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| import pickle | |
| import pandas as pd | |
| import shap | |
| from shap.plots._force_matplotlib import draw_additive_plot | |
| import gradio as gr | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| # Load model | |
| loaded_model = pickle.load(open("default_xgb.pkl", 'rb')) | |
| # SHAP Explainer | |
| explainer = shap.Explainer(loaded_model) | |
| # Load images for different profiles | |
| img_mary = Image.open("mary_profile.png") | |
| img_james = Image.open("james_profile.png") | |
| img_landon = Image.open("landon_profile.png") | |
| # Define main function | |
| def main_func(LIMIT_BAL, | |
| PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, | |
| b1, b2, b3, b4, b5, b6, | |
| profile=""): | |
| new_row = pd.DataFrame.from_dict({ | |
| 'LIMIT_BAL': [LIMIT_BAL], | |
| 'PAY_0': [PAY_0], | |
| 'PAY_2': [PAY_2], | |
| 'PAY_3': [PAY_3], | |
| 'PAY_4': [PAY_4], | |
| 'PAY_5': [PAY_5], | |
| 'PAY_6': [PAY_6], | |
| 'b1': [b1], | |
| 'b2': [b2], | |
| 'b3': [b3], | |
| 'b4': [b4], | |
| 'b5': [b5], | |
| 'b6': [b6] | |
| }) | |
| prob = loaded_model.predict_proba(new_row)[0][1] | |
| shap_values = explainer(new_row) | |
| plt.figure(figsize=(10, 6)) | |
| shap.plots.bar(shap_values[0], max_display=10, show=False) | |
| plt.tight_layout() | |
| local_plot = plt.gcf() | |
| plt.close() | |
| # Select image based on profile | |
| if profile == "Mary": | |
| image = img_mary | |
| elif profile == "James": | |
| image = img_james | |
| elif profile == "Landon": | |
| image = img_landon | |
| else: | |
| image = None | |
| return {"No Default": 1 - float(prob), "Default": float(prob)}, local_plot, image | |
| # UI Text | |
| title = "**Credit Card Default Risk Predictor** 📊" | |
| description1 = """ | |
| This app uses financial data such as credit limits, repayment behavior, and bill balances to estimate the probability that a person may **default on their next credit payment**. | |
| """ | |
| description2 = """ | |
| To use the app, either adjust the values or click one of the examples. Then click on **Analyze** to get predictions and interpretations. 🔍 | |
| """ | |
| # Launch Gradio app | |
| with gr.Blocks(title=title) as demo: | |
| gr.Markdown(f"## {title}") | |
| gr.Markdown(description1) | |
| gr.Markdown("---") | |
| gr.Markdown(description2) | |
| gr.Markdown("---") | |
| with gr.Row(): | |
| with gr.Column(): | |
| LIMIT_BAL = gr.Slider(label="Credit Limit (LIMIT_BAL)", minimum=10000, maximum=1000000, value=150000, step=5000) | |
| PAY_0 = gr.Slider(label="PAY_0 (Most recent payment status)", minimum=-2, maximum=8, value=0, step=1) | |
| PAY_2 = gr.Slider(label="PAY_2", minimum=-2, maximum=8, value=-1, step=1) | |
| PAY_3 = gr.Slider(label="PAY_3", minimum=-2, maximum=8, value=-1, step=1) | |
| PAY_4 = gr.Slider(label="PAY_4", minimum=-2, maximum=8, value=-1, step=1) | |
| PAY_5 = gr.Slider(label="PAY_5", minimum=-2, maximum=8, value=-1, step=1) | |
| PAY_6 = gr.Slider(label="PAY_6", minimum=-2, maximum=8, value=-1, step=1) | |
| b1 = gr.Slider(label="Balance 1 (b1)", minimum=-100000, maximum=100000, value=5000, step=1000) | |
| b2 = gr.Slider(label="Balance 2 (b2)", minimum=-100000, maximum=100000, value=4500, step=1000) | |
| b3 = gr.Slider(label="Balance 3 (b3)", minimum=-100000, maximum=100000, value=4700, step=1000) | |
| b4 = gr.Slider(label="Balance 4 (b4)", minimum=-100000, maximum=100000, value=4300, step=1000) | |
| b5 = gr.Slider(label="Balance 5 (b5)", minimum=-100000, maximum=100000, value=4000, step=1000) | |
| b6 = gr.Slider(label="Balance 6 (b6)", minimum=-100000, maximum=100000, value=3900, step=1000) | |
| profile_input = gr.Textbox(visible=False) | |
| submit_btn = gr.Button("Analyze") | |
| with gr.Column(visible=True, scale=1, min_width=600): | |
| label = gr.Label(label="Predicted Default Risk") | |
| local_plot = gr.Plot(label="SHAP Explanation Plot") | |
| profile_image = gr.Image(label="Customer Profile Image", width=400, height=400) | |
| submit_btn.click( | |
| main_func, | |
| [LIMIT_BAL, | |
| PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, | |
| b1, b2, b3, b4, b5, b6, | |
| profile_input], | |
| [label, local_plot, profile_image], | |
| api_name="Default_Risk" | |
| ) | |
| gr.Markdown("""### Example inputs to try: 3 credit profiles | |
| - Mary: Did not miss payments in last 6 months, with a decreasing remaining balance and credit limit = 200,000. | |
| - James: Constantly missed payments in last 6 months, with an increasing remaining balance and credit limit = 80,000. | |
| - Landon: Missed payments in early months, with high fluctuations in remaining balance and credit limit = 300,000. | |
| """) | |
| gr.Examples( | |
| examples=[ | |
| [200000, -1, -1, -1, -1, -1, -1, 7500, 10000, 12500, 15000, 17500, 20000, "Mary"], | |
| [80000, 2, 2, 2, 3, 2, 2, 20000, 17500, 15500, 12500, 10000, 7500, "James"], | |
| [300000, -1, 1, -1, 1, 2, 2, 15000, 20000, 0, 10000, 25000, 10000, "Landon"] | |
| ], | |
| inputs=[LIMIT_BAL, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, | |
| b1, b2, b3, b4, b5, b6, profile_input], | |
| outputs=[label, local_plot, profile_image], | |
| fn=main_func, | |
| cache_examples=True | |
| ) | |
| demo.launch() |