app.py

# app.py
from preprocess import custom_preprocessing 
from flask import Flask, request, jsonify
import joblib
import numpy as np
import pandas as pd

# Initialize Flask app
app = Flask(__name__)

# Load the saved model pipeline
model = joblib.load("rf_best.joblib")  # path to our saved file

@app.route('/')
def home():
    return jsonify({"message": "SuperKart sales predictor API is running!"})

@app.route('/predict', methods=['POST'])
def predict():
    try:
        # Expecting JSON input
        data = request.get_json()

        # Convert JSON to DataFrame
        df = pd.DataFrame([data])

        # Make prediction
        prediction = model.predict(df)

        # Return response
        return jsonify({
            "prediction": float(prediction[0])
        })
    except Exception as e:
        return jsonify({"error": str(e)}), 400


if __name__ == '__main__':
    app.run(debug=True)