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
rf_superkart_prediction_api = Flask("SuperKart Sales Prediction with XGBoost")

# Load the trained machine learning model
rf_model = joblib.load("superkart_sales_prediction_model_v1_0.joblib")

# Define a route for the home page (GET request)
@rf_superkart_prediction_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 Prediction API With Random Forest!"

# Define an endpoint for single property prediction (POST request)
@rf_superkart_prediction_api.post('/v1/predict')
def predict_sales():
    """
    This function handles POST requests to the '/v1/predict' endpoint.
    It expects a JSON payload containing store details and returns
    the predicted sales as a JSON response.
    """

    try:
      # Get the JSON data from the request body
      data = request.get_json()

      # Extract relevant features from the JSON data
      sample = {
        'Product_Weight': data['Product_Weight'],
        'Product_Sugar_Content': data['Product_Sugar_Content'],
        'Product_Allocated_Area': data['Product_Allocated_Area'],
        'Product_MRP': data['Product_MRP'],
        'Store_Size': data['Store_Size'],
        'Store_Location_City_Type': data['Store_Location_City_Type'],
        'Store_Type': data['Store_Type'],
        'Product_Code': data['Product_Code'],
        'Store_Age': data['Store_Age'],
        'Product_Category': data['Product_Category']
      }


      # Convert the extracted data into a Pandas DataFrame
      input_data = pd.DataFrame([sample])

      # Make prediction (get log_price)
      sales_prediction = rf_model.predict(input_data)[0]

      # Return the prediction
      return jsonify({'Sales': sales_prediction.tolist()})
    except Exception as e:
      print(f"Error in prediction: {e}")
      return jsonify({'error': str(e)})

# Run the Flask application in debug mode if this script is executed directly
if __name__ == '__main__':
    rf_superkart_prediction_api.run(debug=True)