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=6, 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 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():
        with gr.Column():
            age = gr.Number(label="What's your age?")
            sex = gr.Dropdown(label="What's your sex?", choices = ["Female", "Male"],type="index")
            cp = gr.inputs.Dropdown(["typical", "atypical", "other", "asymptomatic"], label="Chest pain type")

        with gr.Column():
            gr.Markdown("""![Heart Attack!](file/heartattack.jpeg)""")
        
        with gr.Column():        
            trtbps = gr.inputs.Slider(50, 180, default=80, label="Resting blood pressure") 
            chol = gr.Number(label="What is your cholesterol in (mg/dl)?", value=100, info = "cholestoral in mg/dl" )
            fbs = gr.Dropdown(label="Is your fasting blood sugar > 120 mg/dl?", choices = ["yes","no"], type = "index")
            restecg = gr.Dropdown(label="What is your resting ECG result?", choices = ["normal","ST-T wave abnormality"], type = "index", value = "normal", info = "resting ESG result")
            thall = gr.Dropdown(label="What is your Thalassemia condition?", choices = ["NULL","Fixed Defect", "Normal Blood Flow", "Reversible Defect"], type = "index", value = "NULL")
            
            
        with gr.Column():
            thalachh = gr.Number(label="What is your maximum heart rate?", value=100)
            exng = gr.Dropdown(label="exercise-induced angina", choices = ["yes","no"], type = "index", value = "1")
            oldpeak = gr.Slider(label="ST depression induced by exercise relative to rest", minimum=0, maximum=10, value=4, step=.1)
            slp = gr.Dropdown(label="Slope of the peak exercise ST segment", choices = ["upsloping","flat","downsloping"], type = "index", value = "1")
            caa = gr.Dropdown(label="Degree of coronary artery anomaly", choices = ["0","1","2","3","4"], type = "index", value = "1")
          
        
    submit_btn = gr.Button("Process")

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

    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"
    )
    
    gr.Markdown("### Click on any of the examples below to see how it works:")
    gr.Examples([[24,"male",4,70,200,"yes","normal",80,"yes",5,1,2,"Fixed Defect"], [24,"female",3,80,180,"no","normal",90,"no",1,1,2,"Reversible Defect"]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall], [label,local_plot], main_func, cache_examples=True)

demo.launch()