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import pickle
import json
import pandas as pd
import numpy as np
import streamlit as st
# Load All Files
with open('best_param.pkl', 'rb') as file_1:
best_params = pickle.load(file_1)
with open('preprocessing_pipeline.pkl', 'rb') as file_2:
preprocessing_pipeline= pickle.load(file_2)
def run ():
with st.form(key ='PREDICT VISITORS FORM'): #Nulis nama sendiri menggunakan name= st.text_input('')
Booking_ID= st.text_input('Booking_ID', 'Input ID Here')
no_of_adults = st.number_input('Number of Adults')
no_of_children= st.number_input('Number of Children')
no_of_weekend_nights= st.number_input('Number of Weekend Nights', min_value=0, max_value=7)
no_of_week_nights= st.number_input('Number of Week Nights', min_value=0, max_value=17)
type_of_meal_plan= st.selectbox(
'Choose your Meal Plan',
('Meal Plan 1', 'Not Selected', 'Meal Plan 2', 'Meal Plan 3'))
required_car_parking_space= st.number_input('Required Car Parking Space')
room_type_reserved= st.selectbox(
'Choose your Room Type',
('Room_Type 1', 'Room_Type 4', 'Room_Type 2', 'Room_Type 6',
'Room_Type 5', 'Room_Type 7', 'Room_Type 3'))
lead_time= st.number_input('The number of days between booking and arrival')
arrival_year= st.number_input('The year of arrival')
arrival_month= st.number_input('The month of arrival', min_value=1, max_value=12)
arrival_date= st.number_input('The date of arrival', min_value=1, max_value=31)
market_segment_type= st.selectbox(
'What Segment Type of Customer',
('Offline', 'Online', 'Corporate', 'Aviation', 'Complementary'))
repeated_guest= st.number_input('Repeated Guest')
no_of_previous_cancellations= st.number_input('The number of previous cancellations by the guest')
no_of_previous_bookings_not_canceled= st.number_input('The number of previous bookings not canceled by the guest')
avg_price_per_room= st.number_input('The average price per room')
no_of_special_requests= st.number_input('The number of special requests made by the guest', min_value=0, max_value=5)
submitted = st.form_submit_button('Predict')
# Create New Data
df_inf={
'Booking_ID': Booking_ID,
'no_of_adults': no_of_adults,
'no_of_children': no_of_children,
'no_of_weekend_nights':no_of_weekend_nights,
'no_of_week_nights':no_of_week_nights,
'type_of_meal_plan':type_of_meal_plan,
'required_car_parking_space': required_car_parking_space,
'room_type_reserved':room_type_reserved,
'lead_time':lead_time,
'arrival_year': arrival_year,
'arrival_month':arrival_month,
'arrival_date':arrival_date,
'market_segment_type':market_segment_type,
'repeated_guest':repeated_guest,
'no_of_previous_cancellations':no_of_previous_cancellations,
'no_of_previous_bookings_not_canceled':no_of_previous_bookings_not_canceled,
'avg_price_per_room':avg_price_per_room,
'no_of_special_requests':no_of_special_requests,
}
df_inf = pd.DataFrame([df_inf])
if submitted:
prediction = best_params.predict(df_inf)
st.write('This Visitor Predicted:', round(prediction[0],2))
# df_inf_best_params = df_inf[best_params]
# df_inf_classifier= df_inf[preprocessing_pipeline]
# df_inf_final = np.concatenate([preprocessing_pipeline], axis=1)
# y_pred_inf = best_params.predict(df_inf_final)
# st.write(f'# Rating {best_params}:', int(y_pred_inf))
if best_params == '__main__':
run() |