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import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json
from PIL import Image
# load all files
with open('ab_model.pkl', 'rb') as file_1:
ab_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_diabetes'):
st.title('Prediction Page')
# sub header
st.subheader('We calculate your metrics to calculate diabetes')
# add pic
image = Image.open('diabetes2.png')
st.image(image)
st.write('Columns below are parameter we would like to use to predict if a patient have a diabetes or not.')
st.write('*`Please fill columns below to predict`*')
gender = st.selectbox('Gender', [0,1], help='0 = Female, 1 = Male')
age = st.number_input('Age', min_value=25, max_value=80,
value=45, step=1, help='Usia Pasien')
hypertension = st.number_input('Hypertension', min_value=0, max_value=1 , value=0,
step=1, help='have hypertension?')
heart_disease = st.number_input('Heart Disease', min_value=0, max_value=1 , value=0,
step=1, help='have heart disease?')
bmi = st.number_input('Body Mass Index', min_value=5, max_value=80,
value=30, step=5, help='Amount of BMI')
HbA1c_level = st.number_input('Hemogloblin Level', min_value= 3, max_value= 10,
value= 6, help='Level of Hemogloblin 3-10')
blood_glucose_level = st.slider('Glucose Level', 0, 400, 150, step=10,
help='Glucose amount in blood stream')
st.markdown('---')
submitted = st.form_submit_button('Predict')
data_inf = {
'age': age,
'bmi': bmi,
'hemoglobin_level': HbA1c_level,
'blood_glucose_level': blood_glucose_level,
'gender': gender,
'hypertension': hypertension,
'heart_disease': heart_disease,
}
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_sc, data_inf_nsc], axis= 1)
# modeling
y_pred_inf = ab_model.predict(data_final)
if y_pred_inf[0] == 1:
st.write('# **`Prediction: You Have Diabetes`**')
else:
st.write('# **`Prediction: You do not Have Diabetes`**')
if __name__ == '__main__':
run() |