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| import gradio as gr | |
| from joblib import load | |
| import pandas as pd | |
| dv , model = load("train_model.joblib") | |
| # creating a predict function to be passed into gradio UI | |
| def predict(age, job, marital, education, default, housing, | |
| loan, contact, month,day_of_week,campaign,pdays, | |
| previous,poutcome,cons_price_idx,cons_conf_idx,emp_var_rate): | |
| customer = { | |
| 'age': age, | |
| 'job': job, | |
| 'marital': marital, | |
| 'education': education, | |
| 'default': default, | |
| 'housing': housing, | |
| 'loan': loan, | |
| 'contact': contact, | |
| 'month': month, | |
| 'day_of_week': day_of_week, | |
| 'campaign': campaign, | |
| 'pdays': pdays, | |
| 'previous': previous, | |
| 'poutcome': poutcome, | |
| 'cons_price_idx': cons_price_idx, | |
| 'cons_conf_idx': cons_conf_idx, | |
| 'emp_var_rate': emp_var_rate | |
| } | |
| print(customer) | |
| df_transformed = dv.transform([customer]) | |
| prediction = model.predict_proba(df_transformed)[:,1] | |
| # Desposited = prediction >= 0.50 | |
| # result = { | |
| # "deposit_probability": float(prediction), | |
| # "Deposited": bool(Deposited) | |
| # } | |
| print(f' The probabilty of depositing in the bank is : {str(prediction)}') | |
| return str(prediction) | |
| age = gr.inputs.Slider(minimum=1,default = 35, maximum=100, label = 'age') #default=data['age'].mean() | |
| job = gr.inputs.Dropdown(choices=["housemaid", "services","admin.","blue-collar","technician", | |
| "retired","management","unemployed","self-employed","unknown", | |
| "entrepreneur","student"],label = 'job') | |
| marital = gr.inputs.Dropdown(choices=["married", "single","divorced","unknown"],label = 'marital') | |
| education = gr.inputs.Dropdown(choices=["basic.4y", "high.school","basic.6y","basic.9y","professional.course", | |
| "unknown","university.degree","illiterate"],label = 'education') | |
| default = gr.inputs.Dropdown(choices=["yes", "no","unknown"],label = 'default') | |
| housing = gr.inputs.Dropdown(choices=["yes", "no","unknown"],label = 'housing') | |
| loan = gr.inputs.Dropdown(choices=["yes", "no","unknown"],label = 'loan') | |
| contact = gr.inputs.Dropdown(choices=["telephone", "cellular"],label = 'contact') | |
| month = gr.inputs.Dropdown(choices=['may', 'jun', 'jul', 'aug', 'oct', 'nov', 'dec', | |
| 'mar', 'apr','sep'],label = 'month') | |
| day_of_week = gr.inputs.Dropdown(choices=['mon', 'tue', 'wed', 'thu', 'fri'],label = 'day_of_week') | |
| campaign = gr.inputs.Slider(minimum=1,default = 2, maximum=56, label = 'campaign') | |
| pdays = gr.inputs.Slider(minimum=0,default = 0, maximum=27, label = 'pdays') | |
| previous = gr.inputs.Slider(minimum=0,default = 0, maximum=7, label = 'previous') | |
| poutcome = gr.inputs.Dropdown(choices=["nonexistent", "failure","success"],label = 'poutcome') | |
| cons_price_idx = gr.inputs.Slider(minimum=92,default = 94, maximum=95, label = 'cons_price_idx') | |
| cons_conf_idx = gr.inputs.Slider(minimum=-51,default = -42, maximum=-27, label = 'cons_conf_idx') | |
| emp_var_rate = gr.inputs.Slider(minimum=4964,default = 5191, maximum=5228, label = 'emp_var_rate') | |
| iface = gr.Interface(predict,[age, job, marital, education, default, housing, | |
| loan, contact, month,day_of_week,campaign,pdays, | |
| previous,poutcome,cons_price_idx,cons_conf_idx,emp_var_rate], | |
| outputs = "number", | |
| interpretation="default" | |
| ) | |
| iface.launch(share=True) | |