app.py

import sys
import os
import pandas as pd
import numpy as np
import joblib
import pandas as pd
from flask import Flask, request, jsonify

# Initialize Flask app with a name
app = Flask("SK Sales Forecast")

# Load the trained sales forecast model
model = joblib.load('SuperKart_salesprediction_model_v1_0-2.joblib')


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

# Define an endpoint to predict forecast for a single product store sales total
@app.post('/v1/singleproduct')
def sales_forecast():
    # Get JSON data from the request
    sales_data = request.get_json()

    # Extract relevant sales features from the input data
    sample = {
        'Product_Id': sales_data['Product_Id'],
        'Product_Weight': sales_data['Product_Weight'],
        'Product_Sugar_Content': sales_data['Product_Sugar_Content'],
        'Product_Allocated_Area': sales_data['Product_Allocated_Area'],
        'Product_Type': sales_data['Product_Type'],
        'Product_MRP': sales_data['Product_MRP'],
        'Store_Id': sales_data['Store_Id'],
        'Store_Establishment_Year': sales_data['Store_Establishment_Year'],
        'Store_Size': sales_data['Store_Size'],
        'Store_Location_City_Type': sales_data['Store_Location_City_Type'],
        'Store_Type': sales_data['Store_Type']

    }

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

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

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


# Define an endpoint to predict churn for a batch of customers
@app.post('/v1/productbatch')
def predict_sales_batch():
    # 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 the batch data and convert raw predictions into a readable format
    predictions = model.predict(input_data).tolist()
    product_id_list = input_data['Product_Id'].tolist()
    output_dict = dict(zip(product_id_list, predictions))

    return output_dict


# Run the Flask app in debug mode
if __name__ == '__main__':
    app.run(debug=True)