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9d4b81e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | # 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)
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