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