app.py

import streamlit as st
import pandas as pd
import numpy as np
import joblib

with open('model_encode1.pkl', 'rb') as file_1:
  encode1 = joblib.load(file_1)
  
with open('model_encode2.pkl', 'rb') as file_2:
  encode2 = joblib.load(file_2)
  
with open('model_scale.pkl', 'rb') as file_3:
  scale = joblib.load(file_3)
  
with open('model.pkl', 'rb') as file_4:
  model = joblib.load(file_4)
  
with open('feature_num_col.pkl', 'rb') as file_5:
  num_col = joblib.load(file_5)
  
with open('feature_cat_nom.pkl', 'rb') as file_6:
  cat_nom = joblib.load(file_6)
  
with open('feature_cat_ord.pkl', 'rb') as file_7:
  cat_ord = joblib.load(file_7)


hour = st.slider('Masukan Jam:',0, 24,step=1)
distance = st.number_input('Masukan Jarak dalam mile:')
platform = st.radio('Lyft/Uber:',('Lyft', 'Uber'))
service = st.selectbox('Masukan Jenis Layanan: ',('Shared', 'Lux', 'UberPool', 'Lyft XL', 'Black', 'Lyft', 'UberXL',
                      'UberX', 'WAV', 'Lux Black', 'Black SUV', 'Lux Black XL'))
destination = st.selectbox('Masukan Tujuan:',('North Station', 'Fenway', 'West End', 'Back Bay',
                          'Haymarket Square', 'Theatre District', 'South Station',
                          'Northeastern University', 'North End', 'Financial District',
                          'Beacon Hill', 'Boston University'))
weather = st.selectbox('Masukan Cuaca Sekarang: ',(' Drizzle ', ' Clear ', ' Overcast ', ' Possible Drizzle ',
                      ' Mostly Cloudy ', ' Partly Cloudy ', ' Rain ', ' Light Rain ',
                      ' Foggy '))

if st.button('Predict'):
    data_inf = pd.DataFrame({'hour' : hour, 'distance' : distance, 'platform' : platform, 'service' : service, 'destination' : destination, 'weather' : weather},index=[0])
    
    data_inf_scaled = scale.transform(data_inf[num_col])
    data_inf_encoded1 = encode1.transform(data_inf[cat_nom])
    data_inf_encoded2 = np.array([encode2.transform(data_inf[cat_ord])]).T

    data_inf_fix = np.concatenate([data_inf_scaled, data_inf_encoded1, data_inf_encoded2], axis=1)

    hasil = model.predict(data_inf_fix)[0]
    st.header(f'Harga Uber= ${hasil}')