import pandas as pd
from collections import defaultdict
from itertools import zip_longest
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 pd
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import statsmodels.api as sm
import seaborn as sns
sns.set()
from zipfile import ZipFile
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, MimeType, CRS, BBox, WmsRequest,DataCollection
import traceback
from firebase_admin import firestore
from PIL import Image
import numpy as np
from google.cloud import storage
import os
from firebase_admin import db
from get_mask import get_mask
import cv2
import scipy.ndimage
from google.oauth2 import service_account
import datetime
from datetime import date
from get_polygon_mask import get_polygon_mask
import requests
import traceback
from ftplib import FTP
import paramiko
import pysftp
import math
storage_client = storage.Client.from_service_account_json("servicekey.json"); 

bucket_name = 'farmbase-b2f7e.appspot.com'
cred = service_account.Credentials.from_service_account_file('servicekey.json')
#cred = credentials.Certificate('servicekey.json')
bucket = storage_client.bucket(bucket_name)
try:
    firebase_admin.initialize_app(credentials.Certificate('servicekey.json'), {'databaseURL': 'https://farmbase-b2f7e-31c0c.firebaseio.com/'})
except:
    print('fire running')
# Read the Excel file
df = pd.read_excel("Downloads/trst.xlsx")
print(df.columns)

# Access the strings stored in the B column
strings = df['textPayload']

# Initialize a defaultdict to store counts
counts = defaultdict(int)

# Count occurrences of each string
# for string in strings:
#     counts[string] += 1
for string in strings:
    key = string.split('/')[1]
    counts[key] += 1
# Print counts
field_count = 0
total_area = 0
uid = 'D4xU2QGhooXfK6qiEeHdAlp0wk53'
for string, count in counts.items():
    #print(f"{string}: {count}")
    field_area = db.reference("PaidMonitoredFields").child("PMF").child(uid).child(string).child("FieldArea").get()
    total_area = total_area + field_area
    field_count = field_count + 1
    #print(field_area, total_area)
print(field_count, total_area)