app.py

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

# Initialize Flask app
superkart_api = Flask("superkart_api")

# Build model path relative to this file's directory
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
MODEL_PATH = os.path.join(BASE_DIR, "bagging_tuned_model.joblib")

# Load the trained sales forecast model
model = joblib.load(MODEL_PATH)


@superkart_api.get("/")
def home():
    return "Welcome to the SuperKart Sales Predict API!"


@superkart_api.post("/v1/predict")
def predict_sales():
    # Get JSON payload
    data = request.get_json()

    # Build a row matching the training feature schema
    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"],
        "Product_Id_char": data["Product_Id_char"],
        "Store_Age_Years": data["Store_Age_Years"],
        "Product_Type_Category": data["Product_Type_Category"],
    }

    # Convert to DataFrame
    input_df = pd.DataFrame([sample])

    # Predict sales
    prediction = model.predict(input_df)[0]

    # Return JSON
    return jsonify({"Sales": float(prediction)})


if __name__ == "__main__":
    # Local debug mode
    superkart_api.run(host="0.0.0.0", port=7860, debug=True)