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# Import necessary libraries
import numpy as np
import joblib # For loading the serialized model
import pandas as pd # For data manipulation
from flask import Flask, request, jsonify # For creating the Flask API
# Initialize the Flask application
rental_price_predictor_api = Flask("Airbnb Rental Price Predictor")
# Load the trained machine learning model
model = joblib.load("rental_price_prediction_model_v1_0.joblib")
# Define a route for the home page (GET request)
@rental_price_predictor_api.get('/')
def home():
"""
This function handles GET requests to the root URL ('/') of the API.
It returns a simple welcome message.
"""
return "Welcome to the Airbnb Rental Price Prediction API!"
# Define an endpoint for single property prediction (POST request)
@rental_price_predictor_api.post('/v1/rental')
def predict_rental_price():
"""
This function handles POST requests to the '/v1/rental' endpoint.
It expects a JSON payload containing property details and returns
the predicted rental price as a JSON response.
"""
# Get the JSON data from the request body
property_data = request.get_json()
# Extract relevant features from the JSON data
sample = {
'room_type': property_data['room_type'],
'accommodates': property_data['accommodates'],
'bathrooms': property_data['bathrooms'],
'cancellation_policy': property_data['cancellation_policy'],
'cleaning_fee': property_data['cleaning_fee'],
'instant_bookable': property_data['instant_bookable'],
'review_scores_rating': property_data['review_scores_rating'],
'bedrooms': property_data['bedrooms'],
'beds': property_data['beds']
}
# Convert the extracted data into a Pandas DataFrame
input_data = pd.DataFrame([sample])
# Make prediction (get log_price)
predicted_log_price = model.predict(input_data)[0]
# Calculate actual price
predicted_price = np.exp(predicted_log_price)
# Convert predicted_price to Python float
predicted_price = round(float(predicted_price), 2)
# The conversion above is needed as we convert the model prediction (log price) to actual price using np.exp, which returns predictions as NumPy float32 values.
# When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error
# Return the actual price
return jsonify({'Predicted Price (in dollars)': predicted_price})
# Define an endpoint for batch prediction (POST request)
@rental_price_predictor_api.post('/v1/rentalbatch')
def predict_rental_price_batch():
"""
This function handles POST requests to the '/v1/rentalbatch' endpoint.
It expects a CSV file containing property details for multiple properties
and returns the predicted rental prices as a dictionary in the JSON response.
"""
# Get the uploaded CSV file from the request
file = request.files['file']
# Read the CSV file into a Pandas DataFrame
input_data = pd.read_csv(file)
# Make predictions for all properties in the DataFrame (get log_prices)
predicted_log_prices = model.predict(input_data).tolist()
# Calculate actual prices
predicted_prices = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
# Create a dictionary of predictions with property IDs as keys
property_ids = input_data['id'].tolist() # Assuming 'id' is the property ID column
output_dict = dict(zip(property_ids, predicted_prices)) # Use actual prices
# Return the predictions dictionary as a JSON response
return output_dict
# Run the Flask application in debug mode if this script is executed directly
if __name__ == '__main__':
rental_price_predictor_api.run(debug=True)
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