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import gradio as gr
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import pandas as pd
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import joblib
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import os
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preprocessor_path = os.path.join('.', 'preprocessor.joblib')
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model_path = os.path.join('.', 'Best_model.joblib')
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preprocessor = joblib.load('C:\Users\USER\Documents\DS\TMP\preprocessor.joblib')
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best_model = joblib.load('C:\Users\USER\Documents\DS\TMP\Best_model.joblib')
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def predict(age, job, marital, education, default, housing, loan, contact, month, day_of_week, duration, campaign, pdays, previous, poutcome, emp_var_rate, cons_price_idx, cons_conf_idx, euribor3m, nr_employed):
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data = pd.DataFrame({
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'age': [age],
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'job': [job],
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'marital': [marital],
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'education': [education],
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'default': [default],
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'housing': [housing],
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'loan': [loan],
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'contact': [contact],
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'month': [month],
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'day_of_week': [day_of_week],
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'duration': [duration],
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'campaign': [campaign],
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'pdays': [pdays],
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'previous': [previous],
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'poutcome': [poutcome],
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'emp.var.rate': [emp_var_rate],
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'cons.price.idx': [cons_price_idx],
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'cons.conf.idx': [cons_conf_idx],
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'euribor3m': [euribor3m],
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'nr.employed': [nr_employed]
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})
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preprocessed_data = preprocessor.transform(data)
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prediction = best_model.predict(preprocessed_data)
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probability = best_model.predict_proba(preprocessed_data)
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return {
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"Prediction": prediction[0],
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"Probability (Yes)": probability[0][1],
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"Probability (No)": probability[0][0]
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}
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def gradio_interface():
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with gr.Blocks() as app:
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gr.Markdown("# Bank Marketing Campaign Prediction")
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with gr.Row():
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age = gr.Number(label="Age", value=30)
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job = gr.Dropdown(["housemaid", "services", "admin.", "blue-collar", "technician", "retired", "management", "unemployed", "self-employed", "unknown", "entrepreneur", "student"], label="Job")
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marital = gr.Dropdown(["married", "single", "divorced", "unknown"], label="Marital Status")
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education = gr.Dropdown(["basic.4y", "high.school", "basic.6y", "basic.9y", "professional.course", "unknown", "university.degree", "illiterate", "tertiary", "secondary", "primary"], label="Education")
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with gr.Row():
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default = gr.Dropdown(["no", "unknown", "yes"], label="Default")
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housing = gr.Dropdown(["no", "yes", "unknown"], label="Housing Loan")
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loan = gr.Dropdown(["no", "yes", "unknown"], label="Personal Loan")
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contact = gr.Dropdown(["telephone", "cellular", "unknown"], label="Contact Type")
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with gr.Row():
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month = gr.Dropdown(["may", "jun", "jul", "aug", "oct", "nov", "dec", "mar", "apr", "sep", "jan", "feb"], label="Month")
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day_of_week = gr.Dropdown(["mon", "tue", "wed", "thu", "fri"], label="Day of Week")
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with gr.Row():
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duration = gr.Number(label="Call Duration (seconds)", value=100)
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campaign = gr.Number(label="Number of Contacts during Campaign", value=1)
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pdays = gr.Number(label="Days since Last Contact", value=999)
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previous = gr.Number(label="Number of Contacts before Campaign", value=0)
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poutcome = gr.Dropdown(["nonexistent", "failure", "success", "unknown", "other"], label="Previous Outcome")
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with gr.Row():
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emp_var_rate = gr.Number(label="Employment Variation Rate", value=1.1)
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cons_price_idx = gr.Number(label="Consumer Price Index", value=93.994)
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cons_conf_idx = gr.Number(label="Consumer Confidence Index", value=-36.4)
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euribor3m = gr.Number(label="Euribor 3-Month Rate", value=4.857)
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nr_employed = gr.Number(label="Number of Employees", value=5191.0)
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predict_btn = gr.Button("Predict")
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output = gr.JSON()
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predict_btn.click(
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predict,
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inputs=[age, job, marital, education, default, housing, loan, contact, month, day_of_week, duration, campaign, pdays, previous, poutcome, emp_var_rate, cons_price_idx, cons_conf_idx, euribor3m, nr_employed],
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outputs=output
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)
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return app
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if __name__ == "__main__":
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app = gradio_interface()
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app.launch()
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