app.py

# -*- coding: utf-8 -*-
"""
Created on Sat Mar 30 22:08:00 2024

@author: aman0
"""

import numpy as np
import pickle
import streamlit as st
from streamlit_option_menu import option_menu



# creating a function for Prediction

def diabetes_prediction(input_data):
    

    # changing the input_data to numpy array
    input_data_as_numpy_array = np.asarray(input_data)

    # reshape the array as we are predicting for one instance
    input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

    prediction = loaded_model.predict(input_data_reshaped)
    print(prediction)

    if (prediction[0] == 0):
      return 'The person is not diabetic'
    else:
      return 'The person is diabetic'
  
    

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

    

# loading the saved model
loaded_model = pickle.load(open('trained_model.sav', 'rb'))
# sidebar for navigation
with st.sidebar:
    selected = option_menu('Multiple Disease Prediction System',

                           ['Diabetes Prediction'],
                           menu_icon='hospital-fill',
                           icons=['activity'],
                           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'):

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

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

        diab_prediction = loaded_model.predict([user_input])

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

    st.success(diab_diagnosis)