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| import streamlit as st | |
| import joblib | |
| import pandas as pd | |
| from google.cloud import bigquery | |
| import datetime | |
| import os | |
| import base64 | |
| Retrieve the base64-encoded credentials from environment variables | |
| encoded_credentials = os.getenv('BIGQUERY_KEY') | |
| #Decode the base64-encoded credentials | |
| decoded_credentials = base64.b64decode(encoded_credentials).decode('utf-8') | |
| # Set Google Application Credentials | |
| os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = decoded_credentials | |
| dataset = 'home_loan' | |
| table = 'home_loan_approval' | |
| client = bigquery.Client() | |
| table_ref = client.dataset(dataset).table(table) | |
| st.title("Welcome to ABC Bank") | |
| model = joblib.load('model_final.joblib') | |
| #Even though we are not going to use gender to predict the loan status, | |
| #we will be getting the gender data for future plans/schemes. | |
| with st.form('Loan Form',clear_on_submit=True): | |
| col1,col2 = st.columns(2) | |
| with col1: | |
| Gender = st.selectbox('Gender',('Male','Female')) | |
| Applicant_Income = st.number_input('Applicant Income',min_value=15000) | |
| Coapplicant_Income = st.number_input('Co-applicant Income',min_value=0) | |
| Loan_amount = st.number_input('Loan Amount (In Lakhs)',min_value=2) | |
| Loan_Amount_Term = st.number_input('Loan Amount Term (Months)',min_value=12) | |
| with col2: | |
| Property_Area = st.selectbox('Property Area',('Urban','Rural','Semiurban')) | |
| Credit_History = st.number_input('Credit History',min_value=0,max_value=1) | |
| Self_Employed = st.selectbox('Self Employed',('Yes','No')) | |
| Dependents = st.selectbox('Dependents',('0','1','2','3+')) | |
| Education = st.selectbox('Education',('Graduate','Not Graduate')) | |
| Married = st.selectbox('Married',('Yes','No')) | |
| df = pd.DataFrame({ | |
| 'Married': [Married], | |
| 'Dependents': [Dependents], | |
| 'Education': [Education], | |
| 'Self_Employed': [Self_Employed], | |
| 'Applicant_Income': [Applicant_Income/100], | |
| 'Coapplicant_Income': [Coapplicant_Income/100], | |
| 'Loan_Amount': [Loan_amount], | |
| 'Loan_Amount_Term': [Loan_Amount_Term], | |
| 'Credit_History': [Credit_History], | |
| 'Property_Area': [Property_Area]} | |
| ) | |
| def emi_calculator(principle,term): | |
| #interest = PNR/100 | |
| interest = (principle * 1000 * 8.5 * term )/float(12*100) | |
| emi = ((principle*1000) + interest)/term | |
| return emi | |
| df['EMI'] = df.apply(lambda row: emi_calculator(row['Loan_Amount'],row['Loan_Amount_Term']),axis =1) | |
| df['EMI'] = round(df['EMI'],2) | |
| df['Balance_Income'] = (df['Applicant_Income'] + df['Coapplicant_Income']) - df['EMI'] | |
| df['Balance_Income'] = round(df['Balance_Income'],2) | |
| df.drop(columns = ['Applicant_Income','Coapplicant_Income','Loan_Amount','Loan_Amount_Term']) | |
| submit = st.form_submit_button('Predict') | |
| if submit: | |
| prediction = model.predict(df) | |
| df['_created_at'] = datetime.datetime.now() | |
| df['_created_by'] = 'user' | |
| df['Approval_Id'] = df['_created_by']+'-'+df['_created_at'].astype('str') | |
| df['Prediction'] = prediction | |
| job = client.load_table_from_dataframe(df, table_ref) | |
| result = job.result() | |
| if prediction: | |
| st.success(f'Congratulations, Your Home Loan is Approved!!') | |
| else: | |
| st.error('We are extremely sorry to inform you that you Home Loan is not approved. Please reach out to nearest branch for further clarification') | |