Update app.py

app.py

@@ -5,42 +5,66 @@ import joblib
 # Load the trained model
 model = joblib.load('random_forest_model.pkl')  # replace with your model path
 
-# Define the prediction function
-def predict_price(host_id, neighbourhood_group, latitude, longitude, number_of_reviews, calculated_host_listings_count):
-    # Initialize custom input data
-    training_columns = model.feature_names_in_
-    custom_data = pd.DataFrame(0, index=[0], columns=training_columns)
-    custom_data = custom_data.astype({'latitude': 'float64', 'longitude': 'float64'})  # Ensure float types
-
-    # Specify values for the relevant columns in `custom_data`
+# Define the function to make predictions
+def predict_price(host_id, latitude, longitude, number_of_reviews, calculated_host_listings_count, 
+                  room_type_Private_room, room_type_Shared_room, 
+                  neighbourhood_group_Brooklyn, neighbourhood_group_Manhattan, 
+                  neighbourhood_group_Queens, neighbourhood_group_Staten_Island, 
+                  neighbourhood_Arden_Heights, neighbourhood_Arrochar, neighbourhood_Arverne):
+    
+    # Prepare input data
+    custom_data = pd.DataFrame(0, index=[0], columns=model.feature_names_in_)
     custom_data.at[0, 'host_id'] = host_id
     custom_data.at[0, 'latitude'] = latitude
     custom_data.at[0, 'longitude'] = longitude
     custom_data.at[0, 'number_of_reviews'] = number_of_reviews
     custom_data.at[0, 'calculated_host_listings_count'] = calculated_host_listings_count
-
-    # Set neighbourhood group feature
-    custom_data.at[0, f'neighbourhood_group_{neighbourhood_group}'] = 1
+    custom_data.at[0, 'room_type_Private room'] = room_type_Private_room
+    custom_data.at[0, 'room_type_Shared room'] = room_type_Shared_room
+    custom_data.at[0, 'neighbourhood_group_Brooklyn'] = neighbourhood_group_Brooklyn
+    custom_data.at[0, 'neighbourhood_group_Manhattan'] = neighbourhood_group_Manhattan
+    custom_data.at[0, 'neighbourhood_group_Queens'] = neighbourhood_group_Queens
+    custom_data.at[0, 'neighbourhood_group_Staten Island'] = neighbourhood_group_Staten_Island
+    custom_data.at[0, 'neighbourhood_Arden Heights'] = neighbourhood_Arden_Heights
+    custom_data.at[0, 'neighbourhood_Arrochar'] = neighbourhood_Arrochar
+    custom_data.at[0, 'neighbourhood_Arverne'] = neighbourhood_Arverne
 
     # Make prediction
     predicted_price = model.predict(custom_data)
+    return f"The predicted house price is: ${predicted_price[0]:.2f}"
 
-    # Display input data and predicted price
-    input_data_display = custom_data.iloc[0].to_dict()
-    input_data_display['Predicted Price'] = predicted_price[0]
+# Set up the Gradio interface
+title = "House Price Predictor"
+description = """
+This application predicts the price of a house based on several features. 
+Please fill in the following details to get a prediction:
+- **Latitude**: Geographic coordinate.
+- **Longitude**: Geographic coordinate.
+- **Number of Reviews**: Total reviews received by the listing.
+- **Calculated Host Listings Count**: Total number of listings by the host.
+- **Room Type**: Select whether the room is a private or shared room.
+- **Neighbourhood Groups**: Select the corresponding neighbourhood group.
 
-    return input_data_display
+After entering the information, click on the **'Submit'** button to see the predicted price.
+"""
 
-# Define Gradio interface
 inputs = [
-    gr.Number(label="Host ID"),
-    gr.Dropdown(choices=["Brooklyn", "Manhattan", "Queens", "Bronx", "Staten Island"], label="Neighbourhood Group"),
+   
     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"),
+    gr.Radio(label="Room Type - Private Room", choices=[0, 1]),
+    gr.Radio(label="Room Type - Shared Room", choices=[0, 1]),
+    gr.Radio(label="Neighbourhood Group - Brooklyn", choices=[0, 1]),
+    gr.Radio(label="Neighbourhood Group - Manhattan", choices=[0, 1]),
+    gr.Radio(label="Neighbourhood Group - Queens", choices=[0, 1]),
+    gr.Radio(label="Neighbourhood Group - Staten Island", choices=[0, 1]),
+    gr.Radio(label="Neighbourhood - Arden Heights", choices=[0, 1]),
+    gr.Radio(label="Neighbourhood - Arrochar", choices=[0, 1]),
+    gr.Radio(label="Neighbourhood - Arverne", choices=[0, 1]),
 ]
 
-output = gr.JSON(label="Input Data and Predicted Price")
+output = gr.Textbox(label="Predicted Price", placeholder="The predicted price will appear here.", lines=2)
 
-gr.Interface(fn=predict_price, inputs=inputs, outputs=output, title="Airbnb Price Prediction", description="Input data to predict Airbnb listing prices").launch()
+gr.Interface(fn=predict_price, inputs=inputs, outputs=output, title=title, description=description).launch()