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# -*- coding: utf-8 -*-
"""
Diabetes Prediction Web App
"""
import numpy as np
import pickle
import streamlit as st
# loading the saved model
loaded_model = pickle.load(open('LogisticRegression_model.pkl', 'rb'))
# 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'
def main():
# giving a title
st.title('Diabetes Prediction Web App')
# getting the input data from the user
Pregnancies = st.text_input('Number of Pregnancies')
Glucose = st.text_input('Glucose Level')
BloodPressure = st.text_input('Blood Pressure Value')
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 the Person')
# code for Prediction
diagnosis = ''
# creating a button for Prediction
if st.button('Diabetes Test Result'):
try:
# Convert input data to floating-point numbers with error handling
input_data = [
float(Pregnancies),
float(Glucose),
float(BloodPressure),
float(SkinThickness),
float(Insulin),
float(BMI),
float(DiabetesPedigreeFunction),
float(Age)
]
diagnosis = diabetes_prediction(input_data)
except ValueError as e:
diagnosis = "Invalid input. Please enter numeric values for all fields."
st.success(diagnosis)
if __name__ == '__main__':
main() |