import os
import ee
import geemap
import json
import geopandas as gpd
import streamlit as st
import pandas as pd
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 
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)
ee.Initialize()

# Functions
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
  
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.")

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

    polygon_info = {}

    # 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])

      # 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]

      # Calculate area and perimeter in EPSG:7761 projection
      # It is a local projection defined for Gujarat as per NNRMS
      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

# 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)

# 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

# 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"])



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()