app.py

import os
import pickle
import streamlit as st
from streamlit_option_menu import option_menu

# Set page configuration
st.set_page_config(page_title="Health Assistant",
                   layout="wide",
                   page_icon="🧑‍⚕️")

   
# getting the working directory of the main.py
working_dir = os.path.dirname(os.path.abspath(__file__))

# loading the saved models

diabetes_model = pickle.load(open(f'{working_dir}/saved_models/diabetes_model.sav', 'rb'))

heart_disease_model = pickle.load(open(f'{working_dir}/saved_models/heart_disease_model.sav', 'rb'))

parkinsons_model = pickle.load(open(f'{working_dir}/saved_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'],
                           menu_icon='hospital-fill',
                           icons=['activity', 'heart', 'person'],
                           default_index=0)


# Diabetes Prediction Page

    # 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'):

        user_input = [Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin,
                      BMI, DiabetesPedigreeFunction, Age]

        user_input = [float(x) for x in user_input]

        diab_prediction = diabetes_model.predict([user_input])

        if diab_prediction[0] == 1:
            diab_diagnosis = 'The person is diabetic'

            # Medicine Recommendation
            st.subheader('Medicine Recommendation:')
            st.write('Based on the prediction, it is recommended to consult with a healthcare professional for appropriate medication.')

            # Exercise Recommendation
            st.subheader('Exercise Recommendation:')
            st.write('Carbohydrate Management:/nFocus on complex carbohydrates with a low glycemic index, such as whole grains, legumes, and vegetables./nMonitor portion sizes to help regulate blood sugar levels./nLean Proteins:/nInclude lean protein sources like poultry, fish, tofu, beans, and low-fat dairy.Healthy Fats:/nChoose sources of healthy fats, such as avocados, nuts, seeds, and olive oil./nLimit saturated and trans fats found in fried foods and processed snacks./nFiber-Rich Foods:/nInclude high-fiber foods like fruits, vegetables, whole grains, and legumes to help manage blood sugar levels./nLimit Added Sugars:/nMinimize the intake of foods and beverages with added sugars./nRegular Meal Timing:/nAim for consistent meal timing and spacing throughout the day to help regulate blood sugar levels./nHydration:/nStay well-hydrated with water or other non-sugar-containing beverages.')
        else:
            diab_diagnosis = 'The person is not diabetic'

            # No Recommendation for Non-Diabetic
            st.subheader('No Specific Recommendation:')
            st.write('As the prediction indicates that the person is not diabetic, no specific medication or exercise recommendation is provided. It is still advisable to maintain a healthy lifestyle with a balanced diet and regular physical activity.')

            # Additional Recommendations for Non-Diabetic
            st.subheader('Additional Recommendations:')
            st.write('Although the prediction suggests the person is not diabetic, it is important to maintain a healthy lifestyle. Consider adopting a balanced diet, staying hydrated, and participating in regular physical activities to promote overall well-being.')

    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'):

        user_input = [age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal]

        user_input = [float(x) for x in user_input]

        heart_prediction = heart_disease_model.predict([user_input])

        if heart_prediction[0] == 1:
            heart_diagnosis = 'The person is having heart disease'
            st.success(heart_diagnosis)

            # Medicine Recommendation
            st.subheader("Medicine Recommendation:")
            st.write("Based on the prediction, it is recommended to consult with a neurologist for appropriate medication for Parkinson's disease.")

            # Exercise Recommendation
            st.subheader("Exercise Recommendation:")
            st.write('Dietary Guidelines:\nFollow a heart-healthy diet, such as the Mediterranean or DASH diet.\nEmphasize fruits, vegetables, whole grains, lean proteins, and healthy fats.\nLimit saturated fats, trans fats, cholesterol, and sodium intake.\nNutritional Requirements:\nEnsure adequate intake of omega-3 fatty acids (from fatty fish, flaxseeds, walnuts).\nMonitor and control portion sizes to maintain a healthy weight.\nConsider sources of potassium, magnesium, and fiber for blood pressure control.\nRestrictions:\nLimit processed foods, fried foods, and sugary beverages.\nBe mindful of salt intake.\n')
        else:
            heart_diagnosis = 'The person does not have any heart disease'

    st.success(heart_diagnosis)


# Parkinson's Prediction Page


    # 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 = ''


    # 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 = ''

    # code for Prediction
    parkinsons_diagnosis = ''

    # creating a button for Prediction    
    if st.button("Parkinson's Test Result"):

        user_input = [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]

        user_input = [float(x) for x in user_input]

        parkinsons_prediction = parkinsons_model.predict([user_input])

        if parkinsons_prediction[0] == 1:
            parkinsons_diagnosis = "The person has Parkinson's disease"
            st.success(parkinsons_diagnosis)

            # Medicine Recommendation
            st.subheader("Medicine Recommendation:")
            st.write("Based on the prediction, it is recommended to consult with a neurologist for appropriate medication for Parkinson's disease.")

            # Exercise Recommendation
            st.subheader("Exercise Recommendation:")
            st.write("In addition to medication, regular physical activity can be beneficial for individuals with Parkinson's disease. Consider activities like walking, stretching, and balance exercises. However, it is crucial to consult with a healthcare professional or a physical therapist for personalized exercise recommendations.")

        else:
            parkinsons_diagnosis = "The person does not have Parkinson's disease"
            st.success(parkinsons_diagnosis)

            # No Recommendation for Non-Parkinson's
            st.subheader("No Specific Recommendation:")
            st.write("As the prediction indicates that the person does not have Parkinson's disease, no specific medication or exercise recommendation is provided. It is still advisable to maintain a healthy lifestyle with regular physical activity.")


    st.success(parkinsons_diagnosis)