aa

aaa

app.py

@@ -5,22 +5,11 @@ 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',
-]
+# Example: Define the training DataFrame X (replace with your actual training data)
+# This should be a DataFrame with the same structure as your training data
+X = pd.DataFrame(columns=['host_id', 'neighbourhood_group', 'neighbourhood', 'room_type',
+                          'latitude', 'longitude', 'number_of_reviews',
+                          'calculated_host_listings_count'])  # Add one-hot encoded columns as well
 
 # Function to make predictions
 def predict(host_id, neighbourhood_group, neighbourhood, room_type, latitude, longitude, number_of_reviews, calculated_host_listings_count):
@@ -40,7 +29,7 @@ def predict(host_id, neighbourhood_group, neighbourhood, room_type, latitude, lo
     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=feature_columns, fill_value=0)
+    input_data = input_data.reindex(columns=X.columns, fill_value=0)
 
     # Make the prediction
     predicted_price = loaded_model.predict(input_data)
@@ -51,16 +40,18 @@ iface = gr.Interface(
     fn=predict,
     inputs=[
         gr.Number(label="Host ID"),
-        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.Dropdown(label="Neighbourhood Group", choices=["Manhattan", "Brooklyn", "Queens", "Staten Island", "Bronx"]),
+        gr.Dropdown(label="Neighbourhood", choices=["Chelsea", "East Village", "Midtown", "SoHo", "Williamsburg"]),  # Add actual neighbourhood options
+        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",
+    outputs=gr.Number(label="Predicted Price"),
+    title="Airbnb Price Prediction",
+    description="Enter the details of the Airbnb listing to predict the price."
 )
 
-# Launch the Gradio app
+# Launch the interface
 iface.launch()