Update app.py

app.py

@@ -2,28 +2,49 @@ import pickle
 import gradio as gr
 import numpy as np
 
-
-with open("Model_final.pkl", "rb") as f:
+# Load the trained model
+with open(r"C:\Users\Lenovo\Downloads\Model_pickle4", "rb") as f:
     model = pickle.load(f)
 
+# Define encoding mappings for categorical variables
+sales_channel_mapping = {"Online": 0, "Offline": 1}
+trip_type_mapping = {"Single Trip": 0, "Round Trip": 1}
+route_mapping = {
+    "AKLDEL": 0, "AKLHGH": 1, "AKLHND": 2, "AKLICN": 3, "AKLKIX": 4, "AKLKTM": 5
+}
+booking_origin_mapping = {"India": 0, "USA": 1, "UK": 2}  # Add actual values
 
 def predict_booking(num_passengers, sales_channel, trip_type, purchase_lead, length_of_stay, 
-            flight_hour, flight_day, route, booking_origin, wants_extra_baggage, 
-            wants_preferred_seat, wants_in_flight_meals, flight_duration):
+                    flight_hour, flight_day, route, booking_origin, wants_extra_baggage, 
+                    wants_preferred_seat, wants_in_flight_meals, flight_duration):
+    
+    # Convert categorical values to numerical
+    sales_channel_encoded = sales_channel_mapping.get(sales_channel, -1)
+    trip_type_encoded = trip_type_mapping.get(trip_type, -1)
+    route_encoded = route_mapping.get(route, -1)
+    booking_origin_encoded = booking_origin_mapping.get(booking_origin, -1)
     
-    input_data = np.array([[num_passengers, sales_channel, trip_type, purchase_lead, length_of_stay, 
-                            flight_hour, flight_day, route, booking_origin, wants_extra_baggage, 
-                            wants_preferred_seat, wants_in_flight_meals, flight_duration]])
+    wants_extra_baggage = 1 if wants_extra_baggage == "Yes" else 0
+    wants_preferred_seat = 1 if wants_preferred_seat == "Yes" else 0
+    wants_in_flight_meals = 1 if wants_in_flight_meals == "Yes" else 0
 
-    prediction = model.predict(input_data)[0]
-
-    return "Booking Completed ✅" if prediction == 1 else "Booking Not Completed ❌"
+    # Ensure correct feature shape
+    input_data = np.array([[num_passengers, sales_channel_encoded, trip_type_encoded, purchase_lead, 
+                            length_of_stay, flight_hour, flight_day, route_encoded, booking_origin_encoded, 
+                            wants_extra_baggage, wants_preferred_seat, wants_in_flight_meals, flight_duration]])
 
+    print("Input Data Shape:", input_data.shape)  # Debugging
 
+    # Make prediction
+    prediction = model.predict(input_data)[0]
 
+    return "Booking Completed ✅" if prediction == 1 else "Booking Not Completed ❌"
 
+# Define available options for dropdowns
 routes = ["AKLDEL", "AKLHGH", "AKLHND", "AKLICN", "AKLKIX", "AKLKTM"]
+booking_origins = ["India", "USA", "UK"]  # Add actual locations if needed
 
+# Create Gradio interface
 iface = gr.Interface(
     fn=predict_booking,
     inputs=[
@@ -34,15 +55,16 @@ iface = gr.Interface(
         gr.Number(label="Length of Stay"),
         gr.Number(label="Flight Hours"),
         gr.Number(label="Flight Day"),
-        gr.Dropdown(choices=routes, label="Route"),  # ✅ Fixed: Dropdown instead of Number
+        gr.Dropdown(choices=routes, label="Route"),
+        gr.Dropdown(choices=booking_origins, label="Booking Origin"),
         gr.Dropdown(choices=["Yes", "No"], label="Want Extra Baggage"),
-        gr.Dropdown(choices=["Yes", "No"], label="Want Preferred Seat"),  # ✅ Fixed spelling
+        gr.Dropdown(choices=["Yes", "No"], label="Want Preferred Seat"),
         gr.Dropdown(choices=["Yes", "No"], label="Want In-Flight Meals"),
         gr.Number(label="Flight Duration")
     ],
-    outputs=gr.Textbox(label="Booking Prediction"),  # ✅ Fixed spelling
+    outputs=gr.Textbox(label="Booking Prediction"),
     title="British Airways Booking Predictions",
-    description="Enter Flight Details to Predict Booking Completion"  # ✅ Fixed case-sensitive error
+    description="Enter Flight Details to Predict Booking Completion"
 )
 
 iface.launch()