Update app.py

app.py

@@ -1,53 +1,55 @@
 import gradio as gr
 import pandas as pd
-import joblib  # or import pickle if you used it to save your model
+import joblib
 
-# Load your trained model
+# Load the trained model
 model = joblib.load('random_forest_model.pkl')  # replace with your model path
 
-# Function to predict price
+# Define the function to make predictions
 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_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],
-        'number_of_reviews': [number_of_reviews],
-        'calculated_host_listings_count': [calculated_host_listings_count],
-        'latitude': [latitude],
-        'longitude': [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
+    
+    # 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 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
 
     # Make prediction
     predicted_price = model.predict(custom_data)
     return predicted_price[0]
 
-# Define Gradio interface
-interface = gr.Interface(
+# Create the Gradio interface
+iface = gr.Interface(
     fn=predict_price,
     inputs=[
         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.Dropdown(label="Neighbourhood Group", choices=["Brooklyn", "Manhattan", "Queens", "Bronx", "Staten Island"]),
+        gr.Dropdown(label="Room Type", choices=["Shared room", "Private room", "Entire home/apt"]),
         gr.Number(label="Number of Reviews"),
         gr.Number(label="Calculated Host Listings Count"),
         gr.Number(label="Latitude"),
         gr.Number(label="Longitude")
     ],
-    outputs="number",
+    outputs=gr.Number(label="Predicted Price"),
     title="Airbnb Price Prediction",
     description="Enter the details to predict the price of an Airbnb listing."
 )
 
 # Launch the interface
-interface.launch()
-
-print("Custom Data Columns:", custom_data.columns.tolist())
-print("Model Training Features:", model.feature_names_in_)
-
+iface.launch()