Update app.py

app.py

@@ -5,6 +5,23 @@ import joblib
 # Load your Random Forest model
 loaded_model = joblib.load('random_forest_model.pkl')
 
+# Define the expected feature columns
+feature_columns = [
+    'host_id',
+    'latitude',
+    'longitude',
+    'number_of_reviews',
+    'calculated_host_listings_count',
+    'room_type_Entire home/apt',
+    'room_type_Private room',
+    'room_type_Shared room',
+    'neighbourhood_group_Bronx',
+    'neighbourhood_group_Brooklyn',
+    'neighbourhood_group_Manhattan',
+    'neighbourhood_group_Queens',
+    'neighbourhood_group_Staten Island',
+]
+
 # Function to make predictions
 def predict(host_id, neighbourhood_group, neighbourhood, room_type, latitude, longitude, number_of_reviews, calculated_host_listings_count):
     # Prepare input data as DataFrame
@@ -21,9 +38,9 @@ def predict(host_id, neighbourhood_group, neighbourhood, room_type, latitude, lo
 
     # One-hot encode the categorical features
     input_data = pd.get_dummies(input_data, columns=['room_type', 'neighbourhood_group', 'neighbourhood'], drop_first=True)
-    
+
     # Ensure the input data has the same columns as the training data
-    input_data = input_data.reindex(columns=X.columns, fill_value=0)
+    input_data = input_data.reindex(columns=feature_columns, fill_value=0)
 
     # Make the prediction
     predicted_price = loaded_model.predict(input_data)
@@ -34,18 +51,16 @@ iface = gr.Interface(
     fn=predict,
     inputs=[
         gr.Number(label="Host ID"),
-        gr.Dropdown(["Manhattan", "Brooklyn", "Queens", "Bronx", "Staten Island"], label="Neighbourhood Group"),
-        gr.Dropdown(["Upper East Side", "Chelsea", "Williamsburg"], label="Neighbourhood"),
-        gr.Dropdown(["Entire home/apt", "Private room", "Shared room"], label="Room Type"),
+        gr.Dropdown(label="Neighbourhood Group", choices=['Bronx', 'Brooklyn', 'Manhattan', 'Queens', 'Staten Island']),
+        gr.Dropdown(label="Neighbourhood", choices=['Chelsea', 'Flatiron District', 'Upper West Side', 'East Village', '...']),
+        gr.Dropdown(label="Room Type", choices=['Entire home/apt', 'Private room', 'Shared room']),
         gr.Number(label="Latitude"),
         gr.Number(label="Longitude"),
         gr.Number(label="Number of Reviews"),
-        gr.Number(label="Calculated Host Listings Count")
+        gr.Number(label="Calculated Host Listings Count"),
     ],
     outputs="number",
-    title="NYC Rental Price Prediction",
-    description="Predict the rental price of an Airbnb listing in NYC."
 )
 
-# Launch the interface
+# Launch the Gradio app
 iface.launch()