backend_files/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
superkart_sales_api = Flask("SuperKart Sales Predictor")

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

# Define a route for the home page (GET request)
@superkart_sales_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!"

# Endpoint for Single Prediction
# -------------------------------
@superkart_sales_api.post('/v1/sales')
def predict_sales():
    """
    Predict sales for a single product-outlet combination
    """
    try:
        # Get JSON data from request
        data = request.get_json()

        # Extract relevant features
        sample = {
            'Product_Weight': data['Product_Weight'],            
            'Product_Allocated_Area': data['Product_Allocated_Area'],
            'Product_MRP': data['Product_MRP'],
            'Store_Establishment_Year': data['Store_Establishment_Year'],
            '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'],
            'Product_Type': data['Product_Type']

        }
          
        # Convert to DataFrame
        input_df = pd.DataFrame([sample])

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

        # Convert to float and round
        prediction = round(float(prediction), 2)

        return jsonify({"Predicted Sales": prediction})

    except Exception as e:
        return jsonify({"error": str(e)}), 500

# -------------------------------
# Endpoint for Batch Prediction
# -------------------------------
@superkart_sales_api.post('/v1/salesbatch')
def predict_sales_batch():
    """
    Predict sales for multiple rows from a CSV file
    """
    try:
        # Get uploaded file
        file = request.files['file']

        # Read into DataFrame
        input_df = pd.read_csv(file)

        # Make predictions
        predictions = model.predict(input_df).tolist()
        predictions = [round(float(p), 2) for p in predictions]

        # Return predictions in a dict format with row index as key
        output_dict = {str(i): predictions[i] for i in range(len(predictions))}

        return jsonify(output_dict)

    except Exception as e:
        return jsonify({"error": str(e)}), 500

# -------------------------------
# Run App
# -------------------------------
if __name__ == '__main__':
    superkart_sales_api.run(debug=True, host="0.0.0.0", port=7860)