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()