app.py

# 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():
    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():
    property_data = request.get_json()
    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']
    }
    input_data = pd.DataFrame([sample])
    predicted_log_price = model.predict(input_data)[0]
    predicted_price = np.exp(predicted_log_price)
    predicted_price = round(float(predicted_price), 2)
    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():
    file = request.files['file']
    input_data = pd.read_csv(file)
    predicted_log_prices = model.predict(input_data).tolist()
    predicted_prices = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
    property_ids = input_data['id'].tolist()
    output_dict = dict(zip(property_ids, predicted_prices))
    return output_dict

# Run the Flask application
if __name__ == '__main__':
    rental_price_predictor_api.run(debug=True)