import os
import ee
import geemap
import json
import geopandas as gpd
import streamlit as st
import pandas as pd
<<<<<<< HEAD
from fastkml import kml
import geojson
from shapely.geometry import Polygon, MultiPolygon, shape, Point

=======
import geojson
from shapely.geometry import Polygon, MultiPolygon, shape, Point
from io import BytesIO


# Enable fiona driver
gpd.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'

#Intialize EE library 
# Error in EE Authentication 
>>>>>>> 4072fc2e14e38a014c456666d781a212750773a4
ee_credentials = os.environ.get("EE")
os.makedirs(os.path.expanduser("~/.config/earthengine/"), exist_ok=True)
with open(os.path.expanduser("~/.config/earthengine/credentials"), "w") as f:
    f.write(ee_credentials)
<<<<<<< HEAD

ee.Initialize()

def convert_3d_to_2d(geometry):
    """
    Recursively convert any 3D coordinates in a geometry to 2D.
    """
    if geometry.is_empty:
        return geometry

    if geometry.geom_type == 'Polygon':
        return geojson.Polygon([[(x, y) for x, y, *_ in ring] for ring in geometry.coordinates])

    elif geometry.geom_type == 'MultiPolygon':
        return geojson.MultiPolygon([
            [[(x, y) for x, y, *_ in ring] for ring in poly]
            for poly in geometry.coordinates
        ])

    elif geometry.geom_type == 'LineString':
        return geojson.LineString([(x, y) for x, y, *_ in geometry.coordinates])

    elif geometry.geom_type == 'MultiLineString':
        return geojson.MultiLineString([
            [(x, y) for x, y, *_ in line]
            for line in geometry.coordinates
        ])

    elif geometry.geom_type == 'Point':
        x, y, *_ = geometry.coordinates
        return geojson.Point((x, y))

    elif geometry.geom_type == 'MultiPoint':
        return geojson.MultiPoint([(x, y) for x, y, *_ in geometry.coordinates])

    return geometry  # Return unchanged if not a supported geometry type

=======
ee.Initialize()

# Functions
>>>>>>> 4072fc2e14e38a014c456666d781a212750773a4
def convert_to_2d_geometry(geom): #Handles Polygon Only
  if geom is None:
    return None
  elif geom.has_z:
    # Extract exterior coordinates and convert to 2D
    exterior_coords = geom.exterior.coords[:]  # Get all coordinates of the exterior ring
    exterior_coords_2d = [(x, y) for x, y, *_ in exterior_coords] # Keep only the x and y coordinates, ignoring z

    # Handle interior rings (holes) if any
    interior_coords_2d = []
    for interior in geom.interiors:
        interior_coords = interior.coords[:]
        interior_coords_2d.append([(x, y) for x, y, *_ in interior_coords])

    # Create a new Polygon with 2D coordinates
    return type(geom)(exterior_coords_2d, interior_coords_2d)
  else:
    return geom
  
<<<<<<< HEAD
def kml_to_geojson(kml_string):
  k = kml.KML()
  k.from_string(kml_string.encode('utf-8'))  # Convert the string to bytes
  features = list(k.features())
  
  geojson_features = []
  for feature in features:
      geometry_2d = convert_3d_to_2d(feature.geometry)
      geojson_features.append(geojson.Feature(geometry=geometry_2d))
  
  geojson_data = geojson.FeatureCollection(geojson_features)
  return geojson_data

# Calculate NDVI as Normalized Index
def reduce_zonal_ndvi(image, ee_object):
  ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
  image = image.addBands(ndvi)
  image = image.select('NDVI')
  reduced = image.reduceRegion(
    reducer=ee.Reducer.mean(), 
    geometry=ee_object.geometry(),
    scale=10, 
    maxPixels=1e12
  )
  return image.set(reduced)

# Validate KML File for Single Polygon and return polygon information
def validate_KML_file(kml_file):
    try:
      gdf = gpd.read_file(kml_file)
    except Exception as e:
      ValueError("Input must be a valid KML file.")
=======
def validate_KML_file(gdf):
    # try:
    #   gdf = gpd.read_file(BytesIO(uploaded_file.read()), driver='KML')
    # except Exception as e:
    #   ValueError("Input must be a valid KML file.")
>>>>>>> 4072fc2e14e38a014c456666d781a212750773a4

    if gdf.empty:
      return {
        'corner_points': None,
        'area': None,
        'perimeter': None,
        'is_single_polygon': False}

    polygon_info = {}
<<<<<<< HEAD
  
    # Check if it's a single polygon or multipolygon
    if isinstance(gdf.iloc[0].geometry, Polygon):
      polygon_info['is_single_polygon'] = True
      
      polygon = gdf.geometry.iloc[0] 
=======

    # Check if it's a single polygon or multipolygon
    if isinstance(gdf.iloc[0].geometry, Polygon):
      polygon_info['is_single_polygon'] = True

      polygon = convert_to_2d_geometry(gdf.geometry.iloc[0])
>>>>>>> 4072fc2e14e38a014c456666d781a212750773a4

