from tempfile import tempdir
import firebase_admin
from firebase_admin import credentials
from firebase_admin import db
from find_s1_image import find_img_value
import pandas as pdf
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import statsmodels.api as sm
import seaborn as sns
sns.set()
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
import csv
import time
import json
from find_modis_ndvi import find_modis_ndvi
import numpy as np
from sklearn.cluster import KMeans
from find_study_area_values import find_study_area_values
from find_study_area_values3 import find_study_area_values3
from make_area_estimate_image import make_area_estimate_image
from make_egypt_estimate_image import make_egypt_estimate_image
from sentinelhub import WebFeatureService, BBox, CRS, DataSource, MimeType, CRS, BBox, WmsRequest,DataCollection
import traceback
from get_mask import get_mask
from PIL import Image
import numpy as np
from google.cloud import storage

cred = credentials.Certificate('servicekey.json')
x = 0.0003
y = 0.0003
fromdate = '20220701'
todate = '20220715'


s1_images = ['IW-VH-DB']
s2_images = []
# s1_images = ["B02", "B03", "B04", "B05"]
#s2_images = ["RVI-NEW"]
try:
    firebase_admin.initialize_app(cred, {'databaseURL': 'https://farmbase-b2f7e-31c0c.firebaseio.com/'})
except:
    print('fire running')
#study_uid = "MBFshQgimkS6AwSDtqAyOpRXZW12"
study_uid = "vPM6EKWHfHRxmRfc0YJ7PZ8J9tR2"
study_uid = "vYw5pBtwLhcSlKIz5YikEr27GMu2"
study_uid = "vPM6EKWHfHRxmRfc0YJ7PZ8J9tR2"
gt_uid = 'hxA3VYEFpaOluurIbHxdzd4gPlw1'
different_gt_uid = 'joCrKyhfobbzgCxCQPHJfOOKKPF3'
study_field_id = '1661604460461'
#study_field_id = '1599307314955'
gt_fields_dir = 'Downloads/PaddyGTpointsMP.geojson'
storage_client = storage.Client()
bucket_name = 'farmbase-b2f7e.appspot.com'

study_fields = db.reference('PaidMonitoredFields').child('PMF').child(study_uid).get()
sentinelSettings = db.reference('SentinelSettings').get()
clientID = sentinelSettings["ClientID"]
clientSecret = sentinelSettings["ClientSecret"]
wmsID = sentinelSettings["WMSID"]
rviID = sentinelSettings["RVIID"]
demID = sentinelSettings["DEMID"]
modisID = sentinelSettings["MODISID"]
field_num = 0
from_date = '2022-09-15T00:00:00'
to_date = '2022-10-7T23:59:59'
max_num = 18

main_from_date = '20220105'
main_to_date = '20220502'
def append_img_vals(temp_arr, img_values):
    if temp_arr is not None:
        for k in temp_arr:
            img_values.append(k)
    return img_values
all_fields_img_values = []
gt_fields_img_values = []

with open("all_field_img_values.csv", mode = "r") as file:
    csvFile = csv.reader(file)
    for line in csvFile:
    #    print(line)
        all_fields_img_values.append(line)

#gt_fields_dir = 'crop_wise_img_values.json'
gt_fields_dir = 'temp1.json'
with open(gt_fields_dir) as f:
    crop_wise_img_values = json.load(f)
# print(all_fields_img_values)
# print(crop_wise_img_values)
k_groups = 7
X = []
y = []
all_fields_img_values = []
main_arr = []
for (crop, crop_values) in crop_wise_img_values.items():
    for single_arr in crop_values:
        X.append(single_arr)
        all_fields_img_values.append(single_arr)
        if crop == "Corn":
            y.append(-1)
        elif crop == "cotton":
            y.append(4)
        elif crop == "rice":
            y.append(-1)
            main_arr.append(single_arr[0:max_num])
        elif crop == "Yellow corn":
            y.append(-1)
        else:
            y.append(-1)

kmeans = KMeans(k_groups)
X = np.array(X)
y = np.array(y)
x_num = 0
for k in X:
    k = k[0:max_num]
    X[x_num] = k

# print(X)
print(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
ii = 0
regressor = RandomForestRegressor(n_estimators=200,random_state=0)
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X)
print(y_pred)
#print(pred_vals)
main_y = [[4,-1,-1,-1,-1],[-1,4,-1,-1,-1],[-1,-1,4,-1,-1],[-1,-1,-1,4,-1]]
veg_arr = ["Corn","cotton","rice","Yellow corn"]
field_num = 0
for (field_id, study_field) in study_fields.items():
    try:
        field_name = study_field["FieldDescription"]
    except:
        field_name = study_field["FieldAddress"]
    print(field_name)
    coordinates = study_field["Coordinates"]
    midlat = (float(study_field["FieldMinLat"]) + float(study_field["FieldMaxLat"]))/2
    midlng = (float(study_field["FieldMinLong"]) + float(study_field["FieldMaxLong"]))/2
    bounds = [float(study_field["FieldMinLong"]),float(study_field["FieldMinLat"]),float(study_field["FieldMaxLong"]),float(study_field["FieldMaxLat"])]
    print(bounds)
    image_num = 0
    find_modis_ndvi(study_uid, field_name, DataCollection.MODIS, "NDVI", bounds, from_date, to_date, clientID, clientSecret, modisID, max_num)
    get_mask(study_uid,study_field_id,coordinates,float(study_field["FieldMaxLat"]),float(study_field["FieldMinLat"]),float(study_field["FieldMaxLong"]),float(study_field["FieldMinLong"]), field_name)
    bucket = storage_client.get_bucket(bucket_name)
    destination_blob_name_dem = 'PaidMonitoredFields/'+study_uid+'/'+study_field_id+'/10101010/ndvi'
    dem_file_name = study_uid + "/area_estimate_ndvi_" + field_name + ".png"
    blob = bucket.blob(destination_blob_name_dem)
    blob.upload_from_filename(dem_file_name)