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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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import pickle |
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import json |
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from joblib import load |
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loaded_model_RF = load('model_RF.joblib') |
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loaded_model_boost = load('model_boost.joblib') |
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with open('col_penting.txt', 'r') as file_1: |
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col = json.load(file_1) |
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def run(): |
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with st.form('key=form_Heart_failure_prediction'): |
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age = st.number_input('Age', min_value=40, max_value=100, value=45, step=1, help='Usia Pasien') |
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anaemia = st.radio('anaemia', (0,1), index=0,help='0 = No, 1 = Yes') |
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creatinine_phosphokinase = st.slider('creatinine phosphokinase', 20, 1500, 200) |
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diabetes = st.radio('diabetes', (0,1), index=1,help='0 = No, 1 = Yes') |
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st.markdown('---') |
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ejection_fraction = st.slider('ejection_fraction', 14, 80, 25) |
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high_blood_pressure = st.radio('high blood pressure', (0,1), index=0,help='0 = No, 1 = Yes') |
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platelets = st.slider('platelets', 25000, 850000, 100000) |
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serum_creatinine = st.number_input('serum creatinine', min_value=0.5, max_value=2.5, value=0.75, step=0.05) |
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serum_sodium = st.number_input('serum sodium', min_value=110, max_value=150, value=125, step=1) |
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sex = st.selectbox('sex', (0,1), index=1,help='0 = Female, 1 = Male') |
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smoking = st.selectbox('smoking', (0,1), index=0,help='0 = No, 1 = Yes') |
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time = st.slider('time', 3, 300, 30) |
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model = st.radio('pilih jenis model yang akan digunakan untuk prediksi :',('Random Forest', 'AdaBoost'),index=1) |
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submitted = st.form_submit_button('Predict') |
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data_inf = { |
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'age': age, |
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'anaemia': anaemia, |
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'creatinine_phosphokinase': creatinine_phosphokinase, |
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'diabetes': diabetes, |
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'ejection_fraction': ejection_fraction, |
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'high_blood_pressure': high_blood_pressure, |
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'platelets': platelets, |
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'serum_creatinine': serum_creatinine, |
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'serum_sodium': serum_sodium, |
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'sex': sex, |
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'smoking': smoking, |
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'time': time |
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} |
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data_inf = pd.DataFrame([data_inf]) |
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inf_new = data_inf[col] |
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if model == 'AdaBoost': |
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if submitted: |
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y_pred_inf = loaded_model_boost.predict(inf_new) |
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st.write('# Death : ', str(int(y_pred_inf))) |
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else : |
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if submitted: |
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y_pred_inf = loaded_model_RF.predict(inf_new) |
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st.write('# Death : ', str(int(y_pred_inf))) |
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if __name__ == '__main__': |
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run() |
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