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| import streamlit as st | |
| import joblib | |
| import pandas as pd | |
| from streamlit_option_menu import option_menu | |
| from reportlab.pdfgen import canvas | |
| from reportlab.lib.pagesizes import letter | |
| from reportlab.lib import colors | |
| from io import BytesIO | |
| import base64 | |
| # Load the trained model | |
| model = joblib.load('loan_approval_model.pkl') | |
| # Define the function to make predictions | |
| def predict_loan_approval(features): | |
| prediction = model.predict([features]) | |
| return prediction[0] | |
| # Streamlit app | |
| st.title("Loan Approval Prediction System") | |
| # Input fields for user data | |
| st.sidebar.header("Menu") | |
| if __name__ == '__main__': | |
| st.markdown("## Loan Approval Prediction System") | |
| with st.sidebar: | |
| selected = option_menu('Loan Approval Prediction System', | |
| ['Predict Loan Approval', | |
| 'Our Prediction Records', | |
| 'About Me'], | |
| icons=['info','book','info'], | |
| default_index=0) | |
| if selected =="Predict Loan Approval": | |
| # Example input fields | |
| Name = st.text_input('Enter your name:') | |
| gender = st.selectbox("Gender", options=[0, 1], format_func=lambda x: 'Male' if x == 1 else 'Female') | |
| married = st.selectbox("Married", options=[0, 1], format_func=lambda x: 'Yes' if x == 1 else 'No') | |
| education = st.selectbox("Education", options=[0, 1], format_func=lambda x: 'Graduate' if x == 1 else 'Not Graduate') | |
| self_employed = st.selectbox("Self Employed", options=[0, 1], format_func=lambda x: 'Yes' if x == 1 else 'No') | |
| applicant_income = st.number_input("Applicant Income", min_value=0, value=50000) | |
| coapplicant_income = st.number_input("Coapplicant Income", min_value=0.0, value=0.0) | |
| loan_amount = st.number_input("Loan Amount", min_value=0.0, value=10000.0) | |
| loan_amount_term = st.number_input("Loan Amount Term", min_value=0.0, value=360.0) | |
| credit_history = st.selectbox("Credit History", options=[0.0, 1.0], format_func=lambda x: 'No' if x == 0.0 else 'Yes') | |
| property_area = st.selectbox("Property Area", options=[0, 1, 2], format_func=lambda x: ['Urban', 'Semiurban', 'Rural'][x]) | |
| dependents = st.number_input("Dependents", min_value=0, value=0) | |
| # Collect inputs into a list | |
| user_input = [gender, married, education, self_employed, applicant_income, coapplicant_income, | |
| loan_amount, loan_amount_term, credit_history, property_area, dependents] | |
| # Predict | |
| if st.button("Predict"): | |
| result = predict_loan_approval(user_input) | |
| if result == 1: | |
| st.success("Loan Approved") | |
| else: | |
| st.error("Loan Denied") | |
| f = open("user_records.txt", "a") | |
| f.write("\n") | |
| new_data = str([Name, gender, married, education, self_employed, applicant_income, coapplicant_income, | |
| loan_amount, loan_amount_term, credit_history, property_area, dependents, result]) | |
| leng = len(new_data) | |
| f.write(new_data[1:leng-1]) | |
| f.close() | |
| def generate_report(Name, result): | |
| buffer = BytesIO() | |
| c = canvas.Canvas(buffer, pagesize=letter) | |
| width, height = letter | |
| c.drawString(100, height-50, "Loan Prediction Report") | |
| c.drawString(100, height-70, "--------------------------------------------") | |
| c.drawString(100, height-90, f"Name: {Name}") | |
| c.drawString(100, height-110, f"Gender: {gender}") | |
| c.drawString(100, height-130, f"Married?: {married}") | |
| c.drawString(100, height-150, f"education Status: {education}") | |
| c.drawString(100, height-170, f"Self Employed?): {self_employed}") | |
| c.drawString(100, height-190, f"Income: {applicant_income}") | |
| c.drawString(100, height-210, f"Gurrantor Income: {coapplicant_income}") | |
| c.drawString(100, height-230, f"Loan Amount: {loan_amount}") | |
| c.drawString(100, height-250, f"Credit History: {credit_history}") | |
| c.drawString(100, height-270, f"Property: {property_area}") | |
| c.drawString(100, height-290, f"Dependents: {dependents}") | |
| c.drawString(100, height-310, "--------------------------------------------") | |
| c.drawString(100, height-330, "Prediction:") | |
| if result == 1: | |
| c.setFillColorRGB(0, 1, 0) # Red color | |
| prediction_text = "Loan Approved" | |
| else: | |
| c.setFillColorRGB(1, 0, 0) # Green color | |
| prediction_text = "Loan Not Approved" | |
| c.drawString(100, height-345, f"{prediction_text}") | |
| c.setFillColor(colors.black) | |
| c.setFont("Helvetica", 10) | |
| footnote = "Note: The prediction is based on probability. Actual results may vary. Please consult an expert for a detailed check." | |
| c.drawString(100, 200, footnote) | |
| c.showPage() | |
| c.save() | |
| pdf_bytes = buffer.getvalue() | |
| buffer.close() | |
| return pdf_bytes | |
| pdf_bytes = generate_report(Name, result) | |
| pdf_base64 = base64.b64encode(pdf_bytes).decode('utf-8') | |
| pdf_display = f'<a href="data:application/pdf;base64,{pdf_base64}" download="Loan_Approval_report_{Name}.pdf">Download Report</a>' | |
| st.markdown(pdf_display, unsafe_allow_html=True) | |
| if selected == "Our Prediction Records": | |
| st.markdown("<h3 style='text-align: center;'>PREDICTION RECORDS OF OUR PREVIOUS USERS</h1>", unsafe_allow_html=True) | |
| f = pd.read_csv("user_records.txt") | |
| #st.table(f) | |
| st.table(f.style.set_table_attributes('style="width:100%;"')) | |
| st.markdown("____") | |
| st.write("All the records are stored only for academic and research purpose & will not be used for any other means.") | |
| if selected == "About Me": | |
| st.markdown("<h2 style='text-align: center;'>ABOUT</h2>", unsafe_allow_html=True) | |
| st.markdown("____") | |
| st.markdown("<p style='text-align: center;'>This is an academic project made by B.Tech Computer Science And Engineering 3rd year student.</p>", unsafe_allow_html=True) | |
| st.markdown("____") | |
| st.markdown("<h4 style='text-align: center;'>Developed and maintained by</h4>", unsafe_allow_html=True) | |
| st.markdown("<p style='text-align: center;'>Subrata Bhuin</p>", unsafe_allow_html=True) | |
| st.markdown("<p style='text-align: center;'>[email protected]</p>", unsafe_allow_html=True) | |
| st.markdown("____") |