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.

sex_dictionary = {"Male":0,"Female":1}

# 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_dictionary[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 {"High Risk": float(prob[0][1]), "Low Risk": 1-float(prob[0][1])}, local_plot

# Create the UI
title = "**Heart Attack Demo App** 🫀"
description1 = """
This app takes six inputs about employees' satisfaction with different aspects of their work (such as work-life balance, ...) and predicts whether the employee intends to stay with the employer or leave. There are two outputs from the app: 1- the predicted probability of stay or leave, 2- Shapley's force-plot which visualizes the extent to which each factor impacts the stay/ leave prediction.✨   
"""

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

with gr.Blocks(title=title) as demo:
    gr.Markdown(f"## {title}")
#    gr.Markdown("""![marketing](file/marketing.jpg)""")
    gr.Markdown(description1)
    gr.Markdown("""---""")
    gr.Markdown(description2)
    gr.Markdown("""---""")
    with gr.Row():
        with gr.Column():
            age = gr.Number(label="Age Score", value=40)
            sex = gr.Dropdown(["Male", "Female"], label="Gender")
            cp = gr.Slider(minimum=1, maximum=4, default=1, step=1, label="Chest Pain Type")
            trtbps = gr.Slider(minimum=50, maximum=200, default=120, step=1, label="Resting Blood Pressure (in mm Hg)")
            chol = gr.Slider(minimum=80, maximum=500, default=190, step=1, label="Cholesterol Level (mg/dL)")
            fbs = gr.Slider(minimum=0, maximum=1, default=0, step=.1, label="(fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)")
            restecg = gr.Slider(minimum=0, maximum=200, step=1, default=80, label="resting electrocardiographic results")
        with gr.Column():
            thalachh = gr.Slider(minimum=80, maximum=400, step=1, default=200, label="maximum heart rate achieved")
            exng = gr.Slider(minimum=80, maximum=400, step=1, default=200, label="maximum heart rate achieved")
            oldpeak = gr.Slider(minimum=0, maximum=10, step=.1, default=1, label="ST depression induced by exercise relative to rest")
            slp = gr.Slider(minimum=0, maximum=2, step=.1, default=1, label="speech-language pathologist")
            caa = gr.Slider(minimum=0, maximum=4, step=.1, default=2, label="cerebral amyloid angiopathy")
            thall = gr.Slider(minimum=0, maximum=3, default=2, step=.1, label="thallium stress test")
            submit_btn = gr.Button("Process")

    with gr.Row(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 Attack Rate"
    )
    
    gr.Markdown("### Click on any of the examples below to see how it works:")
    gr.Examples([[20,"Male",1,1,1,1,1,1,1,1,1,1,1], [30,"Female",1,1,1,1,1,1,1,1,1,1,1]], 
                [age,sex,cp,trtbps,chol,fbs,restecg,thalachh,exng,oldpeak,slp,caa,thall],
                [label,local_plot], main_func, cache_examples=True)

demo.launch()