Update app.py

app.py

@@ -6,20 +6,19 @@ import joblib  # or import pickle if you used it to save your model
 model = joblib.load('random_forest_model.pkl')  # replace with your model path
 
 # Function to predict price
-def predict_price(host_id, neighbourhood_group, room_type, price, reviews, calculated_host_listings_count, latitude, longitude):
+def predict_price(host_id, neighbourhood_group, room_type, number_of_reviews, calculated_host_listings_count, latitude, longitude):
     # Create a DataFrame for the input data
     custom_data = pd.DataFrame({
         'host_id': [host_id],
-        'neighbourhood_group_Brooklyn': [1 if neighbourhood_group == 'Brooklyn' else 0],
-        'neighbourhood_group_Manhattan': [1 if neighbourhood_group == 'Manhattan' else 0],
-        'neighbourhood_group_Queens': [1 if neighbourhood_group == 'Queens' else 0],
-        'neighbourhood_group_Bronx': [1 if neighbourhood_group == 'Bronx' else 0],
-        'neighbourhood_group_Staten Island': [1 if neighbourhood_group == 'Staten Island' else 0],
+        'neighbourhood_Brooklyn': [1 if neighbourhood_group == 'Brooklyn' else 0],
+        'neighbourhood_Manhattan': [1 if neighbourhood_group == 'Manhattan' else 0],
+        'neighbourhood_Queens': [1 if neighbourhood_group == 'Queens' else 0],
+        'neighbourhood_Bronx': [1 if neighbourhood_group == 'Bronx' else 0],
+        'neighbourhood_Staten Island': [1 if neighbourhood_group == 'Staten Island' else 0],
         'room_type_Shared room': [1 if room_type == 'Shared room' else 0],
         'room_type_Private room': [1 if room_type == 'Private room' else 0],
         'room_type_Entire home/apt': [1 if room_type == 'Entire home/apt' else 0],
-        'price': [price],
-        'number_of_reviews': [reviews],
+        'number_of_reviews': [number_of_reviews],
         'calculated_host_listings_count': [calculated_host_listings_count],
         'latitude': [latitude],
         'longitude': [longitude]
@@ -36,7 +35,6 @@ interface = gr.Interface(
         gr.Number(label="Host ID"),
         gr.Dropdown(["Brooklyn", "Manhattan", "Queens", "Bronx", "Staten Island"], label="Neighbourhood Group"),
         gr.Dropdown(["Shared room", "Private room", "Entire home/apt"], label="Room Type"),
-        gr.Number(label="Price"),
         gr.Number(label="Number of Reviews"),
         gr.Number(label="Calculated Host Listings Count"),
         gr.Number(label="Latitude"),
@@ -49,3 +47,7 @@ interface = gr.Interface(
 
 # Launch the interface
 interface.launch()
+
+print("Custom Data Columns:", custom_data.columns.tolist())
+print("Model Training Features:", model.feature_names_in_)
+