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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 pipe rf model | |
| predictions = loaded_model.predict(data_inf) | |
| st.write('## Deposit : ', str(int(predictions))) | |
| if __name__ == '__main__': | |
| run() |