Create app.py

app.py

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+import pickle5
+import streamlit as st
+
+# loading the trained model
+pickle_in = open('classifier.pkl', 'rb')
+classifier = pickle5.load(pickle_in)
+
+
+@st.cache()
+# defining the function which will make the prediction using the data which the user inputs
+def prediction(Gender, Married, ApplicantIncome, LoanAmount, Credit_History):
+	# Pre-processing user input
+	if Gender == "Male":
+		Gender = 0
+	else:
+		Gender = 1
+
+	if Married == "Unmarried":
+		Married = 0
+	else:
+		Married = 1
+
+	if Credit_History == "Unclear Debts":
+		Credit_History = 0
+	else:
+		Credit_History = 1
+
+	LoanAmount = LoanAmount / 1000
+
+	# Making predictions
+	prediction = classifier.predict(
+		[[Gender, Married, ApplicantIncome, LoanAmount, Credit_History]])
+
+	if prediction == 0:
+		pred = 'Rejected'
+	else:
+		pred = 'Approved'
+	return pred
+
+
+# this is the main function in which we define our webpage
+def main():
+	# front end elements of the web page
+	st.title("Streamlit Loan Prediction ML App By DSC PSAU ")
+
+	# display the front end aspect
+
+	# following lines create boxes in which user can enter data required to make prediction
+	Gender = st.selectbox('Gender', ("Male", "Female"))
+	Married = st.selectbox('Marital Status', ("Unmarried", "Married"))
+	ApplicantIncome = st.number_input("Applicants monthly income")
+	LoanAmount = st.number_input("Total loan amount")
+	Credit_History = st.selectbox('Credit_History', ("Unclear Debts", "No Unclear Debts"))
+	result = ""
+
+	# when 'Predict' is clicked, make the prediction and store it
+	if st.button("Predict"):
+		result = prediction(Gender, Married, ApplicantIncome, LoanAmount, Credit_History)
+		st.success('Your loan is {}'.format(result))
+		print(LoanAmount)
+
+
+if __name__ == '__main__':
+	main()