app.py

# -*- coding: utf-8 -*-
"""
Created on Fri Feb 24 08:43:12 2023

@author: piku
"""

import pickle
import streamlit as st
from streamlit_option_menu import option_menu

# loading the saved models

diabetes_model = pickle.load(open('diabetes_model.sav', 'rb'))

heart_model = pickle.load(open('heart_disease_model.sav','rb'))

cancer_model = pickle.load(open('cancer_model.sav','rb'))

parkinsons_model = pickle.load(open('parkinsons_model.sav', 'rb'))


# sidebar for navigation
with st.sidebar:
    
    selected = option_menu('Multiple Disease Prediction System',
                          
                          ['Diabetes Prediction',
                           'Heart Disease Prediction',
                           'Breast Cancer Prediction',
                           'Parkinsons Prediction'],
                          icons=['activity','heart','circle','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 = 'Patient is diabetic,Please consult your doctor immediately!'
        else:
          diab_diagnosis = 'Patient is not diabetic,No need to worry!'
        
    st.success(diab_diagnosis)


# Heart Disease Prediction Page
if (selected == 'Heart Disease Prediction'):
    
    # page title
    st.title('Heart Disease Prediction using ML')
    
    # getting the input data from the user
    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_model.predict([[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal]])                          
        
        if (heart_prediction[0] == 1):
          heart_diagnosis = 'Patient has a high risk of Heart Disease,Please consult your doctor immediately!'
        else:
          heart_diagnosis = 'Patient has a low risk of Heart Disease,No need to worry!'
        
    st.success(heart_diagnosis)
        
    


# Breast Cancer Disease Prediction Page
if (selected == 'Breast Cancer Prediction'):
    
    # page title
    st.title('Breast Cancer Prediction using ML')
    
    # getting the input data from the user
    col1, col2, col3 = st.columns(3)
    
    with col1:
        concavepoints_mean = st.text_input('Concave Points Value')
        
    with col2:
        area_mean = st.text_input('Area Value')
        
    with col3:
        radius_mean = st.text_input('Radius Value')
        
    with col1:
        perimeter_mean = st.text_input('perimeter Value')
        
    with col2:
        concavity_mean = st.text_input('Concavity Value')
        
    with col3:
        texture_mean = st.text_input('Texture Value')
        
    with col1:
        smoothness_mean = st.text_input('Smoothness Value')
        
    with col2:
        compactness_mean = st.text_input('Compactness Value')
        
    with col3:
        symmetry_mean = st.text_input('Symmetry Value')
        
    with col1:
        fractaldimension_mean = st.text_input('Fractal Dimension Value')
        
     
    # code for Prediction
    cancer_diagnosis = ''
    
    # creating a button for Prediction
    
    if st.button('Cancer Disease Test Result'):
        cancer_prediction = cancer_model.predict([[concavepoints_mean, area_mean, radius_mean, perimeter_mean, concavity_mean, texture_mean, smoothness_mean, compactness_mean, symmetry_mean, fractaldimension_mean]])                          
        
        if (cancer_prediction[0] == 1):
          cancer_diagnosis = 'Patient has a high risk of Cancer Disease,Please consult your doctor immediately!'
        else:
          cancer_diagnosis = 'Patient has a low risk of Cancer Disease,No need to worry!'
        
    st.success(cancer_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 = "Patient has a high risk of Parkinson's Disease,Please consult your doctor immediately!"
        else:
          parkinsons_diagnosis = "Patient has a low risk of Parkinson's Disease,No need to worry!"
        
    st.success(parkinsons_diagnosis)