app.py

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


# In[38]:


def encode_df(df):

    # Gender
    sex_map = {"Male": 1, "Female": 0}
    df = df.replace({"Gender": sex_map})

    # Tipe Angina
    chestpain_map = {"Typical": 0, "Asymptomatic": 1,
                     "Nonanginal": 2, "Nontypical": 3}
    df = df.replace({"Tipe_angina": chestpain_map})

    # Gula_darah_puasa
    fastingBLP_map = {"<120": 0, ">120": 1, "120": 2}
    df = df.replace({"Gula_darah_puasa": fastingBLP_map})

    # Angina_aktivitas
    exang_map = {"No": 0, "Yes": 1}
    df = df.replace({"Angina_aktivitas": exang_map})

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

    # st_slope_ECG
    slope_map = {"Upsloping": 1, "Flat": 2, "Downsloping": 3}
    df = df.replace({"st_slope_ECG": slope_map})

    df = df[
        [
            "Umur",
            "Gender",
            "Tipe_angina",
            "Tekanan_darah_istirahat",
            "Kolesterol",
            "Gula_darah_puasa",
            "Hasil_Elektrokardiografi",
            "Denyut_jantung_max",
            "Angina_aktivitas",
            "st_depression_ECG",
            "st_slope_ECG",
        ]
    ]
    return df


filename = 'ACS_model_random_forest.sav'

# load the model from disk
loaded_model = joblib.load(filename)


# In[51]:


def predict(Umur, Gender, Tipe_angina, Tekanan_darah_istirahat, Kolesterol,
            Gula_darah_puasa, Hasil_Elektrokardiografi,
            Denyut_jantung_max, Angina_aktivitas, st_depression_ECG, st_slope_ECG):
    df = pd.DataFrame.from_dict(
        {
            "Umur": [Umur],
            "Gender": [Gender],
            "Tipe_angina": [Tipe_angina],
            "Tekanan_darah_istirahat": [Tekanan_darah_istirahat],
            "Kolesterol": [Kolesterol],
            "Gula_darah_puasa": [Gula_darah_puasa],
            "Hasil_Elektrokardiografi": [Hasil_Elektrokardiografi],
            "Denyut_jantung_max": [Denyut_jantung_max],
            "Angina_aktivitas": [Angina_aktivitas],
            "st_depression_ECG": [st_depression_ECG],
            "st_slope_ECG": [st_slope_ECG],
        }
    )

    df = encode_df(df)
    pred = loaded_model.predict_proba(df)[0]
    output = {"Possible Heart Disease": float(pred[1]),
              "Less chance of Heart Disease": float(pred[0])}

    return output


title = "Interactive Demonstration for ACS Prediction System"
des = '''This model predicts the possibility of a Acute Coronary Syndrome using a hybrid sampling SMOTE-TOMEK with Random Forest Algorithm model that achieved an high accuracy of 85%'''
article = "<p style='text-align: center'><a href='https://www.linkedin.com/in/m-afif-rizky-a-a96048182/'>Created by @Vrooh933 Production</a> | <a href='https://github.com/afifrizkyandika11551100310'>GitHub Profile</a>"

demo = gr.Interface(predict,
                    [gr.Slider(0, 88, value=25, label='Umur'),
                     gr.Radio(["Male", "Female"], label='Gender'),
                     gr.Dropdown(["Typical", "Asymptomatic", "Nonanginal",
                                 "Nontypical"], label="Tipe_angina"),

                     gr.Slider(0, 200, value=125,
                               label='Tekanan_darah_istirahat'),
                     gr.Slider(0, 603, value=50, label='Kolesterol'),
                     gr.Radio(["<120", ">120", '120'],
                              label='Gula_darah_puasa'),
                     gr.Dropdown(["Normal", "Abnormal Wave", "Left Ventricular Hypertrophy"],
                                 label='Hasil_Elektrokardiografi'),
                     gr.Number(value=100, label='Denyut_jantung_max'),
                     gr.Dropdown(["No", "Yes"], label='Angina_aktivitas'),
                     gr.Slider(-2.6, 6.2, value=3.1,
                               label='st_depression_ECG'),
                     gr.Radio(["Upsloping", "Flat", "Downsloping"],
                              label='st_slope_ECG'),
                     ], "label",
                    examples=[[37, 'Male', 'Typical', 89, 276, '>120', "Left Ventricular Hypertrophy", 150, 'No', 2.3, 'Downsloping', 0, 'Fixed'],
                              [63, 'Male', 'Typical', 50, 100, '<120', "Abnormal Wave",
                                  90, 'No', 1.0, 'Downsloping', 1, 'Reversable'],
                              [58, 'Female', 'Asymptomatic', 70, 500, '>120',
                               "Normal", 111, 'Yes', 0.8, 'Flat', 0, 'Normal']
                              ],
                    title=title,
                    description=des,
                    article=article,
                    live=True,
                    )


# In[53]:


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