app.py

import numpy as np
import pickle
import streamlit as st
from sklearn.model_selection import train_test_split

# loading the saved model
# loading the saved model
with open(r"trained_model.sav", 'rb') as file:
    loaded_model = pickle.load(file)

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'
  
    
  
def main():
    
    
    # giving a title
    st.title(':violet[Diabetes Prediction Web App]')
    
    
    # getting the input data from the user
    col1,col2=st.columns(2)
    with col1:
        Pregnancies = st.text_input(':blue[Number of Pregnancies]')
        Glucose = st.text_input(':blue[Glucose Level]')
        BloodPressure = st.text_input(':blue[Blood Pressure value]')
        SkinThickness = st.text_input(':blue[Skin Thickness value]')
    with col2:
        Insulin = st.text_input(':blue[Insulin Level]')
        BMI = st.text_input(':blue[BMI value]')
        DiabetesPedigreeFunction = st.text_input(':blue[Diabetes Pedigree Function value]')
        Age = st.text_input(':blue[Age of the Person]')
    
    
    # code for Prediction
    diagnosis = ''
    
    # creating a button for Prediction
    
    if st.button('Diabetes Test Result'):
        diagnosis = diabetes_prediction([Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age])
        
        
    st.success(diagnosis)
    
    
    
    
    
if __name__ == '__main__':
    main()