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# Import necessary libraries
import os
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("Extraa Learn conversion Predictor")
# Load the trained machine learning model
# Use relative path to load the model inside backend_files
model_path = os.path.join(os.path.dirname(__file__), "conversion_prediction_model_v1_0.joblib")
model = joblib.load(model_path)
print("Model loaded successfully.")
# model = joblib.load(model_path)
# 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 "HI, Welcome to the Extraa Learn conversion Predictor API!"
# Define an endpoint for single property prediction (POST request)
@rental_price_predictor_api.post('/v1/conversion')
def predict_rental_price():
property_data = request.get_json()
sample = {
'age': property_data['age'],
'website_visits': property_data['website_visits'],
'time_spent_on_website': property_data['time_spent_on_website'],
'page_views_per_visit': property_data['page_views_per_visit'],
'current_occupation': property_data['current_occupation'],
'first_interaction': property_data['first_interaction'],
'profile_completed': property_data['profile_completed'],
'last_activity': property_data['last_activity'],
'print_media_type1': property_data['print_media_type1'],
'print_media_type2': property_data['print_media_type2'],
'digital_media': property_data['digital_media'],
'educational_channels': property_data['educational_channels'],
'referral': property_data['referral']
}
input_data = pd.DataFrame([sample])
# Directly predict class (0 or 1)
predicted_status = int(model.predict(input_data)[0])
return jsonify({'Predicted Status': predicted_status})
# Define an endpoint for batch prediction (POST request)
@rental_price_predictor_api.post('/v1/conversionbatch')
def predict_rental_price_batch():
"""
This function handles POST requests to the '/v1/conversionbatch' 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)
status_log = model.predict(input_data).tolist()
# Calculate actual prices
status = [round(float(np.exp(log_price)), 2) for log_price in status_log]
# 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, status)) # 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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