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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('model_rf.pkl', 'rb') as file_1:
model_rf = pickle.load(file_1)
with open('model_scaler.pkl', 'rb') as file_3:
model_scaler = pickle.load(file_3)
with open('model_encoder.pkl','rb') as file_4:
model_encoder = pickle.load(file_4)
with open('list_num_cols.txt', 'r') as file_5:
list_num_cols = json.load(file_5)
with open('list_cat_cols.txt', 'r') as file_6:
list_cat_cols = json.load(file_6)
def run():
st.title('Heart Failure Prediction')
with st.form(key='form_heart_failure'):
age = st.number_input('Age', min_value=1, max_value=100, value=25, step=1, help='in Years Old')
anemia = st.selectbox('Do you have Anemia?', ('Yes', 'No'))
cpk = st.number_input('Level of CPK Enzyme', min_value=10, max_value=8000, value=120, help='in mcg/L, 10-120 considered normal')
diabetes = st.selectbox('Do you have Diabetes?', ('Yes','No'))
ejection_fraction = st.slider('Ejection Fraction', 0,100,50, help='in Percentage (%)')
hypertension = st.selectbox('Do you have Hypertension?', ('Yes','No'))
platelets = st.number_input('Platelets / Trombosit per mL of Blood', min_value=1000, max_value=800000, value=150000,help='150k - 450k is considered normal')
serum_creatinine = st.number_input('Serum Creatinine Level', min_value=.1, max_value=10., value=1.,step=.1, help='in mg/dL, 0.6 - 1.2 is considered normal')
serum_sodium = st.number_input('Serum Sodium Level', min_value=100, max_value=200, value=150, help='in mEq/L, 135 - 145 considered to be normal')
sex = st.selectbox('Are you a male or female?', ('Male','Female'))
smoking = st.selectbox('Do you smoke?', ('Yes','No'))
time = st.number_input('How many days since receiving medical treatment?', min_value=1, max_value=365, value=1)
submitted = st.form_submit_button('Predict')
data_inf = {
'age': age,
'anemia': anemia,
'cpk': cpk,
'diabetes': diabetes,
'ejection_fraction' : ejection_fraction,
'hypertension': hypertension,
'platelets': platelets,
'serum_creatinine': serum_creatinine,
'serum_sodium': serum_sodium,
'sex': sex,
'smoking': smoking,
'time': time
}
data_inf = pd.DataFrame([data_inf])
st.dataframe(data_inf)
data_inf['anemia'] = data_inf['anemia'].replace({'Yes': 1, 'No': 0})
data_inf['diabetes'] = data_inf['diabetes'].replace({'Yes': 1, 'No': 0})
data_inf['hypertension'] = data_inf['hypertension'].replace({'Yes': 1, 'No': 0})
data_inf['smoking'] = data_inf['smoking'].replace({'Yes': 1, 'No': 0})
data_inf['sex'] = data_inf['sex'].replace({'Male': 1, 'Female': 0})
if submitted:
# Split between Numerical Columns and Categorical Columns
data_inf_num = data_inf[list_num_cols]
data_inf_cat = data_inf[list_cat_cols]
# Feature Scaling and Feature Encoding
data_inf_num_scaled = model_scaler.transform(data_inf_num)
data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)
# Predict using Model
y_pred_inf = model_rf.predict(data_inf_final)
st.write('Hasil prediksi Model : ', y_pred_inf)
'''
Hasil Prediksi 0 = Alive, 1 = Dead
'''
if __name__ == '__main__':
run() |