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app.py
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app.py
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import dash
from dash import dcc,html
import plotly.graph_objs as go
import pickle
import json
from dash.dependencies import Input, Output, State
########### Define your variables ######
myheading1='Predicting Mortgage Loan Approval'
image1='assets/rocauc.html'
tabtitle = 'Loan Prediction'
sourceurl = 'https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/'
githublink = 'https://github.com/plotly-dash-apps/503-log-reg-loans-simple'
########### open the json file ######
with open('assets/rocauc.json', 'r') as f:
fig=json.load(f)
########### open the pickle file ######
filename = open('analysis/loan_approval_logistic_model.pkl', 'rb')
unpickled_model = pickle.load(filename)
filename.close()
########### Initiate the app
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
server = app.server
app.title=tabtitle
########### Set up the layout
app.layout = html.Div(children=[
html.H1(myheading1),
html.Div([
html.Div(
[dcc.Graph(figure=fig, id='fig1')
], className='six columns'),
html.Div([
html.H3("Features"),
html.Div('Credit History:'),
dcc.Input(id='Credit_History', value=1, type='number', min=0, max=1, step=1),
html.Div('Loan Amount (in thousands):'),
dcc.Input(id='LoanAmount', value=130, type='number', min=10, max=800, step=10),
html.Div('Term (in months)'),
dcc.Input(id='Loan_Amount_Term', value=360, type='number', min=120, max=480, step=10),
html.Div('Applicant Income (in dollars)'),
dcc.Input(id='ApplicantIncome', value=5000, type='number', min=0, max=100000, step=500),
html.Div('Probability Threshold for Loan Approval'),
dcc.Input(id='Threshold', value=50, type='number', min=0, max=100, step=1),
], className='three columns'),
html.Div([
html.H3('Predictions'),
html.Div('Predicted Status:'),
html.Div(id='PredResults'),
html.Br(),
html.Div('Probability of Approval:'),
html.Div(id='ApprovalProb'),
html.Br(),
html.Div('Probability of Denial:'),
html.Div(id='DenialProb')
], className='three columns')
], className='twelve columns',
),
html.Br(),
html.A('Code on Github', href=githublink),
html.Br(),
html.A("Data Source", href=sourceurl),
]
)
######### Define Callback
@app.callback(
[Output(component_id='PredResults', component_property='children'),
Output(component_id='ApprovalProb', component_property='children'),
Output(component_id='DenialProb', component_property='children'),
],
[Input(component_id='Credit_History', component_property='value'),
Input(component_id='LoanAmount', component_property='value'),
Input(component_id='Loan_Amount_Term', component_property='value'),
Input(component_id='ApplicantIncome', component_property='value'),
Input(component_id='Threshold', component_property='value')
])
def prediction_function(Credit_History, LoanAmount, Loan_Amount_Term, ApplicantIncome, Threshold):
try:
data = [[Credit_History, LoanAmount, Loan_Amount_Term, ApplicantIncome]]
rawprob=100*unpickled_model.predict_proba(data)[0][1]
func = lambda y: 'Approved' if int(rawprob)>Threshold else 'Denied'
formatted_y = func(rawprob)
deny_prob=unpickled_model.predict_proba(data)[0][0]*100
formatted_deny_prob = "{:,.2f}%".format(deny_prob)
app_prob=unpickled_model.predict_proba(data)[0][1]*100
formatted_app_prob = "{:,.2f}%".format(app_prob)
return formatted_y, formatted_app_prob, formatted_deny_prob
except:
return "inadequate inputs", "inadequate inputs"
############ Deploy
if __name__ == '__main__':
app.run_server(debug=True)