import streamlit as st
import pandas as pd
import pickle



# Load All Files

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




# bikin fungsi  
def run():

  with st.form(key='Reserved_data'):
      no_of_adults = st.selectbox('adult(s)', (1,2,3,4,5,6,7,8), index=1, help='How many adults will stay')
      no_of_children = st.selectbox('children', (0,1,2,3,4), index=0, help='How many adults will stay')
      no_of_weekend_nights = st.selectbox('Weekend Night', (0,1,2,3,4,5,6,7,8,9,10), index=1, help='How many Night will stay at weekend')
      no_of_week_nights = st.number_input('Weekdays Night', min_value=0, max_value=20, value=1, help='How many Night will stay at weekdays')
      type_of_meal_plan = st.selectbox('Type of Meal', (1,2,3,4), index=1, help='1 = type 1 \n 2 = type 2 \n 3 = type 3 \n 4 = Not Selected')
      required_car_parking_space = st.radio('Car Parking', (0, 1), index=1, help='0 = No \n 1 = Yes')
      room_type_reserved = st.selectbox('Room Type', (1,2,3,4,5,6,7), index=1)
      arrival_month  = st.selectbox('Arrival Month', (1,2,3,4,5,6,7,8,9,10,11,12), index=1)
      market_segment_type =  st.radio('Source order', (1, 2, 3, 4, 5), index=1, help='1=online \n 2=offline \n 3=corporate \n 4=complementary \n 5=aviation')
      no_of_previous_cancellations = st.selectbox('History Cancel', (0,1,2,3,4,5,6,7,8,9,10), index=0)
      no_of_special_requests = st.selectbox(' Special Request', (0,1,2,3,4,5), index=0)
      binned_lead_time = st.radio('Lead time', (1, 2, 3, 4, 5,6), index=1, help='1= <3 days \n 2= 3-7 days \n 3= 7-14 days \n 4= 14-30 days \n 5=30-90 days \n 6= >90 days')
      binned_no_cancel = st.radio('History not Cancel', (0,1), index=1)
      binned_price = st.radio('Type of Price', (1, 2, 3), index=1, help='1 = low \n 2 = medium \n 3 = high')
      st.markdown('---')

      submitted = st.form_submit_button('Predict')

  data_inf = {
    '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,
    'arrival_month': arrival_month,
    'market_segment_type' : market_segment_type,
    'no_of_previous_cancellations' : no_of_previous_cancellations,
    'no_of_special_requests' : no_of_special_requests,
    'binned_lead_time' : binned_lead_time,
    'binned_no_cancel' : binned_no_cancel,
    'binned_price': binned_price
  }

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

  if submitted:

      # Predict using grid_rfc
      y_pred_inf = grid_rfc_best.predict(data_inf)
      st.write('# Rating: ', str(int(y_pred_inf)))
      
if __name__ == '__main__':
    run()