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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 == 1: | |
| st.error("Positive. Diabetes Diagnosed.") | |
| else: | |
| st.success("Negative. Diabetes not diagnosed.") | |
| if __name__ == "__main__": | |
| run() | |