app.py

import joblib
import pandas as pd
from flask import Flask, request, jsonify

# Initialize Flask app with a name
app = Flask("SuperKart Sales Predictor")

# Load the trained sales prediction model
model = joblib.load("superkart_sales_prediction_model_v1_0.joblib")

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

@app.get('/v1/mytest')
def mytest():
    return "this is testing"

# Define an endpoint to predict sales
@app.post('/v1/totalsales')
def totalsales():
    # Get JSON data from the request
    sales_data = request.get_json()

    # Extract relevant sales features from the input data
    sample = {
        '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 sales prediction using the trained model
    prediction = model.predict(input_data)[0]
        
    # Return the prediction as a JSON response
    return jsonify({'predicted_sales': float(round(prediction, 2))})
    

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