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import streamlit as st
import pandas as pd
import numpy as np
import joblib, pickle
import json
#Load All Files
#load model dan files yang sudah di save pada framing
# with open('model_rf.pkl', 'rb') as file_1:
# model_rf = pickle.load(file_1)
# # model_patch = 'model_rf.pkl'
# model_rf = joblib.load(model_patch)
# with open('list_num_cols.txt', 'r') as file_1:
# list_num_cols = json.load(file_1)
# with open('list_cat_cols.txt', 'r') as file_2:
# list_cat_cols = json.load(file_2)
with open('model_rf.pkl', 'rb') as file3:
loaded_model = pickle.load(file3)
def run():
with st.form('deposito_simulation'):
#Field Umur
Age = st.number_input('Age', min_value = 17, max_value = 75, value = 25, step = 1, help = 'Customers Age')
#Field Nama
Job = st.selectbox('Job', ('admin.', 'technician', 'services','management','retired','blue-collar','unemployed','entrepreneur','housemaid','unknown','self-employed','student'), index = 1)
#Marital Status
Marital = st.selectbox('Marital Status', ('married', 'single', 'divorced'), index = 1)
#Education Status
Education = st.selectbox('Education', ('secondary', 'tertiary', 'primary', 'unknown'), index = 1)
#Default Status
Default = st.selectbox('Default', ('no', 'yes'), index = 1, help = 'has credit in default?')
#Field Pace Total
Balance = st.number_input('Balance', min_value = 0, max_value=999999, value = 50)
#Housing Status
Housing = st.selectbox('Housing', ('yes', 'no'), index = 1, help = 'has housing loan?')
#Loan Status
Loan = st.selectbox('Loan', ('no', 'yes'), index = 1, help = 'has personal loan?')
#Contact Status
Contact = st.selectbox('Contact', ('unknown', 'cellular', 'telephone'), index = 1)
#Day
Day = st.number_input('Day', min_value = 1, max_value=31, value = 1, help = 'last contact day of the month')
#Month
Month = st.selectbox('Month', ('may', 'jun', 'jul', 'aug', 'oct', 'nov', 'dec', 'jan', 'feb', 'mar', 'apr', 'sep'), index = 1, help = 'last contact month of year')
#Duration
Duration = st.number_input('Duration', min_value = 0, max_value=999999, value = 50, help = 'last contact duration, in seconds')
#Day
Campaign = st.number_input('Campaign', min_value = 1, max_value=100, value = 1, help = 'number of contacts performed during this campaign and for this client')
#pDyas
Pdays = st.number_input('Pdays', min_value = -1 , max_value=9999999, value = 0, help = 'number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted')
#Previous
Previous = st.number_input('Previous', min_value = 0, max_value=100, value = 0, help = 'previous: number of contacts performed before this campaign and for this client')
#Poutcome Status
Poutcome = st.selectbox('P Outcome', ('unknown', 'other', 'failure', 'success'), index = 1, help = 'outcome of the previous marketing campaign')
#bikin submit button
submitted = st.form_submit_button('Predict')
#Inference
data_inf = {
'age' : Age,
'job' : Job,
'marital' : Marital,
'education' : Education,
'default' : Default,
'balance' : Balance,
'housing' :Housing,
'loan': Loan,
'contact' : Contact,
'day' :Day,
'month' :Month,
'duration':Duration,
'campaign': Campaign,
'pdays':Pdays,
'previous':Previous,
'poutcome': Poutcome,
}
data_inf = pd.DataFrame([data_inf])
st.dataframe(data_inf)
#Logic ketika predic button ditekan
if submitted:
# Predict using loaded model
predictions = loaded_model.predict(data_inf)
# Menampilkan data input
st.write('## Data Input:')
st.dataframe(data_inf)
# Menampilkan hasil prediksi
st.write('## Hasil Prediksi:')
st.write('Deposit:', int(predictions[0]))
# Menampilkan pesan berdasarkan hasil prediksi
if predictions[0] == 0:
st.write("Nasabah belum berlangganan deposito berjangka.")
else:
st.write("Nasabah telah berlangganan deposito berjangka.")
if __name__ == '__main__':
run() |