Spaces:
Runtime error
Runtime error
File size: 2,100 Bytes
061961e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
import streamlit as st
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle
import json
# Load All Files
with open('gbc_model.pkl', 'rb') as file_1:
gbc_model = pickle.load(file_1)
with open('scaler.pkl', 'rb') as file_2:
scaler = pickle.load(file_2)
# bikin fungsi
def run():
with st.form(key='patient_condition'):
anemia = st.selectbox('Anemia', (0, 1), index=1)
diabetes = st.selectbox('Diabetes', (0, 1), index=1)
high_blood_pressure = st.selectbox('High blood pressure', (0, 1), index=1)
serum_sodium = st.number_input('Serum Sodium', min_value=100, max_value=150, value=100)
sex = st.selectbox('Sex', (0, 1), index=1)
smoking = st.selectbox('Sex', (0, 1), index=1)
time = st.number_input('Time', min_value=100, max_value=150, value=100)
binned_age = st.selectbox('Range of age', (1, 2,3,4), index=1)
binned_creat_serum = st.selectbox('High serum creatinine', (1, 2), index=1)
binned_cpk = st.selectbox('CPK Value', (1,2,3), index=1)
binned_ejection = st.selectbox(' Ejection frraction', (1,2,3), index=1)
binned_cpk = st.selectbox('CPK Value', (1,2,3), index=1)
binned_platelets = st.selectbox('Platelets count', (0, 1, 2), index=1)
st.markdown('---')
submitted = st.form_submit_button('Predict')
data_inf = {
'anemia': 1,
'diabetes': 1,
'high_blood_pressure' : 1,
'serum_sodium': 130,
'sex': 1,
'smoking': 1,
'time': 150,
'binned_age': 3,
'binned_creat_serum' : 2,
'binned_cpk' : 2,
'binned_ejection' : 2,
'binned_platelets': 2
}
data_inf = pd.DataFrame([data_inf])
st.dataframe(data_inf)
if submitted:
# Feature Scaling and Feature Encoding
col_headers = list(data_inf.columns.values)
data_inf_scaled = scaler.transform(data_inf)
data_inf_df = pd.DataFrame(data_inf_scaled, columns=col_headers)
# Predict using gbc
y_pred_inf = gbc_model.predict(data_inf_df)
st.write('# Rating: ', str(int(y_pred_inf)))
if __name__ == '__main__':
run() |