import streamlit as st import pandas as pd import pickle from PIL import Image def run(): # load model with open('model.pkl', 'rb') as model: model = pickle.load(model) age = st.number_input('Age', min_value=18, max_value=100) job = st.selectbox('Job', options=['technician', 'management', 'retired', 'student', 'unemployed', 'admin', 'services', 'blue-collar', 'enterpreneur', 'housemaid', 'unknown', 'self-employed']), marital = st.selectbox('Marital status', options=['married', 'divorced', 'single']), education = st.selectbox('Education', options=['primary', 'tertiary', 'secondary', 'unknown']), default = st.selectbox('Default status',options=['yes', 'no']), balance = st.number_input('Balance', min_value=0.0, max_value=80000.0), housing = st.selectbox('Has housing loans ?',options=['no', 'yes']), loan = st.selectbox('Has personal loan ?',options=['no', 'yes']), contact = st.selectbox('Contacted by',options=['unknown', 'cellular', 'cellular', 'telephone', 'cellular']), day = st.number_input('Day',min_value=1, max_value=31), month = st.selectbox('Month', options=['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']), duration = st.number_input('Call duration (sec)',min_value=2, max_value=3881), campaign = st.number_input('How many campaign that have been conducted ?',min_value=1, max_value=63), pdays = st.number_input('Number of days passed',min_value=0, max_value=854), previous = st.number_input('Number of contacts performed before',min_value=0, max_value=58), poutcome = st.selectbox('Previous result',options=['unknown', 'success','other', 'failure']) st.markdown('**Berikut adalah data yang telah kamu input :**') dataInf = pd.DataFrame({ '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 }, index=[0]) st.table(dataInf) if st.button(label='Predict'): # data dummy prediction yPred_inf = model.predict(dataInf) # result of prediction if yPred_inf[0] == 0: st.write('Bukan client potensial') else: st.write('Client potensial')