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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,emp_var_rate,cons_price_idx,cons_conf_idx): | |
| 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, | |
| 'emp_var_rate': emp_var_rate, | |
| 'cons_price_idx': cons_price_idx, | |
| 'cons_conf_idx': cons_conf_idx, | |
| } | |
| print(customer) | |
| df_transformed = dv.transform([customer]) | |
| prediction = model.predict_proba(df_transformed)[0] | |
| # return prediction[1] | |
| return {'Failure(!Deposit)': prediction[0], 'Success(Deposit)': prediction[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)}') | |
| # In[4]: | |
| def feature_importance(age, job, marital, education, default, housing, | |
| loan, contact, month,day_of_week,campaign,pdays, | |
| previous,poutcome,emp_var_rate,cons_price_idx,cons_conf_idx): | |
| 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, | |
| 'emp_var_rate': emp_var_rate, | |
| 'cons_price_idx': cons_price_idx, | |
| 'cons_conf_idx': cons_conf_idx, | |
| } | |
| df_transformed = pd.DataFrame(dv.transform(customer)) | |
| df_transformed.columns = dv.get_feature_names_out() | |
| important_features = pd.DataFrame({'cols':df_transformed.columns, 'imp':model.feature_importances_} | |
| ).sort_values('imp', ascending=False) | |
| #return important_features.plot('cols', 'imp', 'barh', figsize=(12,7), legend=False) | |
| return print(important_features.to_json()) | |
| age = gr.inputs.Slider(minimum=1,default = 35, maximum=100, step=1,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.Radio(["Yes", "No","Unknown"],label = 'Default',type="index") | |
| housing = gr.inputs.Radio(choices=["Yes", "No","Unknown"],label = 'Housing',type="index") | |
| loan = gr.inputs.Radio(["Yes", "No","Unknown"],type="index",label = 'Loan') | |
| contact = gr.inputs.Radio(["Telephone", "Cellular"],type = "index",label = 'Contact') | |
| month = gr.inputs.Dropdown(choices=['Mar', 'Apr','May', 'Jun', 'Jul', 'Aug','Sep','Oct', 'Nov', 'Dec'],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, step = 1,label = 'Campaign') | |
| pdays = gr.inputs.Slider(minimum=0,default = 0, maximum=27, step = 1,label = 'Last Contact(in days)') | |
| previous = gr.inputs.Slider(minimum=0,default = 0, maximum=7, step = 1,label = 'Previous Contacts') | |
| poutcome = gr.inputs.Radio(["Nonexistent", "Failure","Success"],label = 'Previous Outcome',type="index") | |
| emp_var_rate = gr.inputs.Slider(minimum=-3,default = 1, maximum=1,step= 1, label = 'Employment Variation Rate') | |
| cons_price_idx = gr.inputs.Slider(minimum=92,default = 94, maximum=95,step = 1, label = 'Consumer Price Index ') | |
| cons_conf_idx = gr.inputs.Slider(minimum=-51,default = -42, maximum=-27, step = 1, label = 'Consumer Confidence Index') | |
| iface = gr.Interface(predict,[age, job, marital, education, default, housing, | |
| loan, contact, month,day_of_week,campaign,pdays, | |
| previous,poutcome,emp_var_rate,cons_price_idx,cons_conf_idx], | |
| outputs = "label", | |
| interpretation="default" | |
| ) | |
| iface.launch(share=True , debug=True) | |