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import gradio as gr
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
# 載入完整資料
df = pd.read_csv("bankloan.csv")
df = df.drop(columns=["ID", "ZIP.Code"])
X = df.drop(columns=["Personal.Loan"])
y = df["Personal.Loan"]
# 建立模型
model = DecisionTreeClassifier(random_state=42)
model.fit(X, y)
def predict(age, exp, income, family, ccavg, edu, mort, sec, cd, online, credit):
input_data = pd.DataFrame([{
"Age": age,
"Experience": exp,
"Income": income,
"Family": family,
"CCAvg": ccavg,
"Education": edu,
"Mortgage": mort,
"Securities.Account": sec,
"CD.Account": cd,
"Online": online,
"CreditCard": credit
}])
pred = model.predict(input_data)[0]
return "核貸 ✅" if pred == 1 else "不核貸 ❌"
demo = gr.Interface(
fn=predict,
inputs=[
gr.Number(label="年齡 Age"),
gr.Number(label="工作年資 Experience"),
gr.Number(label="收入 Income"),
gr.Number(label="家庭人數 Family"),
gr.Number(label="信用卡平均月花費 CCAvg"),
gr.Number(label="教育程度 Education"),
gr.Number(label="房貸金額 Mortgage"),
gr.Number(label="是否有證券帳戶 (0/1)"),
gr.Number(label="是否有定存帳戶 (0/1)"),
gr.Number(label="是否使用線上服務 (0/1)"),
gr.Number(label="是否持有信用卡 (0/1)")
],
outputs="text",
title="Loan Approval Predictor",
description="使用 bankloan.csv 資料訓練的決策樹模型，預測是否核貸"
)
demo.launch()
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