import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json

# load semua file yang telah disimpan

with open('model_rfc.pkl', 'rb') as file_1:
  rfc = pickle.load(file_1)

with open('model_scaler.pkl', 'rb') as file_2:
  scaler = pickle.load(file_2)

with open('model_encoder.pkl', 'rb') as file_3:
  encoder = pickle.load(file_3)

with open('list_num_cols.txt', 'r') as file_4:
  list_num_cols = json.load(file_4)

with open('list_cat_cols.txt', 'r') as file_5:
  list_cat_cols = json.load(file_5)

def run():
    # membuat judul
    st.title('Booking Status Prediction')

    with st.form(key='Booking Status'):
        booking_id = st.text_input('Booking ID', value='')
        no_of_adults = st.number_input('Number of Adults', min_value=0, max_value=4, value=0, step=1)
        no_of_children = st.number_input('Number of Children', min_value=0, max_value=10, value=0, step=1)
        no_of_weekend_nights = st.number_input('Number of Weekend Nights',min_value=0, max_value=7, value=0, step=1 )
        no_of_week_nights = st.number_input('Numer of Week Nights', min_value=0, max_value=17, value=0, step=1)
        type_of_meal_plan = st.selectbox('Type of Meal Plan',('Meal Plan 1','Not Selected','Meal Plan 2','Meal Plan 3'),index=1)
        required_car_parking_space = st.number_input('Required Car Parking Space', min_value=0, max_value=1, value=0, step=1)
        room_type_reserved = st.selectbox('Room Type Reserved',('Room_Type 1','Room_Type 2','Room_Type 3','Room_Type 4','Room_Type 5','Room_Type 6'),index=1)
        lead_time = st.number_input('Lead Time', min_value=0, max_value=500, value=0, step=1)
        arrival_year = st.number_input('Arrival Year', min_value=2017, max_value=2025, value=2017, step=1)
        arrival_month = st.number_input('Arrival Month', min_value=1, max_value=12, value=1, step=1)
        arrival_date= st.number_input('Arrival Date', min_value=1, max_value=31, value=26, step=1)
        market_segment_type = st.selectbox('Market Segment Type', ('Online','Aviation','Corporate','Offline'),index=1)
        repeated_guest= st.number_input('Repeated Guest', min_value=0, max_value=1, value=0, step=1)
        no_of_previous_cancellations= st.number_input('Number of Previous Cancellations', min_value=0, max_value=13, value=0, step=1)
        no_of_previous_bookings_not_canceled= st.number_input('Number of Previous Nookings Not Canceled', min_value=0, max_value=58, value=0, step=1)
        avg_price_per_room= st.number_input('Avarage Price Per Room', min_value=0, max_value=540, value=50, step=1)
        no_of_special_requests = st.number_input('Number of Special Requests', min_value=0, max_value=5, value=0, step=1)

        submitted = st.form_submit_button('Predict')

    # membuat data-set baru

    data_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 }


    data_inf = pd.DataFrame([data_inf])
    st.dataframe(data_inf)

    if submitted:
        # Split between Numerical Columns and Categorical Columns
        data_inf_num = data_inf[list_num_cols]
        data_inf_cat = data_inf[list_cat_cols]

        # Feature Scaling and Feature Encoding
        data_inf_num_scaled = scaler.transform(data_inf_num)
        data_inf_cat_encoded = encoder.transform(data_inf_cat)
        data_inf_final = np.concatenate((data_inf_num_scaled, data_inf_cat_encoded.toarray()), axis=1)

        # Predict
        y_pred_inf = rfc.predict(data_inf_final)
        prediction = 'Not Cancel' if y_pred_inf == 0 else 'Cancel'

        st.write('# Prediction: ', prediction)
        st.write('If the prediction shows "Cancel", there is a possibility that the booking will be canceled by the customer.')

if __name__ == '__main__': run()