import pandas as pd
import gradio as gr
import joblib



le=joblib.load('le_col.pkl')
mix=joblib.load('mimx_col.pkl')
lr=joblib.load('model.pkl')


le_col=['type_of_meal_plan','room_type_reserved','market_segment_type']
mimx_col=['no_of_adults','no_of_children','no_of_weekend_nights','no_of_week_nights','required_car_parking_space','lead_time','arrival_year','arrival_month','arrival_date','repeated_guest','no_of_previous_cancellations','no_of_previous_bookings_not_canceled','avg_price_per_room','no_of_special_requests']





def prediction_Hotel_Customer_Churn_Model(no,of,w,n,t,r,s,l,a,aa,ad,ms,rg,oc,pb,av,sr):
    try:
        input_data=pd.DataFrame({
            'no_of_adults':[no],
            'no_of_children':[of],
            'no_of_weekend_nights':[w],
            'no_of_week_nights':[n],
            'type_of_meal_plan':[t],
            'required_car_parking_space':[r],
            'room_type_reserved':[s],
            'lead_time':[l],
            'arrival_year':[a],
            'arrival_month':[aa],
            'arrival_date':[ad],
            'market_segment_type':[ms],
            'repeated_guest':[rg],
            'no_of_previous_cancellations':[oc],
            'no_of_previous_bookings_not_canceled':[pb],
            'avg_price_per_room':[av],
            'no_of_special_requests':[sr]            
        })
        for col in le_col:
            input_data[col]=le[col].transform(input_data[col])
        input_data[mimx_col]=mix.transform(input_data[mimx_col])
        prediction=lr.predict(input_data)
        if prediction[0]==0:
            return 'Not_Canceled'
        else:
            return 'Canceled'
    except Exception as e:
        return str(e)
gr.Interface(
    inputs=[
        gr.Number(label='no_of_adults'),
        gr.Number(label='no_of_children'),
        gr.Number(label='no_of_weekend_nights'),
        gr.Number(label='no_of_week_nights'),
        gr.Radio(['Meal Plan One', 'Not Selected', 'Meal Plan Two','Meal Plan Three'],label='type_of_meal_plan'),
        gr.Number(label='required_car_parking_space'),
        gr.Radio(['Room_Type 1', 'Room_Type 4', 'Room_Type 2', 'Room_Type 6','Room_Type 5', 'Room_Type 7', 'Room_Type 3'],label='room_type_reserved'),
        gr.Number(label='lead_time'),
        gr.Number(label='arrival_year'),
        gr.Number(label='arrival_month'),
        gr.Number(label='arrival_date'),
        gr.Radio(['Offline', 'Online', 'Corporate', 'Aviation', 'Complementary'],label='market_segment_type'),
        gr.Number(label='repeated_guest'),
        gr.Number(label='no_of_previous_cancellations'),
        gr.Number(label='no_of_previous_bookings_not_canceled'),
        gr.Number(label='avg_price_per_room'),
        gr.Number(label='no_of_special_requests')
        
        
    ],
    fn=prediction_Hotel_Customer_Churn_Model,
    outputs=gr.Textbox(label='Prediction'),
    title='Prediction Program',
    description='This App for work predict the Customer in hotel Not_Canceled or Canceled Booking'
).launch()