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="Prediction of Disease Outbreaks", layout="wide", page_icon="🔬")

# Custom CSS for styling
st.markdown("""
    <style>
        .stButton > button {
            background-color: #4CAF50;
            color: white;
            font-size: 18px;
            border-radius: 10px;
            padding: 10px 20px;
            margin-top: 20px;
        }
        .stButton > button:hover {
            background-color: #45a049;
            color: #06402B;
            border : 2px solid #06402B;
        }
        .sidebar .sidebar-content {
            background-color: #f5f5f5;
        }
        .stTextInput input {
            font-size: 16px;
            padding: 10px;
            border-radius: 5px;
        }
        .stTitle {
            font-size: 28px;
            font-weight: bold;
        }
        .stSubheader {
            font-size: 22px;
        }
    </style>
""", unsafe_allow_html=True)

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

# Sidebar for navigation
with st.sidebar:
    selected = option_menu('Prediction of Disease Outbreaks System',
                           ['Diabetes Prediction',
                            'Heart Disease Prediction',
                            'Parkinsons Prediction'],
                           menu_icon='hospital-fill',
                           icons=['activity', 'heart', 'person'],
                           default_index=0)

# Diabetes Prediction Page
if selected == 'Diabetes Prediction':
    st.title('Diabetes Prediction using Machine Learning')
    
    col1, col2, col3 = st.columns(3)
    with col1:
        Pregnancies = st.number_input('Number of Pregnancies', min_value=0, step=1)
    with col2:
        Glucose = st.number_input('Glucose Level', min_value=0, step=1)
    with col3:
        BloodPressure = st.number_input('Blood Pressure Value', min_value=0, step=1)
    with col1:
        SkinThickness = st.number_input('Skin Thickness Value', min_value=0, step=1)
    with col2:
        Insulin = st.number_input('Insulin Level', min_value=0, step=1)
    with col3:
        BMI = st.number_input('BMI Value', min_value=0.0, step=0.1)
    with col1:
        DiabetesPedigreeFunction = st.number_input('Diabetes Pedigree Function Value', min_value=0.0, step=0.01)
    with col2:
        Age = st.number_input('Age of the Person', min_value=0, step=1)

    # Prediction Button
    if st.button('Diabetes Test Result'):
        user_input = [Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age]
        diab_prediction = diabetes_model.predict([user_input])
        if diab_prediction[0] == 1:
            st.success('The person is diabetic')
        else:
            st.success('The person is not diabetic')

# Heart Disease Prediction Page
if selected == 'Heart Disease Prediction':
    st.title('Heart Disease Prediction using Machine Learning')
    
    col1, col2, col3 = st.columns(3)
    with col1:
        age = st.number_input('Age', min_value=0, step=1)
    with col2:
        sex = st.number_input('Sex (1 = male, 0 = female)', min_value=0, max_value=1)
    with col3:
        cp = st.number_input('Chest Pain Types', min_value=0, step=1)
    with col1:
        trestbps = st.number_input('Resting Blood Pressure', min_value=0, step=1)
    with col2:
        chol = st.number_input('Serum Cholestoral in mg/dl', min_value=0, step=1)
    with col3:
        fbs = st.number_input('Fasting Blood Sugar > 120 mg/dl (1 = true; 0 = false)', min_value=0, max_value=1)
    with col1:
        restecg = st.number_input('Resting Electrocardiographic Results', min_value=0, step=1)
    with col2:
        thalach = st.number_input('Maximum Heart Rate Achieved', min_value=0, step=1)
    with col3:
        exang = st.number_input('Exercise Induced Angina (1 = yes; 0 = no)', min_value=0, max_value=1)
    with col1:
        oldpeak = st.number_input('ST Depression Induced by Exercise', min_value=0.0, step=0.1)
    with col2:
        slope = st.number_input('Slope of the Peak Exercise ST Segment', min_value=0, step=1)
    with col3:
        ca = st.number_input('Number of Major Vessels Colored by Fluoroscopy', min_value=0, step=1)
    with col1:
        thal = st.number_input('Thalassemia (1 = normal; 2 = fixed defect; 3 = reversible defect)', min_value=0, max_value=3)

    # Prediction Button
    if st.button('Heart Disease Test Result'):
        user_input = [age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal]
        heart_prediction = heart_disease_model.predict([user_input])
        if heart_prediction[0] == 1:
            st.success('The person is having heart disease')
        else:
            st.success('The person does not have any heart disease')

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

    col1, col2, col3, col4, col5 = st.columns(5)
    with col1:
        fo = st.number_input('MDVP:Fo(Hz)', min_value=0.0, step=0.01)
    with col2:
        fhi = st.number_input('MDVP:Fhi(Hz)', min_value=0.0, step=0.01)
    with col3:
        flo = st.number_input('MDVP:Flo(Hz)', min_value=0.0, step=0.01)
    with col4:
        Jitter_percent = st.number_input('MDVP:Jitter(%)', min_value=0.0, step=0.01)
    with col5:
        Jitter_Abs = st.number_input('MDVP:Jitter(Abs)', min_value=0.0, step=0.01)
    with col1:
        RAP = st.number_input('MDVP:RAP', min_value=0.0, step=0.01)
    with col2:
        PPQ = st.number_input('MDVP:PPQ', min_value=0.0, step=0.01)
    with col3:
        DDP = st.number_input('Jitter:DDP', min_value=0.0, step=0.01)
    with col4:
        Shimmer = st.number_input('MDVP:Shimmer', min_value=0.0, step=0.01)
    with col5:
        Shimmer_dB = st.number_input('MDVP:Shimmer(dB)', min_value=0.0, step=0.01)
    with col1:
        APQ3 = st.number_input('Shimmer:APQ3', min_value=0.0, step=0.01)
    with col2:
        APQ5 = st.number_input('Shimmer:APQ5', min_value=0.0, step=0.01)
    with col3:
        APQ = st.number_input('MDVP:APQ', min_value=0.0, step=0.01)
    with col4:
        DDA = st.number_input('Shimmer:DDA', min_value=0.0, step=0.01)
    with col5:
        NHR = st.number_input('NHR', min_value=0.0, step=0.01)
    with col1:
        HNR = st.number_input('HNR', min_value=0.0, step=0.01)
    with col2:
        RPDE = st.number_input('RPDE', min_value=0.0, step=0.01)
    with col3:
        DFA = st.number_input('DFA', min_value=0.0, step=0.01)
    with col4:
        spread1 = st.number_input('spread1', step=0.01)
    with col5:
        spread2 = st.number_input('spread2', min_value=0.0, step=0.01)
    with col1:
        D2 = st.number_input('D2', min_value=0.0, step=0.01)
    with col2:
        PPE = st.number_input('PPE', min_value=0.0, step=0.01)

    # Prediction Button
    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]
        parkinsons_prediction = parkinsons_model.predict([user_input])
        if parkinsons_prediction[0] == 1:
            st.success("The person has Parkinson's disease")
        else:
            st.success("The person does not have Parkinson's disease")