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
extraalearn_predictor_api = Flask("Extraalearn Predictor")

# Load the trained machine learning model
model = joblib.load("extraalearn_model_v1_0.joblib")

# Define a route for the home page (GET request)
@extraalearn_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 ExtraaLearn Prediction API!"

# Define an endpoint for prediction (POST request)
@extraalearn_predictor_api.post('/v1/extraalearn')
def predict_rental_price():
    """
    This function handles POST requests to the '/v1/extraalearn' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted product price as a JSON response.
    """
    # Get the JSON data from the request body
    data = request.get_json()

    # Extract relevant features from the JSON data
    sample =

    {
    'ID': data['ID'],
    'Age': data['age'],
    'Current_Occupation': data['current_occupation'],
    'First_Interaction': data['first_interaction'],
    'Profile_Completed': data['profile_completed'],
    'Website_Visits': data['website_visits'],
    'Time_Spent_On_Website': data['time_spent_on_website'],
    'Page_Views_Per_Visit': data['page_views_per_visit'],
    'Last_Activity': data['last_activity'],
    'Print_Media_Type1': data['print_media_type1'],
    'Print_Media_Type2': data['print_media_type2'],
    'Digital_Media': data['digital_media'],
    'Educational_Channels': data['educational_channels'],
    'Referral': data['referral'],
    'Status': data['status']


}

    # 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)
@extraalearn_predictor_api.post('/v1/extraalearnbatch')
def predict_rental_price_batch():
    """
    This function handles POST requests to the '/v1/extraalearnbatch' 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_status = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]

    # Create a dictionary of predictions with property IDs as keys
 # Example: using 'ID' as the key
    ids = input_data['ID'].tolist()   # This is like Product_Id
    output_dict = dict(zip(ids, predicted_status))  # predicted_status = your model's output list


    # 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__':
    extraalearn_predictor_api.run(debug=True)