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	from ssl import Options
	import joblib
	import pandas as pd
	import streamlit as st


	model = joblib.load('model (1).joblib')
	unique_values = joblib.load('unique_values (2).joblib')


	#unique_post_on = unique_values["Posted On"]
	unique_fea_1 = unique_values["fea_1"]
	#unique_fea_2 = unique_values["fea_2"]
	unique_fea_3 = unique_values["fea_3"]
	#unique_fea_4 = unique_values["fea_4"]
	unique_fea_5 = unique_values["fea_5"]
	unique_fea_6 = unique_values["fea_6"]
	unique_fea_7 = unique_values["fea_7"]
	#unique_fea_8 = unique_values["fea_8"]
	unique_fea_9 = unique_values["fea_9"]
	#unique_fea_10 = unique_values["fea_10"]
	#unique_fea_11 = unique_values["fea_11"]


	def main():
	st.title("Customer check risk")

	with st.form("questionaire"):
	fea1 = st.slider(min_value = 2, max_value=7 )
	fea3 = st.slider(min_value = 1, max_value=2 )
	fea5 = st.selectbox(options =unique_fea_5 )
	fea6 = st.slider(min_value=3, max_value = 15 )
	fea7 = st.slider(min_value=-1, max_value =10 )
	fea9 = st.selectbox(options =unique_fea_9 )

	# clicked==True only when the button is clicked
	clicked = st.form_submit_button("credit risk")
	if clicked:
	result=model.predict(pd.DataFrame({"fea1": [fea1],
	"fea3": [fea3],
	"fea5": [fea5],
	"fea6": [fea6],
	"fea7": [fea7],
	"fea9": [fea9]
	}))
	# Show prediction
	if result==1:
	result='the customer is in high credit risk'
	else :
	result='the customer is in low credit risk'
	st.success('Your risk is '+result)
	# Run main()
	if __name__=='__main__':
	main()