| """Gradio app for credit worthiness inference.""" |
|
|
| from __future__ import annotations |
|
|
| import sys |
| from pathlib import Path |
|
|
| import gradio as gr |
|
|
| PROJECT_ROOT = Path(__file__).resolve().parent |
| sys.path.insert(0, str(PROJECT_ROOT / "src")) |
|
|
| from credit_risk.app_support import format_metrics_markdown, load_artifacts |
| from credit_risk.config import FEATURE_GROUPS, NOT_SELECTED_LABEL |
| from credit_risk.features import build_inference_frame |
|
|
| ARTIFACTS = load_artifacts() |
|
|
|
|
| def predict_credit_worthiness(name: str, *selections: str) -> tuple[str, str, object, object, object]: |
| """Predict loan eligibility from UI selections.""" |
| selection_by_group = {} |
| for group, selected_label in zip(FEATURE_GROUPS, selections): |
| if selected_label == NOT_SELECTED_LABEL: |
| selection_by_group[group.name] = None |
| else: |
| selection_by_group[group.name] = selected_label |
|
|
| inference_frame = build_inference_frame(selection_by_group) |
| prediction = int(ARTIFACTS.model.predict(inference_frame)[0]) |
|
|
| if hasattr(ARTIFACTS.model, "predict_proba"): |
| probabilities = ARTIFACTS.model.predict_proba(inference_frame)[0] |
| confidence = max(probabilities) |
| else: |
| confidence = None |
|
|
| user_name = (name or "there").strip() |
| if prediction == 1: |
| verdict = "eligible for the loan" |
| else: |
| verdict = "not eligible for the loan at the moment" |
|
|
| if confidence is None: |
| prediction_text = f"Hi {user_name}. According to the model, your client is {verdict}." |
| else: |
| prediction_text = ( |
| f"Hi {user_name}. According to the model, your client is {verdict}. " |
| f"(Confidence: {confidence:.2%})" |
| ) |
|
|
| return ( |
| prediction_text, |
| format_metrics_markdown(ARTIFACTS.metrics), |
| ARTIFACTS.feature_importance_plot, |
| ARTIFACTS.confusion_matrix_plot, |
| ARTIFACTS.roc_curve_plot, |
| ) |
|
|
|
|
| with gr.Blocks(title="Credit Worthiness Risk Classification") as demo: |
| gr.Markdown("# Credit Worthiness Risk Classification") |
| gr.Markdown( |
| "Select the option that best describes the client in each section, then run prediction." |
| ) |
|
|
| name_input = gr.Textbox(label="Analyst Name", placeholder="Your name") |
| selection_components = [] |
|
|
| with gr.Accordion("Client Profile Inputs", open=True): |
| for group in FEATURE_GROUPS: |
| with gr.Row(): |
| component = gr.Radio( |
| choices=[NOT_SELECTED_LABEL, *group.labels], |
| value=NOT_SELECTED_LABEL, |
| label=group.name, |
| ) |
| selection_components.append(component) |
|
|
| predict_button = gr.Button("Predict") |
| prediction_output = gr.Textbox(label="Prediction", interactive=False) |
| metrics_output = gr.Markdown(label="Metrics") |
| feature_plot_output = gr.Plot(label="Feature Importance") |
| matrix_plot_output = gr.Plot(label="Confusion Matrix") |
| roc_plot_output = gr.Plot(label="ROC Curve") |
|
|
| predict_button.click( |
| fn=predict_credit_worthiness, |
| inputs=[name_input, *selection_components], |
| outputs=[prediction_output, metrics_output, feature_plot_output, matrix_plot_output, roc_plot_output], |
| ) |
|
|
| demo.launch() |
|
|
