| 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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|
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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):
|
|
|
| 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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|
|
|
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| preprocessed_data = preprocessor.transform(data)
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|
|
|
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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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|
|
| 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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|
|
