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lightgbm.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4b7d22e69f268fb748603b825ed4b52a82665babcf684254dc74ee646e9f58e0
+size 407716

main.py

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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()