app.py

import pickle
import pandas as pd
import shap
from shap.plots._force_matplotlib import draw_additive_plot
import gradio as gr
import numpy as np
import matplotlib.pyplot as plt

# load the model from disk
loaded_model = pickle.load(open("db_xgb.pkl", 'rb'))

# Setup SHAP
explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.

# Define mapping functions
def map_HighBP(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]
    
def map_HighChol(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]

def map_CholCheck(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]

def map_Smoker(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]

def map_Stroke(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]

def map_HeartDiseaseorAttack(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]

def map_PhysActivity(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]
    
def map_Fruits(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]
    
def map_Veggies(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]

def map_HvyAlcoholConsump(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]

def map_AnyHealthcare(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]

def map_NoDocbcCost(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]

def map_DiffWalk(value):
    mapping = {'No': 0, 'Yes': 1}
    return mapping[value]

def map_Sex(value):
    mapping = {'Female': 0, 'Male': 1}
    return mapping[value]

def map_Education(value):
    mapping = {
        "Never attended school": 1,
        "Grades 1-8": 2,
        "Grades 9-11": 3,
        "Grade 12 or GED": 4,
        "College 1-3 years": 5,
        "College 4+ years": 6
    }
    return mapping[value]

def map_Income(value):
    mapping = {
        "> $10,000": 1,
        "> $20,000": 2,
        "> $25,000": 3,
        "> $30,000": 4,
        "> $35,000": 5,
        "> $50,000": 6,
        "> $60,000": 7,
        "< $75,000": 8
    }
    return mapping[value]

# Create the main function for server
def main_func(HighBP, HighChol, CholCheck, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income):
    new_row = pd.DataFrame.from_dict({        
        'HighBP': map_HighBP(HighBP),
        'HighChol': map_HighChol(HighChol),
        'CholCheck': map_CholCheck(CholCheck),
        'BMI': BMI,
        'Smoker': map_Smoker(Smoker),
        'Stroke': map_Stroke(Stroke),
        'HeartDiseaseorAttack': map_HeartDiseaseorAttack(HeartDiseaseorAttack),
        'PhysActivity': map_PhysActivity(PhysActivity),
        'Fruits': map_Fruits(Fruits),
        'Veggies': map_Veggies(Veggies),
        'HvyAlcoholConsump': map_HvyAlcoholConsump(HvyAlcoholConsump),
        'AnyHealthcare': map_AnyHealthcare(AnyHealthcare),
        'NoDocbcCost': map_NoDocbcCost(NoDocbcCost),
        'GenHlth': GenHlth,
        'MentHlth': MentHlth,
        'PhysHlth': PhysHlth,
        'DiffWalk': map_DiffWalk(DiffWalk),
        'Sex': map_Sex(Sex),
        'Age': Age,
        'Education': map_Education(Education),
        'Income': map_Income(Income)
    }, orient='index').transpose()
   

    prob = loaded_model.predict_proba(new_row)
    shap_values = explainer(new_row)
    plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)
    plt.tight_layout()
    local_plot = plt.gcf()
    plt.close()
    
    return {"Low Chance of Diabetes": float(prob[0][0]), "High Chance of Diabetes": 1-float(prob[0][0])}, local_plot

# Create the UI
title = "**Diabetes Predictor Application** 🪐"
description1 = """This app takes information from subjects and predicts their diabetes likelihood. Do not use for medical diagnosis."""

description2 = """
To use the app, click on one of the examples, or adjust the values of the factors, and click on Analyze. 🤞
""" 

with gr.Blocks(title=title) as demo:
    gr.Markdown(f"## {title}")
    gr.Markdown(description1)
    gr.Markdown("""---""")
    gr.Markdown(description2)
    gr.Markdown("""---""")
    
    with gr.Row():
        CholCheck = gr.Radio(label="Did you check your cholestorol in the past 5 years?", choices=["No", "Yes"])
        HighChol = gr.Radio(label="Do you have high cholesterol?",  choices=["No", "Yes"])
    with gr.Row():
        DiffWalk = gr.Radio(label="Do you have serious difficulty walking or climbing stairs?", choices=["No", "Yes"])
        BMI = gr.Number(label="BMI")
    with gr.Row():
        Smoker = gr.Radio(label="Are you a smoker?", choices=["No", "Yes"])
        HvyAlcoholConsump = gr.Radio(label="Do you drink often?", choices=["No", "Yes"])
    with gr.Row():
        Stroke = gr.Radio(label="Have you had a stroke?", choices=["No", "Yes"])
        HighBP = gr.Radio(label="Do you have high blood pressure?", choices=["No", "Yes"])
        HeartDiseaseorAttack = gr.Radio(label="Do you have coronary heart disease or myocardial infarction?", choices=["No", "Yes"])
    with gr.Row():
        PhysActivity = gr.Radio(label="Did you partake in physical activity in the past 30 days?", choices=["No", "Yes"])
        Fruits = gr.Radio(label="Do you consume fruit 1 or more times per day?", choices=["No", "Yes"])
        Veggies = gr.Radio(label="Do you consume vegetables 1 or more times per day?", choices=["No", "Yes"])
    with gr.Row():
        AnyHealthcare = gr.Radio(label="Do you have any kind of health care coverage?", choices=["No", "Yes"])
        NoDocbcCost = gr.Radio(label="Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?", choices=["No", "Yes"])
    with gr.Row():
        MentHlth = gr.Number(label="How many days in the past 30 days did you have poor mental health?")
        PhysHlth = gr.Number(label="How many days in the past 30 days did you have poor physical health?")
        GenHlth = gr.Slider(label="In general, rank your overall health on a scale: 1(excellent)-5(poor)", minimum=1, maximum=5)       
    with gr.Row():
        Sex = gr.Dropdown(label="Sex", choices=["Female", "Male"])
        Age = gr.Number(label="Age")
    with gr.Row():
        Education = gr.Dropdown(label="Education Level", choices=["Never attended school", "Grades 1-8", "Grades 9-11", "Grade 12 or GED", "College 1-3 years", "College 4+ years"])
        Income = gr.Dropdown(label="Income Level", choices=["> $10,000", "> $20,000", "> $25,000", "> $30,000", "> $35,000","> $50,000","> $60,000", "< $75,000"])

    with gr.Column(visible=True) as output_col:
        label = gr.Label(label = "Predicted Label")
        local_plot = gr.Plot(label = 'Shap:')
    
    submit_btn = gr.Button("Analyze")
        
    submit_btn.click(
        main_func,
        [HighBP, HighChol, CholCheck, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, 
         HvyAlcoholConsump, AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income],
        [label,local_plot],api_name="Diabetes Predictor"
    )

    gr.Markdown("### Click on any of the examples below to see how it works:")
    
    gr.Examples(
        [["No", "No", "Yes", 22, "No", "No", "No", "Yes", "Yes", "Yes", "No", "No", "Yes", 3, 25, 23, "No", "Female", 22, "Grade 12 or GED", "> $35,000"],
        ["Yes", "Yes", "Yes", 30, "Yes", "Yes", "Yes", "No", "No", "No", "Yes", "Yes", "No", 2, 20, 23, "No", "Male", 21, "College 4+ years", "< $75,000"]],
        [HighBP, CholCheck, HighChol, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, 
         AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income],
        [label, local_plot],
        main_func,
        cache_examples=True
    )

demo.launch()