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()