Update app.py

app.py

@@ -3,70 +3,44 @@ import pandas as pd
 import joblib
 
 # Load the trained model
-model = joblib.load('random_forest_model.pkl')  # replace with your model path
+model = joblib.load('/content/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):
-    # 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 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
-    
-    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
+# 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
 
-    # Create DataFrame from the dictionary
-    custom_data = pd.DataFrame([data])
+    # Specify values for the relevant columns in `custom_data`
+    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
 
     # Make prediction
     predicted_price = model.predict(custom_data)
-    return predicted_price[0]
 
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict_price,
-    inputs=[
-        gr.Number(label="Host ID"),
-        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=gr.Number(label="Predicted Price"),
-    title="Airbnb Price Prediction",
-    description="Enter the details to predict the price of an Airbnb listing."
-)
+    # Display input data and predicted price
+    input_data_display = custom_data.iloc[0].to_dict()
+    input_data_display['Predicted Price'] = predicted_price[0]
+
+    return input_data_display
+
+# 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")
+]
+
+output = gr.JSON(label="Input Data and Predicted Price")
 
-# Launch the interface
-iface.launch(share=True)  # Set share=True to create a public link
+gr.Interface(fn=predict_price, inputs=inputs, outputs=output, title="Airbnb Price Prediction", description="Input data to predict Airbnb listing prices").launch()