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import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json
# load all files
with open('cat_model.pkl', 'rb') as file_1:
cat_model = pickle.load(file_1)
# Pre-processing
with open('scale_feat.pkl', 'rb') as file_2:
scale_feat = pickle.load(file_2)
with open('winsoriser.pkl', 'rb') as file_3:
winsoriser = pickle.load(file_3)
# List Numeric & Category
with open('num_cols_sc.txt', 'r') as file_4:
num_cols_sc = json.load(file_4)
with open('num_cols_nsc.txt', 'r') as file_5:
num_cols_nsc = json.load(file_5)
def run():
with st.form(key='from_health'):
age = st.number_input('Usia', min_value=25, max_value=95,
value=40, step=1, help='Usia Pasien')
anaemia = st.number_input('Anemia', min_value=0, max_value=1,
value=0, step=1, help='Terindikasi Anemia 0 = Tidak, 1 = Ya')
creatinine_phosphokinase = st.slider('Creatine Value', 0, 10_000, 1500, step=20, help='Kadar Creatinine')
diabetes = st.number_input('Diabetes', min_value=0, max_value=1,
value=0, step=1, help='Terindikasi Diabetes 0 = Tidak, 1 = Ya')
ejection_fraction = st.number_input('Ejection Value', min_value=0, max_value=100,
value=50, step=1, help='Kadar Ejection')
high_blood_pressure = st.number_input('Tekanan Darah Tinggi', min_value=0, max_value=1,
value=0, step=1, help='Terindikasi Tekanan Darah Tinggi 0 = Tidak, 1 = Ya')
platelets = st.slider('Platelets', 0, 850_000, 50_000, step=100, help='Kadar Platelets')
serum_creatinine = st.number_input('Serum Creatinine', min_value=0, max_value=10,
value=5, step=1, help='Kadar Serum_Creatinine')
serum_sodium = st.number_input('Serum Sodium', min_value=0, max_value=150,
value=50, step=1, help='Kadar Serum_Sodium')
sex = st.number_input('Jenis Kelamin', min_value=0, max_value=1,
value=0, step=1, help='Kode jenis kelamin 0 = wanita , 1 = pria')
smoking = st.number_input('Merokok', min_value=0, max_value=1,
value=0, step=1, help='Terindikasi Merokok 0 = Tidak, 1 = Ya')
time = st.number_input('Waktu', min_value=0, max_value=300,
value=10, step=1, help='Waktu berkunjung kembali')
st.markdown('---')
submitted = st.form_submit_button('Predict')
data_inf = {
'age': age,
'anemia': anaemia,
'creatinine_phosphokinase': creatinine_phosphokinase,
'diabetes': diabetes,
'ejection_fraction': ejection_fraction,
'high_blood_pressure': high_blood_pressure,
'platelets': platelets,
'serum_creatinine': serum_creatinine,
'serum_sodium': serum_sodium,
'sex': sex,
'smoking': smoking,
'time': time,
}
st.markdown('Jika Hasil Prediksi = 1, maka model memprediksi bahwa pasien akan meninggal.')
st.markdown('Jika Hasil Prediksi = 0, maka model memprediksi bahwa pasien akan selamat.')
data_inf = pd.DataFrame([data_inf])
st.dataframe(data_inf)
if submitted:
data_inf_sc = data_inf[num_cols_sc]
data_inf_nsc = data_inf[num_cols_nsc]
# scalling
data_inf_sc = scale_feat.transform(data_inf_sc)
data_inf_sc = pd.DataFrame(data_inf_sc, columns=num_cols_sc)
# Reset Index
data_inf_sc.reset_index(drop= True, inplace= True)
data_inf_nsc.reset_index(drop = True, inplace = True)
data_final = pd.concat([data_inf_nsc, data_inf_sc], axis= 1)
# modeling
y_pred_inf = cat_model.predict(data_final)
st.write('Prediction: ', (y_pred_inf))
if __name__ == '__main__':
run() |