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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
from pathlib import Path # For using a robust, absolute path
# Define the base directory of the script
BASE_DIR = Path(__file__).resolve().parent
# Define the full path to your model file
MODEL_PATH = BASE_DIR / "xgb_tuned.joblib"
# Initialize Flask application
superkart_api = Flask("SuperKart Sales Predictor")
# Load the trained machine learning model
model = joblib.load(MODEL_PATH)
# Define a route for the home page (GET request)
@superkart_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 SuperKart Sales Predictor API !"
# Define an endpoint to predict for a single observation
@superkart_api.post('/v1/predict')
def predict_sales():
"""
This function handles POST requests to the '/v1/predict' endpoint.
It expects a JSON payload containing property details and returns
the predicted rental price as a JSON response.
"""
# Get JSON data from the request
data = request.get_json()
# Extract relevant customer features from the input data. The order of the column names matters.
sample = {
'Product_Weight': data['Product_Weight'],
'Product_MRP': data['Product_MRP'],
'Product_Allocated_Area': data['Product_Allocated_Area'],
'Product_Sugar_Content': data['Product_Sugar_Content'],
'Store_Size': data['Store_Size'],
'Store_Location_City_Type': data['Store_Location_City_Type'],
'Store_Type': data['Store_Type'],
'Store_Age_Years': data['Store_Age_Years'],
'Product_Id_prefix': data['Product_Id_prefix'],
'Product_FD_perishable': data['Product_FD_perishable'],
}
# Convert the extracted data into a DataFrame
input_data = pd.DataFrame([sample])
# Make a store sales prediction using the trained model
prediction = model.predict(input_data).tolist()[0]
# Return the prediction as a JSON response
return jsonify({'Sales': prediction})
# Define an endpoint for batch prediction (POST request)
@superkart_api.post('/v1/batch')
def predict_sales_batch():
"""
This function handles POST requests to the '/v1/batch' 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
predicted_sales = model.predict(input_data).tolist()
# Create a dictionary of predictions with property IDs as keys
product_ids = input_data['Product_Id'].tolist()
output_dict = dict(zip(product_ids, predicted_sales))
# Return the predictions dictionary as a JSON response
return output_dict
# Run the Flask app in debug mode
if __name__ == '__main__':
superkart_api.run(debug=True)
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