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
sales_predictor_api = Flask("SuparKart Sales Predictor")

# Load the trained machine learning model
model = joblib.load("superkart_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 SuperKart Sales Prediction API!"

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

    # Extract relevant features from the JSON data
    sample = {
        'Product_Weight': product_data['product_weight'],
        'Product_Sugar_Content': product_data['product_sugar_content'],
        'Product_Allocated_Area': product_data['product_allocated_area'],
        'Product_Type': product_data['product_type'],
        'Product_MRP': product_data['product_mrp'],
        'Store_Id': product_data['store_id'],
        'Store_Size': product_data['store_size'],
        'Store_Location_City_Type': product_data['store_location_city_type']
    }

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

    # Make prediction
    predicted_sales = round(float(model.predict(input_data)[0]), 2)

    # Return the predicted sales
    return jsonify({'Predicted product Store Sales': predicted_sales})


# Define an endpoint for batch prediction (POST request)
@sales_predictor_api.post('/v1/salesbatch')
def predict_sales_batch():
    """
    This function handles POST requests to the '/v1/salesbatch' endpoint.
    It expects a CSV file containing product and store details for multiple products
    and returns the predicted sales 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 product stores in the DataFrame
    predicted_sales = model.predict(input_data).tolist()

    # Create a dictionary of predictions with product IDs as keys
    product_ids = input_data['id'].tolist()  # Assuming 'id' is the product ID column
    output_dict = dict(zip(product_ids, predicted_sales))  # Use actual prices

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