app.py

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

st.set_page_config(page_title="Mulitple Disease Prediction",layout="wide", page_icon="👨‍🦰🤶")

working_dir = os.path.dirname(os.path.abspath(__file__))

diabetes_model = pickle.load(open(f'{working_dir}/saved_models/diabetes.pkl','rb'))
heart_disease_model = pickle.load(open(f'{working_dir}/saved_models/heart.pkl','rb'))
kidney_disease_model = pickle.load(open(f'{working_dir}/saved_models/kidney.pkl','rb'))

NewBMI_Overweight=0
NewBMI_Underweight=0
NewBMI_Obesity_1=0
NewBMI_Obesity_2=0 
NewBMI_Obesity_3=0
NewInsulinScore_Normal=0 
NewGlucose_Low=0
NewGlucose_Normal=0 
NewGlucose_Overweight=0
NewGlucose_Secret=0

with st.sidebar:
    selected = option_menu("Mulitple Disease Prediction", 
                ['Diabetes Prediction',
                 'Heart Disease Prediction',
                 'Kidney Disease Prediction'],
                 menu_icon='hospital-fill',
                 icons=['activity','heart', 'person'],
                 default_index=0)

if selected == 'Diabetes Prediction':
    st.title("Diabetes Prediction Using Machine Learning")

    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("BloodPressure Value")
    with col1:
        SkinThickness = st.text_input("SkinThickness Value")
    with col2:
        Insulin = st.text_input("Insulin Value")
    with col3:
        BMI = st.text_input("BMI Value")
    with col1:
        DiabetesPedigreeFunction = st.text_input("DiabetesPedigreeFunction Value")
    with col2:
        Age = st.text_input("Age")
    diabetes_result = ""
    if st.button("Diabetes Test Result"):
        if float(BMI)<=18.5:
            NewBMI_Underweight = 1
        elif 18.5 < float(BMI) <=24.9:
            pass
        elif 24.9<float(BMI)<=29.9:
            NewBMI_Overweight =1
        elif 29.9<float(BMI)<=34.9:
            NewBMI_Obesity_1 =1
        elif 34.9<float(BMI)<=39.9:
            NewBMI_Obesity_2=1
        elif float(BMI)>39.9:
            NewBMI_Obesity_3 = 1
        
        if 16<=float(Insulin)<=166:
            NewInsulinScore_Normal = 1

        if float(Glucose)<=70:
            NewGlucose_Low = 1
        elif 70<float(Glucose)<=99:
            NewGlucose_Normal = 1
        elif 99<float(Glucose)<=126:
            NewGlucose_Overweight = 1
        elif float(Glucose)>126:
            NewGlucose_Secret = 1

        user_input=[Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,
                    BMI,DiabetesPedigreeFunction,Age, NewBMI_Underweight,
                    NewBMI_Overweight,NewBMI_Obesity_1,
                    NewBMI_Obesity_2,NewBMI_Obesity_3,NewInsulinScore_Normal, 
                    NewGlucose_Low,NewGlucose_Normal, NewGlucose_Overweight,
                    NewGlucose_Secret]
        
        user_input = [float(x) for x in user_input]
        prediction = diabetes_model.predict([user_input])
        if prediction[0]==1:
            diabetes_result = "The person has diabetic"
        else:
            diabetes_result = "The person has no diabetic"
    st.success(diabetes_result)

if selected == 'Heart Disease Prediction':
    st.title("Heart Disease Prediction Using Machine Learning")
    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 Cholestroal 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')
    heart_disease_result = ""
    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]
        prediction = heart_disease_model.predict([user_input])
        if prediction[0]==1:
            heart_disease_result = "This person is having heart disease"
        else:
            heart_disease_result = "This person does not have any heart disease"
    st.success(heart_disease_result)

if selected == 'Kidney Disease Prediction':
    
    st.title("Kidney Disease Prediction using ML")

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

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

    with col2:
        blood_pressure = st.text_input('Blood Pressure')

    with col3:
        specific_gravity = st.text_input('Specific Gravity')

    with col4:
        albumin = st.text_input('Albumin')

    with col5:
        sugar = st.text_input('Sugar')

    with col1:
        red_blood_cells = st.text_input('Red Blood Cell')

    with col2:
        pus_cell = st.text_input('Pus Cell')

    with col3:
        pus_cell_clumps = st.text_input('Pus Cell Clumps')

    with col4:
        bacteria = st.text_input('Bacteria')

    with col5:
        blood_glucose_random = st.text_input('Blood Glucose Random')

    with col1:
        blood_urea = st.text_input('Blood Urea')

    with col2:
        serum_creatinine = st.text_input('Serum Creatinine')

    with col3:
        sodium = st.text_input('Sodium')

    with col4:
        potassium = st.text_input('Potassium')

    with col5:
        haemoglobin = st.text_input('Haemoglobin')

    with col1:
        packed_cell_volume = st.text_input('Packet Cell Volume')

    with col2:
        white_blood_cell_count = st.text_input('White Blood Cell Count')

    with col3:
        red_blood_cell_count = st.text_input('Red Blood Cell Count')

    with col4:
        hypertension = st.text_input('Hypertension')

    with col5:
        diabetes_mellitus = st.text_input('Diabetes Mellitus')

    with col1:
        coronary_artery_disease = st.text_input('Coronary Artery Disease')

    with col2:
        appetite = st.text_input('Appetitte')

    with col3:
        peda_edema = st.text_input('Peda Edema')
    with col4:
        aanemia = st.text_input('Aanemia')

    # code for Prediction
    kindey_diagnosis = ''

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

        user_input = [age, blood_pressure, specific_gravity, albumin, sugar,
       red_blood_cells, pus_cell, pus_cell_clumps, bacteria,
       blood_glucose_random, blood_urea, serum_creatinine, sodium,
       potassium, haemoglobin, packed_cell_volume,
       white_blood_cell_count, red_blood_cell_count, hypertension,
       diabetes_mellitus, coronary_artery_disease, appetite,
       peda_edema, aanemia]

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

        prediction = kidney_disease_model.predict([user_input])

        if prediction[0] == 1:
            kindey_diagnosis = "The person has Kidney's disease"
        else:
            kindey_diagnosis = "The person does not have Kidney's disease"
    st.success(kindey_diagnosis)