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# importing python modules.
import streamlit as st
import joblib
import numpy as np
import time
# loading pickle files gotten from model
lightgbm_pickle = open(r"./lightgbm.pickle", "rb")
lgbm_model = joblib.load(lightgbm_pickle)
# column name for each column in the diabetes dataset.
column_names = ['cholesterol', 'glucose', 'hdl_chol', 'chol_hdl_ratio', 'age',
'gender', 'weight', 'height', 'bmi', 'systolic_bp', 'diastolic_bp', 'waist', 'hip',
'waist_hip_ratio', 'diabetes']
# function to receive user information.
def inputs():
# creating form for data inputs.
with st.form(key="diabetes_data"):
name = st.text_input("Patient's Name: ")
gender_obj = st.selectbox(label="Patient's Gender: ", options=["Male", "Female"])
if gender_obj == "Male":
gender = 1
else:
gender = 0
age = st.slider(label="Patient's Age: ", min_value=0, max_value=100)
chol = st.slider(label="Patient's Cholesterol Level(mg/dL): ", min_value=40, max_value=400)
glucose = st.slider(label="Patient's Sugar Level(mg/dL): ", min_value=40, max_value=250)
height_cm = st.number_input(label="Patient's Height(cm): ")
height = height_cm * 0.393701
weight_kg = st.number_input("Patient's Weight in(kg): ")
weight = weight_kg * 2.205
hdl_chol = st.slider(label="Patient's HDL Cholesterol(mg/dL): ", min_value=0, max_value=100)
waist = st.number_input("Patient's Waist Size(inches): ", step=1)
hip = st.number_input("Patient's Hip Size(inches): ", step=1)
systolic_bp = st.number_input(label="Patient's Systolic Blood Pressure(mmHg): ", step=1)
diastolic_bp = st.number_input(label="Patient's Diastolic Blood Pressure(mmHg): ", step=1)
submit = st.form_submit_button("Submit Test")
if submit:
bmi = weight_kg / ((height_cm / 100)**2)
chol_hdl_ratio = chol / hdl_chol
waist_hip_ratio = waist / hip
patient_data = [chol, glucose, hdl_chol, chol_hdl_ratio, age, gender, weight, height, bmi,
systolic_bp, diastolic_bp, waist, hip, waist_hip_ratio]
else:
patient_data = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
return patient_data
# function to create a dataframe and carry out prediction.
def predict(var_name):
pred = [var_name]
np_pred = np.array(pred)
score = lgbm_model.predict(np_pred)
return score
# function to run streamlit app
def run():
st.title("Diabetes Test App")
st.write("Diabetes is known as a very deadly disease if not diagnosed early. To make it easier for health "
"practitioners to diagnose this disease early, previous data have been accumulated to predict an accurate "
"result for new patients. "
"The Doctor is to retrieve necessary information from the patients to carry out this test."
" A diabetic patient should be notified early and should commence treatment immediately.")
info = inputs()
dia_score = predict(info)
with st.spinner(text="Diagnosing....."):
time.sleep(5)
if dia_score == 0:
st.error("Positive. Diabetes Diagnosed.")
else:
st.success("Negative. Diabetes not diagnosed.")
if __name__ == "__main__":
run()
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