      # Calculate corner points in GCS projection
      polygon_info['corner_points'] = [
          (polygon.bounds[0], polygon.bounds[1]),
          (polygon.bounds[2], polygon.bounds[1]),
          (polygon.bounds[2], polygon.bounds[3]),
          (polygon.bounds[0], polygon.bounds[3])
      ]

      # Calculate Centroids in GCS projection
      polygon_info['centroid'] = polygon.centroid.coords[0]

<<<<<<< HEAD
      # Calculate area and perimeter in EPSG:7761 projection 
      # It is a local projection defined for Gujarat as per NNRMS 
=======
      # Calculate area and perimeter in EPSG:7761 projection
      # It is a local projection defined for Gujarat as per NNRMS
>>>>>>> 4072fc2e14e38a014c456666d781a212750773a4
      polygon = gdf.to_crs(epsg=7761).geometry.iloc[0]
      polygon_info['area'] = polygon.area
      polygon_info['perimeter'] = polygon.length

    else:
      polygon_info['is_single_polygon'] = False
      polygon_info['corner_points'] = None
      polygon_info['area'] = None
      polygon_info['perimeter'] = None
      polygon_info['centroid'] = None
      ValueError("Input must be a single Polygon.")

    return polygon_info

<<<<<<< HEAD
=======
# Calculate NDVI as Normalized Index
def reduce_zonal_ndvi(image, ee_object):
  ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
  image = image.addBands(ndvi)
  image = image.select('NDVI')
  reduced = image.reduceRegion(
    reducer=ee.Reducer.mean(),
    geometry=ee_object.geometry(),
    scale=10,
    maxPixels=1e12
  )
  return image.set(reduced)

>>>>>>> 4072fc2e14e38a014c456666d781a212750773a4
# Get Zonal NDVI
def get_zonal_ndvi(collection, geom_ee_object):
  reduced_collection = collection.map(lambda image: reduce_zonal_ndvi(image, ee_object=geom_ee_object))
  stats_list = reduced_collection.aggregate_array('NDVI').getInfo()
  filenames = reduced_collection.aggregate_array('system:index').getInfo()
  dates = [f.split("_")[0].split('T')[0] for f in reduced_collection.aggregate_array('system:index').getInfo()]
  df = pd.DataFrame({'NDVI': stats_list, 'Date': dates, 'Imagery': filenames})
  return df

<<<<<<< HEAD
def geojson_to_ee(geojson_data):
  ee_object = ee.FeatureCollection(geojson_data)
  return ee_object

def kml_to_gdf(kml_file):
  try:
    gdf = gpd.read_file(kml_file)
    for i in range(len(gdf)):
      geom = gdf.iloc[i].geometry
      new_geom = convert_to_2d_geometry(geom)
      gdf.loc[i, 'geometry'] = new_geom
      print(gdf.iloc[i].geometry)
    print(f"KML file '{kml_file}' successfully read")
  except Exception as e:
    print(f"Error: {e}")
  return gdf

=======
>>>>>>> 4072fc2e14e38a014c456666d781a212750773a4
# put title in center
st.markdown("""
<style>
h1 {
    text-align: center;
}
</style>
""", unsafe_allow_html=True)

st.title("Mean NDVI Calculator")

# get the start and end date from the user
col = st.columns(2)
start_date = col[0].date_input("Start Date", value=pd.to_datetime('2021-01-01'))
end_date = col[1].date_input("End Date", value=pd.to_datetime('2021-01-30'))
start_date = start_date.strftime("%Y-%m-%d")
end_date = end_date.strftime("%Y-%m-%d")

max_cloud_cover = st.number_input("Max Cloud Cover", value=20)

# Get the geojson file from the user
uploaded_file = st.file_uploader("Upload KML/GeoJSON file", type=["geojson", "kml"])

<<<<<<< HEAD
# Read the KML file
if uploaded_file is None:
    file_name = "Bhankhara_Df_11_he_5_2020-21.geojson"
    st.write(f"Using default file: {file_name}")
    data = gpd.read_file(file_name)
    with open(file_name) as f:
      str_data = f.read()
else:
    st.write(f"Using uploaded file: {uploaded_file.name}")
    file_name = uploaded_file.name
    bytes_data = uploaded_file.getvalue()
    str_data = bytes_data.decode("utf-8")


if file_name.endswith(".geojson"):
  geojson_data = json.loads(str_data)
elif file_name.endswith(".kml"):
  geojson_data = json.loads(kml_to_gdf(str_data).to_json())

# Read Geojson File
ee_object = geojson_to_ee(geojson_data)

# Filter data based on the date, bounds, cloud coverage and select NIR and Red Band
collection = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").filterBounds(ee_object).filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', max_cloud_cover)).filter(ee.Filter.date(start_date, end_date)).select(['B4', 'B8'])

polygon_info = validate_KML_file(str_data)

if polygon_info['is_single_polygon']:
  # Read KML file
  geom_ee_object = ee.FeatureCollection(geojson_data)

