start_date_str,
#                     end_date_str,
#                     dataset_id,
#                     selected_bands,
#                     reducer_choice,
#                     shape_type,
#                     aggregation_period,
#                     original_lat_col,
#                     original_lon_col,
#                     custom_formula,
#                     kernel_size,
#                     include_boundary,
#                     cloud_threshold=cloud_threshold
#                 )
#                 if results:
#                     result_df = pd.DataFrame(results)
#                     st.write(f"Processed Results Table ({aggregation_period}) for Formula: {custom_formula}")
#                     st.dataframe(result_df)
                    
#                     # Debug: Print column names to verify
#                     st.write("Available columns in results:", result_df.columns.tolist())
                    
#                     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.success(f"Processing complete! Total processing time: {processing_time:.2f} seconds.")
                    
#                     # Graph Visualization Section
#                     st.markdown("<h5>Graph Visualization</h5>", unsafe_allow_html=True)
                    
#                     # Dynamically identify the value column (handle both 'Calculated Value' and 'Aggregated Value')
#                     value_column = None
#                     if 'Calculated Value' in result_df.columns:
#                         value_column = 'Calculated Value'
#                     elif 'Aggregated Value' in result_df.columns:
#                         value_column = 'Aggregated Value'
#                     else:
#                         st.warning("No value column found for plotting. Available columns: " + ", ".join(result_df.columns))
#                         st.stop()
                    
#                     # Dynamically identify the time column
#                     if aggregation_period.lower() == 'custom (start date to end date)':
#                         x_column = 'Date Range'  
#                     elif 'Date' in result_df.columns:
#                         x_column = 'Date'
#                     elif 'Week' in result_df.columns:
#                         x_column = 'Week'
#                     elif 'Month' in result_df.columns:
#                         x_column = 'Month'
#                     elif 'Year' in result_df.columns:
#                         x_column = 'Year'
#                     else:
#                         st.warning("No valid time column found for plotting. Available columns: " + ", ".join(result_df.columns))
#                         st.stop()
                        
#                     # Ensure we have valid data to plot
#                     if result_df.empty:
#                         st.warning("No data available for plotting.")
#                         st.stop()
                    
#                     # Line Chart
#                     try:
#                         st.subheader("Line Chart")
#                         st.line_chart(result_df.set_index(x_column)[value_column])
#                     except Exception as e:
#                         st.error(f"Error creating line chart: {str(e)}")
                    
#                     # Bar Chart
#                     try:
#                         st.subheader("Bar Chart")
#                         st.bar_chart(result_df.set_index(x_column)[value_column])
#                     except Exception as e:
#                         st.error(f"Error creating bar chart: {str(e)}")
                    
#                     # Advanced Plot (Plotly)
#                     try:
#                         st.subheader("Advanced Interactive Plot (Plotly)")
#                         fig = px.line(
#                             result_df,
#                             x=x_column,
#                             y=value_column,
#                             color='Location Name' if 'Location Name' in result_df.columns else None,
#                             title=f"{custom_formula} Over Time"
#                         )
#                         st.plotly_chart(fig)
#                     except Exception as e:
#                         st.error(f"Error creating interactive plot: {str(e)}")
                
#                 else:
#                     st.warning("No results were generated. Check your inputs or formula.")
#                     st.info(f"Total processing time: {processing_time:.2f} seconds.")
            
#             except Exception as e:
#                 st.error(f"An error occurred during processing: {str(e)}")
#     else:
#         st.warning("Please upload a valid file to proceed.")

# if st.button(f"Calculate {custom_formula}"):
#     if not locations_df.empty:
#         with st.spinner("Processing Data..."):
#             try:
#                 results, processing_time = process_aggregation(
#                     locations_df,
#                     start_date_str,
#                     end_date_str,
#                     dataset_id,
#                     selected_bands,
#                     reducer_choice,
#                     shape_type,
#                     aggregation_period,
#                     original_lat_col,
#                     original_lon_col,
#                     custom_formula,
#                     kernel_size,
#                     include_boundary,
#                     cloud_threshold=cloud_threshold
#                 )
#                 if results:
#                     result_df = pd.DataFrame(results)
                    
