app.py

# Import necessary libraries
from datetime import datetime
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
SK_Sales_Forecast_api = Flask("SK_Sales_Backend")

# Load the trained machine learning model
model = joblib.load("SuperKart_Sales_Forecast_model_v1_0.joblib")

# Define a route for the home page (GET request)
@SK_Sales_Forecast_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 Forecast API!"

# Define an endpoint for single product sales prediction (POST request)
@SK_Sales_Forecast_api.post('/v1/salespredict')
#@SK_Sales_Forecast_api.route('/salespredict', methods=['GET', 'POST'])

def predict_product_sale():
    """
    This function handles POST requests to the '/salespredict' 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
    product_data = request.get_json()

    # Extract relevant features from the JSON data
    sample = {
        'Product_Id': product_data['Product_Id'],
        '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_Establishment_Year': product_data['Store_Establishment_Year'],
        'Store_Size': product_data['Store_Size'],
        'Store_Location_City_Type': product_data['Store_Location_City_Type'],
        'Store_Type': product_data['Store_Type']
    }

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

    # Extract the Product_Code and Store_Age before feeding to the model
    input_data["Product_Code"] = input_data["Product_Id"].str[:2]
    input_data.drop("Product_Id", axis=1, inplace=True)

    current_year = datetime.now().year
    input_data["Store_Age"] = current_year - input_data["Store_Establishment_Year"]
    input_data.drop("Store_Establishment_Year", axis=1, inplace=True)

    # Make prediction
    predicted_sale = model.predict(input_data)[0]

    # Return the actual price
    return jsonify({'Predicted Sale': predicted_sale})


# Define an endpoint for batch prediction (POST request)
#@SK_Sales_Forecast_api.post('/salespredictbatch')
@SK_Sales_Forecast_api.route('/salespredictbatch', methods=['GET', 'POST'])


def predict_product_sale_batch():
    """
    This function handles POST requests to the '/salespredictbatch' 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)

    # Extract the Product_Code and Store_Age before feeding to the model
    input_data["Product_Code"] = input_data["Product_Id"].str[:2]
    product_ids = input_data['Product_Id'].tolist()
    input_data.drop("Product_Id", axis=1, inplace=True)
    
    current_year = datetime.now().year
    input_data["Store_Age"] = current_year - input_data["Store_Establishment_Year"]
    input_data.drop("Store_Establishment_Year", axis=1, inplace=True)

    # Make predictions for all products in the DataFrame
    predicted_sales = model.predict(input_data).tolist()

    # Create a dictionary of predictions with product IDs as keys
    #product_ids = input_data['Product_Id'].tolist()
    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__':
    #SK_Sales_Forecast_api.run(debug=True)
    SK_Sales_Forecast_api.run(host="0.0.0.0", port=7860)