app.py

import pickle
import streamlit as st
from streamlit_option_menu import option_menu


# loading the saved models

diabetes_model = pickle.load(open("./models/diabetes_model.sav", "rb"))

heart_disease_model = pickle.load(open("./models/heart_disease_model.sav", "rb"))

parkinsons_model = pickle.load(open("./models/parkinsons_model.sav", "rb"))


# sidebar for navigation
with st.sidebar:
    selected = option_menu(
        "Multiple Disease Prediction System",
        ["Diabetes Prediction", "Heart Disease Prediction", "Parkinsons Prediction"],
        icons=["activity", "heart", "person"],
        default_index=0,
    )


# Diabetes Prediction Page
if selected == "Diabetes Prediction":
    # page title
    st.title("Diabetes Prediction using ML")

    # getting the input data from the user
    col1, col2, col3 = st.columns(3)

    with col1:
        Pregnancies = st.text_input("Number of Pregnancies")

    with col2:
        Glucose = st.text_input("Glucose Level")

    with col3:
        BloodPressure = st.text_input("Blood Pressure value")

    with col1:
        SkinThickness = st.text_input("Skin Thickness value")

    with col2:
        Insulin = st.text_input("Insulin Level")

    with col3:
        BMI = st.text_input("BMI value")

    with col1:
        DiabetesPedigreeFunction = st.text_input("Diabetes Pedigree Function value")

    with col2:
        Age = st.text_input("Age of the Person")

    # code for Prediction
    diab_diagnosis = ""

    # creating a button for Prediction

    if st.button("Diabetes Test Result"):
        diab_prediction = diabetes_model.predict(
            [
                [
                    Pregnancies,
                    Glucose,
                    BloodPressure,
                    SkinThickness,
                    Insulin,
                    BMI,
                    DiabetesPedigreeFunction,
                    Age,
                ]
            ]
        )

        if diab_prediction[0] == 1:
            diab_diagnosis = "The person is diabetic"
        else:
            diab_diagnosis = "The person is not diabetic"

    st.success(diab_diagnosis)


# Heart Disease Prediction Page
if selected == "Heart Disease Prediction":
    # page title
    st.title("Heart Disease Prediction using ML")

    col1, col2, col3 = st.columns(3)

    with col1:
        age = st.text_input("Age")

    with col2:
        sex = st.text_input("Sex")

    with col3:
        cp = st.text_input("Chest Pain types")

    with col1:
        trestbps = st.text_input("Resting Blood Pressure")

    with col2:
        chol = st.text_input("Serum Cholestoral in mg/dl")

    with col3:
        fbs = st.text_input("Fasting Blood Sugar > 120 mg/dl")

    with col1:
        restecg = st.text_input("Resting Electrocardiographic results")

    with col2:
        thalach = st.text_input("Maximum Heart Rate achieved")

    with col3:
        exang = st.text_input("Exercise Induced Angina")

    with col1:
        oldpeak = st.text_input("ST depression induced by exercise")

    with col2:
        slope = st.text_input("Slope of the peak exercise ST segment")

    with col3:
        ca = st.text_input("Major vessels colored by flourosopy")

    with col1:
        thal = st.text_input(
            "thal: 0 = normal; 1 = fixed defect; 2 = reversable defect"
        )

    # code for Prediction
    heart_diagnosis = ""

    # creating a button for Prediction

    if st.button("Heart Disease Test Result"):
        heart_prediction = heart_disease_model.predict(
            [
                [
                    age,
                    sex,
                    cp,
                    trestbps,
                    chol,
                    fbs,
                    restecg,
                    thalach,
                    exang,
                    oldpeak,
                    slope,
                    ca,
                    thal,
                ]
            ]
        )

        if heart_prediction[0] == 1:
            heart_diagnosis = "The person is having heart disease"
        else:
            heart_diagnosis = "The person does not have any heart disease"

    st.success(heart_diagnosis)


# Parkinson's Prediction Page
if selected == "Parkinsons Prediction":
    # page title
    st.title("Parkinson's Disease Prediction using ML")

    col1, col2, col3, col4, col5 = st.columns(5)

    with col1:
        fo = st.text_input("MDVP:Fo(Hz)")

    with col2:
        fhi = st.text_input("MDVP:Fhi(Hz)")

    with col3:
        flo = st.text_input("MDVP:Flo(Hz)")

    with col4:
        Jitter_percent = st.text_input("MDVP:Jitter(%)")

    with col5:
        Jitter_Abs = st.text_input("MDVP:Jitter(Abs)")

    with col1:
        RAP = st.text_input("MDVP:RAP")

    with col2:
        PPQ = st.text_input("MDVP:PPQ")

    with col3:
        DDP = st.text_input("Jitter:DDP")

    with col4:
        Shimmer = st.text_input("MDVP:Shimmer")

    with col5:
        Shimmer_dB = st.text_input("MDVP:Shimmer(dB)")

    with col1:
        APQ3 = st.text_input("Shimmer:APQ3")

    with col2:
        APQ5 = st.text_input("Shimmer:APQ5")

    with col3:
        APQ = st.text_input("MDVP:APQ")

    with col4:
        DDA = st.text_input("Shimmer:DDA")

    with col5:
        NHR = st.text_input("NHR")

    with col1:
        HNR = st.text_input("HNR")

    with col2:
        RPDE = st.text_input("RPDE")

    with col3:
        DFA = st.text_input("DFA")

    with col4:
        spread1 = st.text_input("spread1")

    with col5:
        spread2 = st.text_input("spread2")

    with col1:
        D2 = st.text_input("D2")

    with col2:
        PPE = st.text_input("PPE")

    # code for Prediction
    parkinsons_diagnosis = ""

    # creating a button for Prediction
    if st.button("Parkinson's Test Result"):
        parkinsons_prediction = parkinsons_model.predict(
            [
                [
                    fo,
                    fhi,
                    flo,
                    Jitter_percent,
                    Jitter_Abs,
                    RAP,
                    PPQ,
                    DDP,
                    Shimmer,
                    Shimmer_dB,
                    APQ3,
                    APQ5,
                    APQ,
                    DDA,
                    NHR,
                    HNR,
                    RPDE,
                    DFA,
                    spread1,
                    spread2,
                    D2,
                    PPE,
                ]
            ]
        )

        if parkinsons_prediction[0] == 1:
            parkinsons_diagnosis = "The person has Parkinson's disease"
        else:
            parkinsons_diagnosis = "The person does not have Parkinson's disease"

    st.success(parkinsons_diagnosis)