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("heart_xgb.pkl", 'rb'))

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

# Create the main function for server
def main_func(age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall):
    new_row = pd.DataFrame.from_dict({'age':age,'sex':sex,
              'cp':cp,'trtbps':trtbps,'chol':chol,
              'fbs':fbs, 'restecg':restecg,'thalachh':thalachh,'exng':exng,
                                     'oldpeak':oldpeak,'slp':slp,'caa':caa,'thall':thall}, 
                                     orient = 'index').transpose()
    
    prob = loaded_model.predict_proba(new_row)
    
    shap_values = explainer(new_row)
    # plot = shap.force_plot(shap_values[0], matplotlib=True, figsize=(30,30), show=False)
    # plot = shap.plots.waterfall(shap_values[0], max_display=6, show=False)
    plot = shap.plots.bar(shap_values[0], max_display=8, order=shap.Explanation.abs, show_data='auto', show=False)

    plt.tight_layout()
    local_plot = plt.gcf()
    plt.close()
    
    return {"Low Chance": float(prob[0][0]), "High Chance": 1-float(prob[0][0])}, local_plot

# Create the UI
title = "**Heart Attack Predictor & Interpreter** 🪐"
description1 = """This app takes info from subjects and predicts their heart attack likelihood. Do not use these results for an actual medical diagnosis."""

description2 = """
To use the app, simply adjust the inputs and click the "Analyze" button. You can also click one of the examples below to see how it's done!
""" 

with gr.Blocks(title=title) as demo:

    with gr.Row():
        with gr.Column():
            gr.Markdown(f"# {title}")
            gr.Markdown(f"## How does it work?")
            gr.Markdown(description1)
            gr.Markdown("""---""")
            gr.Markdown(description2)

    gr.Markdown("""---""")
    
    with gr.Row():
        with gr.Column():
            gr.Markdown(f"## Edit the Inputs Below:")
            gr.Markdown("""---""")

            with gr.Row():
                age = gr.Number(label="Age", info="How old are you?", value=40)
                # sex = gr.Radio(["Male", "Female"], label = "What Gender are you?", type = "index")
                sex = gr.Radio(["Male", "Female"], label="Sex", info="What gender are you?", type="index")
                # sex = gr.Radio(choices=["Male", "Female"])
            
            cp = gr.Radio(["Typical Angina", "Atypical Angina", "Non-anginal Pain", "Asymptomatic"], label="Chest Pain", info="What kind of chest pain do you have?", type="index")
            # cp = gr.Slider(label="Chest Pain Type", minimum=1, maximum=5, value=4, step=1)
             # trtbps = gr.Slider(label="Resting blood pressure (in mm Hg)", minimum=1, maximum=200, value=4, step=1) 
            trtbps = gr.Number(label="trtbps", value=100) 
            chol = gr.Number(label="chol", value=70) 
            fbs = gr.Radio(["False", "True"], label="fbs",  info="Is your fasting blood sugar > 120 mg/dl?" , type="index")

            # restecg = gr.Slider(label="Resting ECG Score", minimum=1, maximum=5, value=4, step=1)
            restecg = gr.Dropdown(["Normal", "Having ST-T wave abnormality", "Showing probable or definite left ventricular hypertrophy by Estes' criteria"], label="rest_ecg", type="index")
            thalachh = gr.Slider(label="thalach Score", minimum=1, maximum=205, value=4, step=1)
            exng = gr.Radio(["No", "Yes"], label="Exercise Induced Angina", type="index")
            oldpeak = gr.Slider(label="Oldpeak Score", minimum=1, maximum=10, value=4, step=1)
            slp = gr.Slider(label="Slp Score", minimum=1, maximum=5, value=4, step=1)
            caa = gr.Slider(label="Number of Major Vessels", minimum=1, maximum=3, value=3, step=1)
            thall = gr.Slider(label="Thall Score", minimum=1, maximum=5, value=4, step=1)

            
            
            

        with gr.Column():
            gr.Markdown(f"## Output:")
            gr.Markdown("""---""")
            with gr.Column(visible=True) as output_col:
                label = gr.Label(label = "Predicted Label")
                local_plot = gr.Plot(label = 'Shap:')

            gr.Markdown(f"## Examples:")
            gr.Markdown("""---""")
            gr.Markdown("### Click on any of the examples below to see how it works:")
            gr.Examples([[24,"Male","Typical Angina",4,5,"True","Normal",4,"No",5,1,2,3], [24,"Female","Asymptomatic",4,5,"False","Normal",2,"Yes",1,1,2,3]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall], [label,local_plot], main_func, cache_examples=True)

        
    submit_btn = gr.Button("Analyze", variant="primary")


    gr.Markdown("""---""")
    gr.Markdown(f"## Data Dictionary:")
    gr.Markdown("""
    
Age : Age of the patient
Sex : Sex of the patient
trtbps : resting blood pressure (in mm Hg)
chol : cholestoral in mg/dl fetched via BMI sensor
fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
rest_ecg : resting electrocardiographic results
    Value 0: normal
    Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
    Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
    
thalach : maximum heart rate achieved
target : 0 = less chance of heart attack 1= more chance of heart attack""")
    

    submit_btn.click(
        main_func,
        [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall],
        [label,local_plot], api_name="Heart_Predictor"
    )

    
demo.launch()