superKartApi.py

import joblib
import pandas as pd
import xgboost as xgb
from xgboost import XGBRegressor
from flask import Flask, request, jsonify

# Initialize Flask app with a name
superKartApi = Flask("Super Kart Sales Predictor")

# Load the trained churn prediction model
model = joblib.load("superkart_prediction_model_v1_0.joblib")

# Define a route for the home page
@superKartApi.get('/')
def home():
    return "Welcome to the Superkart Sales Forecast API"

# Define an endpoint to predict churn for a single customer
@superKartApi.post('/v1/product')
def predict_forecast():
    """
    This function handles POST requests to the '/v1/product' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted rental price as a JSON response.
    """

    # Get JSON data from the request
    superKar_data = request.get_json()

    # Extract relevant customer features from the input data
    sample = {
        'ProductWeight': superKar_data['ProductWeight'],
        'ProductSugarContent': superKar_data['ProductSugarContent'],
        'ProductAllocatedArea': superKar_data['ProductAllocatedArea'],
        'ProductType': superKar_data['ProductType'],
        'ProductMRP': superKar_data['ProductMRP'],
        'StoreId': superKar_data['StoreId'],
        'StoreEstablishmentYear': superKar_data['StoreEstablishmentYear'],
        'StoreSize': superKar_data['StoreSize'],
        'StoreLocationCityType': superKar_data['StoreLocationCityType'],
        'StoreType': superKar_data['StoreType']
    }

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

    # Make a churn prediction using the trained model
    predicted_sales_value = model.predict(input_data).tolist()[0]

    # Calculate actual price
    predicted_sales = np.exp(predicted_sales_value)

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

    # Return the actual price
    return jsonify({'Predicted Sales': predicted_sales})

    # # Map prediction result to a human-readable label
    # prediction_label = "churn" if prediction == 1 else "not churn"

    # # Return the prediction as a JSON response
    # return jsonify({'Prediction': prediction_label})

# Define an endpoint to predict churn for a batch of customers
@superKartApi.post('/v1/superKartbatch')
def predict_churn_batch():

    """
    This function handles POST requests to the '/v1/superKartbatch' 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 file into a DataFrame
    input_data = pd.read_csv(file)


    # Make predictions for all properties in the DataFrame (get log_sales)
    predicted_log_sales = model.predict(input_data).tolist()

    # Calculate actual sales
    predicted_sales = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_sales]

    # # Make predictions for the batch data and convert raw predictions into a readable format
    # predictions = [
    #     'Churn' if x == 1
    #     else "Not Churn"
    #     for x in model.predict(input_data.drop("ProductId",axis=1)).tolist()
    # ]

    # Create a dictionary of predictions with property IDs as keys
    product_id_list = input_data.ProductId.values.tolist()
    output_dict = dict(zip(product_id_list, predicted_sales))

    # Return the predictions dictionary as a JSON response
    return output_dict