def create_index_piechart(uid,fieldid,img_type, image_date):
	import matplotlib.pyplot as plt
	import numpy as np
	import os,sys
	import cv2
	import csv
	import firebase_admin
	from firebase_admin import credentials
	from firebase_admin import db
	import seaborn as sns
	sns.set()
	import csv
	import time
	import traceback
	import xlsxwriter
	cred = credentials.Certificate('servicekey.json')
	try:
		firebase_admin.initialize_app(cred, {'databaseURL': 'https://farmbase-b2f7e-31c0c.firebaseio.com/'})
	except:
		wes1 = 1#print('fire running')
	import matplotlib.patches as mpatches
	plt.rcParams.update({'font.size':12})
	if img_type=='ndvi':
		lb = 'NDVI: '
		lb2 = 'NDVI'
	elif img_type=='ndwi':
		lb = 'NDWI: '
		lb2 = 'NDWI'
	elif img_type=='vari':
		lb = 'VARI: '
		lb2 = 'VARI'
	elif img_type=='evi':
		lb = 'EVI: '
		lb2 = 'EVI'
	elif img_type=='savi':
		lb = 'SAVI: '
		lb2 = 'SAVI'
	elif img_type=='avi':
		lb = 'AVI: '
		lb2 = 'AVI'
	elif img_type=='bsi':
		lb = 'BSI: '
		lb2 = 'BSI'
	elif img_type=='si':
		lb = 'SI: '
		lb2 = 'SI'
	elif img_type=='soc':
		lb = 'SOC: '
		lb2 = 'SOC'
	elif img_type=='ndmi':
		lb = 'NDMI: '
		lb2 = 'NDMI'
	elif img_type=='lai':
		lb = 'LAI: '
		lb2 = 'LAI'
	elif img_type=='rvi':
		lb = 'RVI: '
		lb2 = 'RVI'
	elif img_type=='lai':
		lb = 'RSM: '
		lb2 = 'RSM'
	else:
		lb='NDRE: '
		lb2 = 'NDRE'	
	labels = (lb+'-1 to 0.1'),(lb+'0.1 to 0.2'),(lb+'0.2 to 0.3'),(lb+'0.3 to 0.4'),(lb+'0.4 to 0.5'),(lb+'0.5 to 0.6'),(lb+'0.6 to 0.7'),(lb+'0.7 to 0.8'),(lb+'0.8 to 0.9'),(lb+'0.9 to 1.0')

	labels_vari = (lb+'-1 to -0.8'),(lb+'-0.8 to -0.6'),(lb+'-0.6 to -0.4'),(lb+'-0.4 to -0.2'),(lb+'-0.2 to 0'),(lb+'0 to 0.2'),(lb+'0.2 to 0.4'),(lb+'0.4 to 0.6'),(lb+'0.6 to 0.8'),(lb+'0.8 to 1.0')

	if img_type =='vari':
		labels = labels_vari
	cmap = ["#ab0535","#ea4f3b","#f7885a","#fbc07e","#fff0b5","#e6f3a4","#bae383","#81bf6c","#11a75f","#06653d"]
	file_name = uid +'/' + fieldid+'/IndexValues_'+img_type+'.csv'
	abc = 0
	sqm = []
	ind_values = []
	explode = (0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2)
	wes1 = 1#print(['file_name', file_name])
	with open(file_name,'rt') as f:
		data = csv.reader(f)
		for row in data:
			db.reference('PaidMonitoredFields').child('PMF').child(uid).child(fieldid).child('IndexBreakdown').child(image_date).child(img_type).set(row[1:11])
			for values in row:
				if (abc>0) and (abc<11):
					sq1 = (float(values)/10000)
					sq1 = round(sq1,1)
					ind_values.append(sq1)
					ss = str(values) + ' sq. m.'
					sqm.append(ss)
				if abc==0:
					avg_ind_value = values
				if abc==11:
					field_area = values
				if abc==12:
					good_area = values
				if abc==13:
					avg_area = values
				if abc==14:
					bad_area = values
				abc = abc+1
	fig1,ax1 = plt.subplots()
	wes1 = 1#print(labels)
	wes1 = 1#print(ind_values)
	sizes = ind_values
	#ax1.set_prop_cycle("color",cmap)
	#ax1.pie(ind_values,labeldistance = 2, explode=explode, labels=labels, autopct='%1.1f%%',shadow=True, startangle=90)
	ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
	ax1.set_title(("Field Area (in square meters) in Different "+lb2+" Ranges"))
	patch1 = mpatches.Patch(color="#ab0535",label=(labels[0]+' -> '+sqm[0]))
	patch2 = mpatches.Patch(color="#ea4f3b",label=(labels[1]+' -> '+sqm[1]))
	patch3 = mpatches.Patch(color="#f7885a",label=(labels[2]+' -> '+sqm[2]))
	patch4 = mpatches.Patch(color="#fbc07e",label=(labels[3]+' -> '+sqm[3]))
	patch5 = mpatches.Patch(color="#fff0b5",label=(labels[4]+' -> '+sqm[4]))
	patch6 = mpatches.Patch(color="#e6f3a4",label=(labels[5]+' -> '+sqm[5]))
	patch7 = mpatches.Patch(color="#bae383",label=(labels[6]+' -> '+sqm[6]))
	patch8 = mpatches.Patch(color="#81bf6c",label=(labels[7]+' -> '+sqm[7]))
	patch9 = mpatches.Patch(color="#11a75f",label=(labels[8]+' -> '+sqm[8]))
	patch10 = mpatches.Patch(color="#06653d",label=(labels[9]+' -> '+sqm[9]))
	ax1.legend(loc = 'center',handles=[patch10,patch9,patch8,patch7,patch6,patch5,patch4,patch3,patch2,patch1],title='Field Area in Different Health Zones')
	plt.show()
	file_name = uid+'/' + fieldid+'/'+img_type+'_piechart.png'
	plt.savefig(file_name)
	plt.close()
	pie_c =	cv2.imread(file_name)
	pie_c = pie_c[100:400,120:500]
	cv2.imwrite(file_name,pie_c)

	return avg_ind_value,field_area