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import pandas as pd
import gradio as gr
import joblib
le=joblib.load('le_col.pkl')
std=joblib.load('std_column.pkl')
model=joblib.load('model.pkl')
le_col=['Gender','Geography','CardType']
std_column=['Tenure','SatisfactionScore','CreditScore','Age','PointEarned','EstimatedSalary']
def prediction_Bank_model(creditScore,geography,gender,age,tenure,hasCrCard,estimatedSalary,complain,satisfactionScore,cardType,pointEarned):
try:
input_data=pd.DataFrame({
'CreditScore':[creditScore],
'Geography':[geography],
'Gender':[gender],
'Age':[age],
'Tenure':[tenure],
'HasCrCard':[hasCrCard],
'EstimatedSalary':[estimatedSalary],
'Complain':[complain],
'SatisfactionScore':[satisfactionScore],
'CardType':[cardType],
'PointEarned':[pointEarned]
})
for col in le_col:
input_data[col]=le[col].transform(input_data[col])
input_data[std_column]=std.transform(input_data[std_column])
predictiction=model.predict(input_data)
if predictiction[0]==0:
return 'No'
else:
return 'yes'
except Exception as e:
return f"Error :{e}"
gr.Interface(
fn=prediction_Bank_model,
inputs=[
gr.Number(label='CreditScore'),
gr.Dropdown(choices=('France','Germany','Spain'),label='Geography'),
gr.Dropdown(choices=('Male','Female'),label='Gender'),
gr.Number(label='Age'),
gr.Number(label='Tenure'),
gr.Number(label='HasCrCard'),
gr.Number(label='EstimatedSalary'),
gr.Number(label='Complain'),
gr.Number(label='SatisfactionScore'),
gr.Dropdown(choices=('DIAMOND','GOLD','SILVER','PLATINUM'),label='CardType'),
gr.Number(label='PointEarned')
],
outputs=gr.Textbox(label='Prediction'),
title='Prediction Program'
).launch()
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