Update app.py

app.py

@@ -7,28 +7,45 @@ model = joblib.load('random_forest_model.pkl')  # replace with your model path
 
 # Define the function to make predictions
 def predict_price(host_id, neighbourhood_group, room_type, number_of_reviews, calculated_host_listings_count, latitude, longitude):
-    # Initialize custom input data with columns matching the training data
-    custom_data = pd.DataFrame(0, index=[0], columns=model.feature_names_in_)
-    custom_data = custom_data.astype({'latitude': 'float64', 'longitude': 'float64'})  # Ensure latitude and longitude are floats
+    # Create a dictionary with default values
+    data = {
+        'host_id': host_id,
+        'number_of_reviews': number_of_reviews,
+        'calculated_host_listings_count': calculated_host_listings_count,
+        'latitude': latitude,
+        'longitude': longitude,
+        # Set dummy values for categorical features
+        'neighbourhood_group_Brooklyn': 0,
+        'neighbourhood_group_Manhattan': 0,
+        'neighbourhood_group_Queens': 0,
+        'neighbourhood_group_Bronx': 0,
+        'neighbourhood_group_Staten Island': 0,
+        'room_type_Shared room': 0,
+        'room_type_Private room': 0,
+        'room_type_Entire home/apt': 0,
+    }
     
-    # Set values for the relevant columns
-    custom_data.at[0, 'host_id'] = host_id
-    custom_data.at[0, 'number_of_reviews'] = number_of_reviews
-    custom_data.at[0, 'calculated_host_listings_count'] = calculated_host_listings_count
-    custom_data.at[0, 'latitude'] = latitude
-    custom_data.at[0, 'longitude'] = longitude
+    # Set appropriate values based on user input
+    if neighbourhood_group == 'Brooklyn':
+        data['neighbourhood_group_Brooklyn'] = 1
+    elif neighbourhood_group == 'Manhattan':
+        data['neighbourhood_group_Manhattan'] = 1
+    elif neighbourhood_group == 'Queens':
+        data['neighbourhood_group_Queens'] = 1
+    elif neighbourhood_group == 'Bronx':
+        data['neighbourhood_group_Bronx'] = 1
+    elif neighbourhood_group == 'Staten Island':
+        data['neighbourhood_group_Staten Island'] = 1
     
-    # Set neighbourhood group features
-    custom_data['neighbourhood_group_Brooklyn'] = 1 if neighbourhood_group == 'Brooklyn' else 0
-    custom_data['neighbourhood_group_Manhattan'] = 1 if neighbourhood_group == 'Manhattan' else 0
-    custom_data['neighbourhood_group_Queens'] = 1 if neighbourhood_group == 'Queens' else 0
-    custom_data['neighbourhood_group_Bronx'] = 1 if neighbourhood_group == 'Bronx' else 0
-    custom_data['neighbourhood_group_Staten Island'] = 1 if neighbourhood_group == 'Staten Island' else 0
-    
-    # Set room type features
-    custom_data['room_type_Shared room'] = 1 if room_type == 'Shared room' else 0
-    custom_data['room_type_Private room'] = 1 if room_type == 'Private room' else 0
-    custom_data['room_type_Entire home/apt'] = 1 if room_type == 'Entire home/apt' else 0
+    if room_type == 'Shared room':
+        data['room_type_Shared room'] = 1
+    elif room_type == 'Private room':
+        data['room_type_Private room'] = 1
+    elif room_type == 'Entire home/apt':
+        data['room_type_Entire home/apt'] = 1
+
+    # Create DataFrame from the dictionary
+    custom_data = pd.DataFrame([data])
 
     # Make prediction
     predicted_price = model.predict(custom_data)
@@ -52,4 +69,4 @@ iface = gr.Interface(
 )
 
 # Launch the interface
-iface.launch()
+iface.launch(share=True)  # Set share=True to create a public link