  # Add buffer of 100m to ee_object
  buffered_ee_object = geom_ee_object.map(lambda feature: feature.buffer(100))
  
  # Filter data based on the date, bounds, cloud coverage and select NIR and Red Band
  collection = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").filterBounds(geom_ee_object).filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)).filter(ee.Filter.date('2022-01-01', '2023-01-01')).select(['B4', 'B8'])

  # Get Zonal NDVI based on collection and geometries (Original KML and Buffered KML)
  df_geom = get_zonal_ndvi(collection, geom_ee_object)
  df_buffered_geom = get_zonal_ndvi(collection, buffered_ee_object)

  # Merge both Zonalstats and create resultant dataframe
  resultant_df = pd.merge(df_geom, df_buffered_geom, on='Date', how='inner')
  resultant_df = resultant_df.rename(columns={'NDVI_x': 'AvgNDVI_Inside', 'NDVI_y': 'Avg_NDVI_Buffer'})
  resultant_df['Ratio'] = resultant_df['AvgNDVI_Inside'] / resultant_df['Avg_NDVI_Buffer']
  resultant_df.drop(columns=['Imagery_y'], inplace=True)

  # Re-order the columns of the resultant dataframe
  resultant_df = resultant_df[['Date', 'Imagery_x', 'AvgNDVI_Inside', 'Avg_NDVI_Buffer', 'Ratio']]

  # Map = geemap.Map(center=(polygon_info['centroid'][1],polygon_info['centroid'][0]) , zoom=12)
  # Map.addLayer(geom_ee_object, {}, 'Layer1')
  # Map.addLayer(buffered_ee_object, {}, 'Layer2')

  # plot the time series
  st.write("Time Series Plot")
  st.line_chart(resultant_df.set_index('Date'))

  #st.write(f"Overall Mean NDVI: {resultant_df['Mean NDVI'].mean():.2f}")

else:
  print("Input must be a single Polygon.")
=======


if uploaded_file is not None:
    try:
        if uploaded_file.name.endswith("kml"):
            gdf = gpd.read_file(BytesIO(uploaded_file.read()), driver='KML')
        elif uploaded_file.name.endswith("geojson"):
            gdf = gpd.read_file(uploaded_file)
    except Exception as e:
      st.write('ValueError: "Input must be a valid KML file."')
      st.stop()
    
    # Validate KML File
    polygon_info = validate_KML_file(gdf)

    if polygon_info["is_single_polygon"]==True:
        st.write("Uploaded KML file has single geometry.")
        st.write("It has bounds as {0:.6f}, {1:.6f}, {2:.6f}, and {3:.6f}.".format(
            polygon_info['corner_points'][0][0],
            polygon_info['corner_points'][0][1],
            polygon_info['corner_points'][2][0],
            polygon_info['corner_points'][2][1]
        ))
        st.write("It has centroid at ({0:.6f}, {1:.6f}).".format(polygon_info['centroid'][0], polygon_info['centroid'][1]))
        st.write("It has area of {:.2f} meter squared.".format(polygon_info['area']))
        st.write("It has perimeter of {:.2f} meters.".format(polygon_info['perimeter']))

        # # Read KML file
        # geom_ee_object = ee.FeatureCollection(json.loads(gdf.to_json()))

        # # Add buffer of 100m to ee_object
        # buffered_ee_object = geom_ee_object.map(lambda feature: feature.buffer(100))

        # # Filter data based on the date, bounds, cloud coverage and select NIR and Red Band
        # collection = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', max_cloud_cover)).filter(ee.Filter.date(start_date, end_date)).select(['B4', 'B8'])

        # # Get Zonal NDVI based on collection and geometries (Original KML and Buffered KML)
        # df_geom = get_zonal_ndvi(collection, geom_ee_object)
        # df_buffered_geom = get_zonal_ndvi(collection, buffered_ee_object)

        # # Merge both Zonalstats and create resultant dataframe
        # resultant_df = pd.merge(df_geom, df_buffered_geom, on='Date', how='inner')
        # resultant_df = resultant_df.rename(columns={'NDVI_x': 'AvgNDVI_Inside', 'NDVI_y': 'Avg_NDVI_Buffer', 'Imagery_x': 'Imagery'})
        # resultant_df['Ratio'] = resultant_df['AvgNDVI_Inside'] / resultant_df['Avg_NDVI_Buffer']
        # resultant_df.drop(columns=['Imagery_y'], inplace=True)

        # # Re-order the columns of the resultant dataframe
        # resultant_df = resultant_df[['Date', 'Imagery', 'AvgNDVI_Inside', 'Avg_NDVI_Buffer', 'Ratio']]

        # st.write(resultant_df)
    
    else:
        st.write('ValueError: "Input must have single polygon geometry"')
        st.write(gdf)
        st.stop()
>>>>>>> 4072fc2e14e38a014c456666d781a212750773a4