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app.py

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+import skops.io as sio
+import gradio as gr
+
+pipe = sio.load("bank_marketing_pipe.skops", trusted=True)
+
+classes = [
+    "Not Subscribe",
+    "Subscribe"]
+
+
+def classifier(age, job, marital, education, default, balance, housing,loan, contact):
+    pred = pipe.predict([[age, job, marital, education, default, balance, housing,loan, contact]])[0]
+    label = f"Predicted output: **{classes[pred]}**"
+    return label
+
+
+inputs = [
+    gr.Slider(10, 90, step=1, label="Age"),
+    gr.Dropdown(["admin.","unknown","unemployed","management","housemaid","entrepreneur","student","blue-collar",
+                 "self-employed","retired","technician","services"], label="Job", multiselect=False),
+    gr.Dropdown(["married","divorced","single"], label="Marital", multiselect=False),
+    gr.Dropdown(["unknown","secondary","primary","tertiary"], label="Education", multiselect=False),
+    gr.Radio(["yes","no"], label="Default", info='has credit in default?'),
+    gr.Slider(-100000, 100000, step=1, label="Balance"),
+    gr.Radio(["yes","no"], label="Housing", info='has housing loan?'),
+    gr.Radio(["yes","no"], label="Loan", info='has personal loan?'),
+    gr.Dropdown(["unknown","telephone","cellular"], label="Contact")
+]
+
+outputs = [gr.Label(num_top_classes=2)]
+
+title = "Deposit Subscription Prediction"
+description = "Enter the details to identify where or not the customer is subscribed or not subscribed for deposit"
+
+gr.Interface(
+    fn=classifier,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    description=description,
+).launch()

requirements .txt

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+skops==0.8.0
+scikit-learn==1.2.2