#                     # Reorder and rename columns
#                     column_mapping = {
#                         'Location Name': 'Location Name',
#                         'Start Date': 'Start Date',
#                         'End Date': 'End Date',
#                         'Date Range': 'Date Range',
#                         original_lat_col: 'Latitude',
#                         original_lon_col: 'Longitude',
#                         'Aggregated Value': 'Calculated Value',
#                         'Calculated Value': 'Calculated Value'
#                     }
                    
#                     # Keep only columns that exist in the results
#                     available_columns = [col for col in column_mapping.keys() if col in result_df.columns]
#                     result_df = result_df[available_columns]
#                     result_df = result_df.rename(columns={k:v for k,v in column_mapping.items() if k in available_columns})
                    
#                     st.write(f"Processed Results Table ({aggregation_period}) for Formula: {custom_formula}")
#                     st.dataframe(result_df)
                    
#                     # Graph Visualization Section
#                     st.markdown("<h5>Graph Visualization</h5>", unsafe_allow_html=True)
                    
#                     # Determine time column based on aggregation period
#                     time_column_map = {
#                         'custom (start date to end date)': 'Date Range',
#                         'daily': 'Date',
#                         'weekly': 'Week',
#                         'monthly': 'Month',
#                         'yearly': 'Year'
#                     }
                    
#                     x_column = time_column_map.get(aggregation_period.lower())
                    
#                     if x_column not in result_df.columns:
#                         # Try to find any time-related column
#                         time_columns = ['Date Range', 'Date', 'Week', 'Month', 'Year', 'day', 'month', 'year']
#                         x_column = next((col for col in time_columns if col in result_df.columns), None)
                    
#                     if x_column is None:
#                         st.warning("No time column found for plotting. Showing data without time axis.")
#                         x_column = 'Location Name'
                    
#                     value_column = 'Calculated Value'
                    
#                     if value_column not in result_df.columns:
#                         st.error("No calculated values found for plotting.")
#                         st.stop()
                    
#                     # Line Chart
#                     try:
#                         st.subheader("Line Chart")
#                         if x_column == 'Location Name':
#                             st.line_chart(result_df.set_index(x_column)[value_column])
#                         else:
#                             # Convert to datetime for better sorting
#                             result_df[x_column] = pd.to_datetime(result_df[x_column], errors='ignore')
#                             result_df = result_df.sort_values(x_column)
#                             st.line_chart(result_df.set_index(x_column)[value_column])
#                     except Exception as e:
#                         st.error(f"Error creating line chart: {str(e)}")
                    
#                     # Bar Chart
#                     try:
#                         st.subheader("Bar Chart")
#                         if x_column == 'Location Name':
#                             st.bar_chart(result_df.set_index(x_column)[value_column])
#                         else:
#                             result_df[x_column] = pd.to_datetime(result_df[x_column], errors='ignore')
#                             result_df = result_df.sort_values(x_column)
#                             st.bar_chart(result_df.set_index(x_column)[value_column])
#                     except Exception as e:
#                         st.error(f"Error creating bar chart: {str(e)}")
                    
#                     # Advanced Plot (Plotly)
#                     try:
#                         st.subheader("Advanced Interactive Plot (Plotly)")
#                         if x_column == 'Location Name':
#                             fig = px.bar(
#                                 result_df,
#                                 x=x_column,
#                                 y=value_column,
#                                 color='Location Name',
#                                 title=f"{custom_formula} by Location"
#                             )
#                         else:
#                             fig = px.line(
#                                 result_df,
#                                 x=x_column,
#                                 y=value_column,
#                                 color='Location Name',
#                                 title=f"{custom_formula} Over Time"
#                             )
#                         st.plotly_chart(fig)
#                     except Exception as e:
#                         st.error(f"Error creating interactive plot: {str(e)}")
                    
#                     # Download button
#                     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.success(f"Processing complete! Total processing time: {processing_time:.2f} seconds.")
                
#                 else:
#                     st.warning("No results were generated. Check your inputs or formula.")
#                     st.info(f"Total processing time: {processing_time:.2f} seconds.")
            
#             except Exception as e:
#                 st.error(f"An error occurred during processing: {str(e)}")
#     else:
#         st.warning("Please upload a valid file to proceed.")