Update app.py

app.py

@@ -0,0 +1,76 @@
+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()