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96f4571 75b9644 96f4571 75b9644 96f4571 75b9644 96f4571 e04d581 96f4571 75b9644 96f4571 75b9644 96f4571 e04d581 96f4571 75b9644 96f4571 e04d581 96f4571 e04d581 96f4571 75b9644 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | """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 # noqa: E402
from credit_risk.config import FEATURE_GROUPS, NOT_SELECTED_LABEL # noqa: E402
from credit_risk.features import build_inference_frame # noqa: E402
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()
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