app.py

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

# Load model
model = joblib.load("tuned_random_forest.joblib")

# Preprocessing pipeline must match training
# For now, assume incoming JSON already matches feature schema

app = Flask(__name__)

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

@app.route("/health", methods=["GET"])
def health():
    return jsonify({"status": "ok"}), 200

@app.post('/predict')
def predict():
    try:
        data = request.get_json()
        df = pd.DataFrame([data])  # single row input
        y_pred_log = model.predict(df)
        y_pred = np.expm1(y_pred_log)  # inverse log1p
        return jsonify({"prediction": float(y_pred[0])})
    except Exception as e:
        return jsonify({"error": str(e)}), 400

if __name__ == '__main__':
    app.run(host="0.0.0.0", port=7860, debug=True)