app.py

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

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

# Load the trained churn prediction model
model = joblib.load("sales_prediction_model_v1_0.joblib")

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

# Define an endpoint to predict sales
@app.post('/v1/sales')
def predict_sales():
    # Get JSON data from the request
    data = request.get_json()
    print(data)
    # Extract relevant customer features from the input data
    sample = {
        'Product_Weight': data['Product_Weight'],
        'Product_Sugar_Content': data['Product_Sugar_Content'],
        'Product_Allocated_Area': data['Product_Allocated_Area'],
        'Product_MRP': data['Product_MRP'],
        'Store_Size': data['Store_Size'],
        'Store_Location_City_Type': data['Store_Location_City_Type'],
        'Store_Type': data['Store_Type'],
        'category': data['category'],
        'Product_Type_category': data['Product_Type_category'],
        'Store_Establishment_Year':1999, 
        'Product_Id':"", 
        'Store_Id':"", 
        'Product_Type':""}
        
    print(sample)
    # 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]

    # Map prediction result to a human-readable label


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

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