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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('random_forest_model.pkl', 'rb') as file_2:
loaded_model = pickle.load(file_2)
with open('model_scaler.pkl', 'rb') as file_2:
model_scaler = pickle.load(file_2)
# Load the list of numerical columns
with open('list_num_cols.txt', 'r') as file_3:
list_num_cols = json.load(file_3)
# Load the list of categorical columns
with open('list_cat_cols.txt', 'r') as file_4:
list_cat_cols = json.load(file_4)
def run():
with st.form(key='from_heart_failure'):
Age = st.number_input('age', min_value=16, max_value=50, value=25, step=1, help='Usia Prediksi')
Anaemia = st.selectbox('anaemia', (1, 0), index=1, help='0 for NO 1 for YES')
Creatinine = st.slider('creatinine_phosphokinase', min_value=0, max_value=1000, value=78)
Diabetes = st.selectbox('diabetes', (1, 0), index=1, help='0 for NO 1 for YES')
Ejection = st.slider('ejection_fraction', min_value=0, max_value=100, value=80)
BloodPleasure = st.selectbox('high_blood_pressure', (1, 0), index=1, help='0 for NO 1 for YES')
st.markdown('---')
Platelets = st.number_input('platelets', min_value=100000, max_value=500000, value=250000)
SerumCreatinine = st.number_input('serum_creatinine', min_value=0.1, max_value=5.0, value=1.7)
Serum_Sodium = st.number_input('serum_sodium', min_value=100, max_value=160, value=137)
st.markdown('---')
Gender = st.selectbox('sex', (1, 0), index=1, help='0 for Male 1 for Famele')
Smoking = st.selectbox('smoking', (1, 0), index=1, help='0 for No 1 for Yes')
Time = st.number_input('time', min_value=10, max_value=300, value=197)
submitted = st.form_submit_button('Prediction')
data_inf = {
'age': Age,
'anaemia': Anaemia,
'creatinine_phosphokinase': Creatinine,
'diabetes': Diabetes,
'ejection_fraction': Ejection,
'high_blood_pressure': BloodPleasure,
'platelets': Platelets,
'serum_creatinine': SerumCreatinine,
'serum_sodium': Serum_Sodium,
'sex': Gender,
'smoking': Smoking,
'time': Time
}
data_inf = pd.DataFrame([data_inf])
st.dataframe(data_inf)
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_final = np.concatenate([data_inf_num_scaled], axis=1)
# Prediksi menggunakan random forest
y_pred_inf = loaded_model.predict(data_inf_final)
# Mengubah hasil prediksi menjadi label yang sesuai
if y_pred_inf == 0:
hasil_prediksi = "Ada Harapan"
else:
hasil_prediksi = "Death"
st.write('# Kemungkinan : ', hasil_prediksi)
if __name__== '__main__':
run() |