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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
import logging
# Configure the logging
logging.basicConfig(
level=logging.INFO, # You can change to DEBUG for more details
format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
# Initialize the Flask application
sales_predictor_api = Flask("Sales Predictor")
# Load the trained machine learning model
model = joblib.load("deployment_files/sales_forecast_model_v1_0.joblib")
# Define a route for the home page (GET request)
@sales_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 Sales Prediction API!"
# Define an endpoint for single property prediction (POST request)
@sales_predictor_api.post('/v1/prediction')
def predict_rental_price():
"""
This function handles POST requests to the '/v1/prediction' 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
logger.debug(f"predict_rental_price called")
property_data = request.get_json()
logger.debug(f"property data {property_data}")
# Extract relevant features from the JSON data
sample = {
'Product_Weight': property_data['Product_Weight'],
'Product_Allocated_Area': property_data['Product_Allocated_Area'],
'Product_MRP': property_data['Product_MRP'],
'Store_Establishment_Year': property_data['Store_Establishment_Year'],
'Product_Sugar_Content': property_data['Product_Sugar_Content'],
'Product_Type': property_data['Product_Type'],
'Store_Id': property_data['Store_Id'],
'Store_Size': property_data['Store_Size'],
'Store_Location_City_Type': property_data['Store_Location_City_Type'],
'Store_Type': property_data['Store_Type']
}
# Convert the extracted data into a Pandas DataFrame
input_data = pd.DataFrame([sample])
logger.debug(f"input_data {input_data}")
# Make prediction
predicted_sales = model.predict(input_data)[0]
logger.debug(f"predicted_sales {predicted_sales}")
# Return the actual price
return jsonify({'Predicted Sales ': str(predicted_sales)})
if __name__ == '__main__':
sales_predictor_api.run(debug=True)
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