app.py

import pickle
import pandas as pd
import sklearn
import gradio as gr

def encode_df(df):

    sex_map = {"Male": 1, "Female": 0}
    df = df.replace({"Sex": sex_map})
    
    chestpain_map = {"Typical": 0, "Asymptomatic": 1, "Nonanginal": 2, "Nontypical" : 3}
    df = df.replace({"ChestPain": chestpain_map})
    
    thal_map = {"Fixed": 0, "Normal": 1, "Reversable": 2}
    df = df.replace({"Thal": thal_map})
    
    exang_map = {"No": 0, "Yes": 1}
    df = df.replace({"ExAng": exang_map})

    recg_map = {"Normal": 0, "Abnormal Wave": 1, "Left Ventricular Hypertrophy": 2}
    df = df.replace({"RestECG": recg_map})

    slope_map = {"Upsloping": 1, "Flat": 2, "Downsloping": 3}
    df = df.replace({"Slope": slope_map})
    
    df = df[
        [
            "Age",
            "Sex",
            "ChestPain",
            "RestECG",
            "MaxHR",
            "ExAng",
            "Oldpeak",
            "Slope",
            "Ca",
            "Thal",
        ]
    ]
    return df

filename = './heart_model.sav'

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

def predict(age, sex, cp, restecg, mhr, exang, oldpeak, slope, ca, thal):
    df = pd.DataFrame.from_dict(
        {
            "Age": [age],
            "Sex": [sex],
            "ChestPain": [cp],
            "RestECG": [restecg],
            "MaxHR": [mhr],
            "ExAng": [exang],
            "Oldpeak": [oldpeak],
            "Slope": [slope],
            "Ca": [ca],
            "Thal": [thal],
        }
    )
    
    df = encode_df(df)
    pred = loaded_model.predict_proba(df)[0]
    return {"Possible Heart Disease": float(pred[0]), "Less chance of Heart Disease": float(pred[1])}

title = "Interactive Demo for Heart Disease Prediction"
des = 'This model predicts the possibility of a heart disease using a stacking classifier model that achieved an accuracy of 78%'
article = "<p style='text-align: center'><a href='https://www.linkedin.com/in/oayodeji/'>Space by Wvle</a> | <a href='https://github.com/oayodeji/heart_disease'>Model</a>"

demo = gr.Interface(
    predict,
    [   gr.Slider(0, 80, value=25, label='Age'),
        gr.Radio(["Male", "Female"], label='Sex'),
        gr.Dropdown(["Typical", "Asymptomatic", "Nonanginal", "Nontypical"], label="Chest Pain Type"),
        gr.Dropdown(["Normal", "Abnormal Wave", "Left Ventricular Hypertrophy"], label='Resting Electrocardiographic Results'),
        gr.Number(value=100, label='Maximum Heart Rate'),
        gr.Dropdown(["No", "Yes"], label='Exercise Induced Angina'),
        gr.Slider(0, 10, value=3.1, label='ST depression induced by exercise relation to rest'),
        gr.Radio(["Upsloping", "Flat", "Downsloping"], label='Slope of the Peak Exercise'),
        gr.Radio(["0", "1", "2", "3"], label="Number of Major Vessels"),
        gr.Dropdown(["Fixed", "Normal", "Reversable"], label="Blood Flow Disorder")
    ],
    "label",
    examples=[
        [37,'Male','Typical',"Left Ventricular Hypertrophy",150,'No',2.3,'Downsloping',0,'Fixed'],
        [63,'Male','Typical',"Abnormal Wave",90,'No',1.0,'Downsloping',1,'Reversable'],
        [58,'Female','Asymptomatic',"Normal",111,'Yes',0.8,'Flat',0,'Normal']
    ],
    title=title,
    description=des,
    article=article,
    interpretation="default",
    live=True,
)

if __name__ == "__main__":
    demo.launch()