app.py

# importing required libraries
import pickle
import streamlit as st

# loading the trained model
pickle_in = open('classifier.pkl', 'rb') 
classifier = pickle.load(pickle_in)

# this is the main function in which we define our app  
def main():       
    # header of the page 
    html_temp = """ 
    <div style ="background-color:yellow;padding:13px"> 
    <h1 style ="color:black;text-align:center;">Check your Loan Eligibility</h1> 
    </div> 
    """
    st.markdown(html_temp, unsafe_allow_html = True) 

    # following lines create boxes in which user can enter data required to make prediction 
    Gender = st.selectbox('Gender',("Male","Female","Other"))
    Married = st.selectbox('Marital Status',("Unmarried","Married","Other")) 
    ApplicantIncome = st.number_input("Monthly Income in Rupees") 
    LoanAmount = st.number_input("Loan Amount in Rupees")
    result =""
      
    # when 'Check' is clicked, make the prediction and store it 
    if st.button("Check"): 
        result = prediction(Gender, Married, ApplicantIncome, LoanAmount) 
        st.success('Your loan is {}'.format(result))
 
# defining the function which will make the prediction using the data which the user inputs 
def prediction(Gender, Married, ApplicantIncome, LoanAmount): 

    # 2. Loading and Pre-processing the data 

    if Gender == "Male":
        Gender = 0
    else:
        Gender = 1

    if Married == "Married":
        Married = 1
    else:
        Married = 0

    #3. Building the model to automate Loan Eligibility 

    # if (ApplicantIncome >= 50000):
    #     loan_status = 'Approved'
    # elif (LoanAmount < 500000):
    #     loan_status = 'Approved'
    # else:
    #     loan_status = 'Rejected'
    # return loan_status

    prediction = classifier.predict( 
        [[Gender, Married, ApplicantIncome, LoanAmount]])
     
    if prediction == 0:
        pred = 'Rejected'
    else:
        pred = 'Approved'
    return pred
     
if __name__=='__main__': 
    main()