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

print("--- app.py: Starting Flask application setup ---")

# Initialize the Flask application
superkart_sales_api = Flask("SuperKart Sales Predictor")
print("--- app.py: Flask app initialized ---")

# Load the trained machine learning model
try:
    model = joblib.load("superkart_sales_model.pkl")
    print("--- app.py: Model loaded successfully ---")
except Exception as e:
    print(f"--- app.py: ERROR loading model: {e} ---")
    raise # Re-raise to ensure the error is visible

# Define a route for the home page (GET request)
@superkart_sales_api.get('/')
def home():
    print("--- API: Home route accessed ---")
    """
    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 sales prediction (POST request)
@superkart_sales_api.post('/v1/sales')
def predict_sales():
    print("--- API: Single sales prediction route accessed ---")
    """
    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
    input_data_json = request.get_json()

    # Extract relevant features from the JSON data, matching x_train columns
    # The model expects original feature names before one-hot encoding
    sample = {
        'Product_Id': input_data_json['Product_Id'],
        'Product_Weight': input_data_json['Product_Weight'],
        'Product_Sugar_Content': input_data_json['Product_Sugar_Content'],
        'Product_Allocated_Area': input_data_json['Product_Allocated_Area'],
        'Product_Type': input_data_json['Product_Type'],
        'Product_MRP': input_data_json['Product_MRP'],
        'Store_Id': input_data_json['Store_Id'],
        'Store_Size': input_data_json['Store_Size'],
        'Store_Location_City_Type': input_data_json['Store_Location_City_Type'],
        'Store_Current_Age': input_data_json['Store_Current_Age']
    }

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

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

    # Convert predicted_sales to Python float and round
    predicted_sales = round(float(predicted_sales), 2)

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


# Define an endpoint for batch prediction (POST request)
@superkart_sales_api.post('/v1/salesbatch')
def predict_sales_batch():
    print("--- API: Batch sales prediction route accessed ---")
    """
    This function handles POST requests to the '/v1/salesbatch' endpoint.
    It expects a CSV file containing product and store details for multiple entries
    and returns the predicted sales as a list 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_df_batch = pd.read_csv(file)

    # Make predictions for all entries in the DataFrame
    predicted_sales_batch = model.predict(input_df_batch).tolist()

    # Round each prediction and convert to float
    predicted_sales_batch = [round(float(s), 2) for s in predicted_sales_batch]

    # Return the predictions list as a JSON response
    return jsonify({'Predicted Sales': predicted_sales_batch})

# Run the Flask application in debug mode if this script is executed directly
# When deploying with Gunicorn, this block is usually commented out or removed
if __name__ == '__main__':
    print("--- app.py: Running Flask app in debug mode ---")
    superkart_sales_api.run(debug=True)