app.py

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

loaded_model = pickle.load(open('trained_model.csv', 'rb'))


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():

    st.title("Diabetics Prediction Web App")

    #getting i/p data from user

    Pregnancies = st.text_input('No.of Pregnancies')
    Glucose = st.text_input('Glucose Level')
    BloodPressure = st.text_input('Blood Pressure Level')
    SkinThickness = st.text_input('Skin Thickness Value')
    Insulin = st.text_input('Insulin Level')
    BMI = st.text_input('BMI Value')
    DiabetesPedigreeFunction = st.text_input('Diabetes Pedigree Function Value')
    Age = st.text_input('Age of Person')


    # code for prediction

    diagnosis = " "

    if st.button('Diabetes Test Result'):
        diagnosis = diabetes_prediction([Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age])

    st.success(diagnosis)

if __name__ == '__main__':
    main()