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import streamlit as st
import pickle
import json
import pandas as pd
import numpy as np
with open("list_num_columns.txt", 'r') as file_1:
list_num_skew_columns = json.load(file_1)
with open("list_cat_nominal_columns.txt", "r") as file_2:
nom_col_skew = json.load(file_2)
with open("list_cat_ordinal_columns.txt", "r") as file_3:
ord_col_skew = json.load(file_3)
with open("best_pipeline.pkl", "rb") as file_4:
best_pipeline = pickle.load(file_4)
def run():
# create form
with st.form("form"):
age = st.number_input("age",
min_value= 5,
max_value= 90,
value=30,
step=2)
flight_distance = st.number_input("flight distance",
min_value= 31,
max_value= 5000,
value=850,
step=10)
departure_delay_in_minutes = st.number_input("departure delay in minutes",
min_value= 0,
max_value= 1600,
value=200,
step=10)
arrival_delay_in_minutes = st.number_input("arrival delay in minutes",
min_value= 0,
max_value= 1600,
value=200,
step=10)
st.markdown("---")
gender = st.radio("gender",("Male","Female"),index= 0)
customer_type = st.radio("customer type",("Loyal customer","disloyal customer"),index= 0)
type_of_travel = st.radio("type of travel",('Personal Travel', 'Business travel'),index= 0)
class_flight = st.radio("class flight",('Eco Plus', 'Business', 'Eco'),index= 0)
st.markdown("---")
inflight_wifi_service = st.radio("inflight_wifi_service",(0,1,2,3,4,5),index= 0)
departure_arrival_time_convenient = st.radio("departure/arrival_time_convenient",(0,1,2,3,4,5),index= 0)
ease_of_online_booking = st.radio("ease_of_online_booking",(0,1,2,3,4,5),index= 0)
gate_location = st.radio("gate_location",(0,1,2,3,4,5),index= 0)
food_and_drink = st.radio("food_and_drink",(0,1,2,3,4,5),index= 0)
online_boarding = st.radio("online_boarding",(0,1,2,3,4,5),index= 0)
seat_comfort = st.radio("seat_comfort",(0,1,2,3,4,5),index= 0)
inflight_entertainment = st.radio("inflight_entertainment",(0,1,2,3,4,5),index= 0)
on_board_service = st.radio("on_board_service",(0,1,2,3,4,5),index= 0)
leg_room_service = st.radio("leg_room_service",(0,1,2,3,4,5),index= 0)
baggage_handling = st.radio("baggage_handling",(0,1,2,3,4,5),index= 0)
checkin_service = st.radio("checkin_service",(0,1,2,3,4,5),index= 0)
inflight_service = st.radio("inflight_service",(0,1,2,3,4,5),index= 0)
cleanliness = st.radio("cleanliness",(0,1,2,3,4,5),index= 0)
st.markdown("---")
submitted = st.form_submit_button("predict")
data_inf = {
"gender" : gender,
"customer type" : customer_type,
"age" : age,
"type of travel" : type_of_travel,
"class" : class_flight,
"inflight wifi service" : inflight_wifi_service,
"departure/arrival time convenient" : departure_arrival_time_convenient,
"ease of online booking" : ease_of_online_booking,
"gate location" : gate_location,
"food and drink" : food_and_drink,
"online boarding" : online_boarding,
"seat comfort" : seat_comfort,
"inflight entertainment" : inflight_entertainment,
"on-board service" : on_board_service,
"leg room service" : leg_room_service,
"baggage handling" : baggage_handling,
"checkin service" : checkin_service,
"inflight service" : inflight_service,
"cleanliness" : cleanliness,
"flight distance" : flight_distance,
"departure delay in minutes" : departure_delay_in_minutes,
"arrival delay in minutes" : arrival_delay_in_minutes
}
data_inf = pd.DataFrame([data_inf])
st.dataframe(data_inf)
age_category = []
for x in data_inf["age"]:
if 6 <= x <= 21:
age_category.append('Generation z')
elif 22 <= x <= 36:
age_category.append('Millennials')
elif 37 <= x <= 52:
age_category.append('Generation X')
elif 53 <= x <= 73:
age_category.append('Baby Boomers')
else:
age_category.append('Silent Generation')
data_inf["generation"] = age_category
if submitted:
data_inf_num_skew = data_inf[list_num_skew_columns]
data_inf_cat_nom = data_inf[nom_col_skew]
data_inf_cat_ord = data_inf[ord_col_skew]
y_predict_inf = best_pipeline.predict(data_inf)
st.write("# Satisfaction: ", str(y_predict_inf[0]))
if __name__=="__main__":
run() |