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| from tensorflow.keras.models import load_model | |
| from sklearn.preprocessing import MinMaxScaler | |
| import pandas as pd | |
| saved_model = load_model('churn_model2.h5') | |
| scaler = MinMaxScaler() | |
| def churn_prediction(CreditScore,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Location): | |
| Geography_France,Geography_Germany,Geography_Spain=0,0,0 | |
| df = pd.DataFrame.from_dict({ | |
| "Credit score":[CreditScore], | |
| "Is female?":[1 if Gender=='Female' else 0], | |
| "Age":[Age], | |
| 'Tenure':[Tenure], | |
| 'Balance':[Balance], | |
| 'Number of products':[NumOfProducts], | |
| 'Has credit card?': [1 if HasCrCard=='Yes' else 0], | |
| 'Is a active member?':[1 if IsActiveMember=='Yes' else 0], | |
| 'Estimated Salary': [EstimatedSalary], | |
| 'Geography_France': [1 if Location=='France' else 0], | |
| 'Geography_Germany':[1 if Location=='Germany' else 0], | |
| 'Geography_Spain':[1 if Location=='Spain' else 0] | |
| }) | |
| cols_to_scale = ["Credit score",'Age','Tenure','Balance','Number of products','Estimated Salary'] | |
| df[cols_to_scale] = scaler.fit_transform(df[cols_to_scale]) | |
| pred=saved_model.predict(df) | |
| pred = pred[0][0] | |
| churn_prob=str(round(pred,2)) | |
| churn_prob_d = round(round(pred,2) * 100) | |
| non_churn_prob_d = 100 - churn_prob_d | |
| non_churn_prob = str(round(1-pred,2)) | |
| return {f"probability customer will exit: {churn_prob_d}%":churn_prob , f"probability customer will stay: { non_churn_prob_d}%": non_churn_prob} | |
| import gradio as gr | |
| iface = gr.Interface(fn=churn_prediction, | |
| inputs=['number',gr.inputs.Radio(['Female','Male']),'number','number','number','number',gr.inputs.Radio(['Yes','No']),gr.inputs.Radio(['Yes','No']),'number',gr.inputs.Radio(['France','Germany','Spain'])], | |
| outputs=['label']) | |
| iface.launch() |