import joblib
import pandas as pd
import streamlit as st 


model = joblib.load('model.joblib')
unique_values = joblib.load('unique_values.joblib')
    
unique_education =  unique_values["education"]
unique_self_employed =  unique_values["self_employed"]


def main():
    st.title("Loan Approve Prediction")

    with st.form("questionaire"):
        education = st.selectbox("Education", unique_education)
        self_employed = st.selectbox("Self Employed", unique_self_employed)
        no_of_dependents = st.slider("Number of Dependents", min_value=0 ,max_value=10)
        income_annum = st.slider("Income Per Year",min_value=200000, max_value=1000000)
        loan_amount = st.slider("Loan Amount", min_value=300000, max_value=40000000)
        loan_term = st.slider("Loan Term (Year)", min_value=2, max_value=20)
        cibil_score = st.slider("CIBIL Score", min_value=300, max_value=900)
        residential_assets_value = st.slider("Residential Assets Value", min_value=-100000, max_value=30000000)
        commercial_assets_value = st.slider("Commercial Assets Value", min_value=0,max_value=20000000)
        luxury_assets_value = st.slider("Luxury Assets Value", min_value=0,max_value=50000000)
        bank_asset_value = st.slider("Bank Assets Value", min_value=0,max_value=20000000)

        clicked = st.form_submit_button("Loan Approve Prediction")
        if clicked:
            result=model.predict(pd.DataFrame({"no_of_dependents": [no_of_dependents],
                                               "education": [education],
                                               "self_employed": [self_employed],
                                               "income_annum": [income_annum],
                                               "loan_amount": [loan_amount],
                                               "loan_term": [loan_term],
                                               "cibil_score": [cibil_score],
                                               "residential_assets_value": [residential_assets_value],
                                               "commercial_assets_value": [commercial_assets_value],
                                               "luxury_assets_value": [luxury_assets_value],
                                               "bank_asset_value": [bank_asset_value]
                                              }))
            result = 'Approved' if result[0] == 1 else 'Declined'
            st.success('The prediction of loan approval is  {}'.format(result))

if __name__=='__main__':
    main()