app.py

import os
import joblib
from flask import Flask, request, jsonify
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings("ignore")

# Define the path to the serialised model
MODEL_PATH = "/content/Backend_files/SuperKart_Sales_Prediction_Model.joblib"

# Load the trained model pipeline
try:
    model_pipeline = joblib.load(MODEL_PATH)
    print(f"Model loaded successfully from {MODEL_PATH}")
except Exception as e:
    model_pipeline = None
    print(f"Error loading model: {e}")

# Initialize the Flask application
app = Flask(__name__)

# Define a route for the home page
@app.route("/", methods=["GET"])
def home():
    return "Welcome to the SuperKart Sales Prediction App!"

# Define an endpoint for making predictions
@app.route("/predict", methods=["POST"])
def predict():
    if model_pipeline is None:
        return jsonify({"error": "Model not loaded"}), 500

    try:
        # Get JSON data from the request
        data = request.get_json()
        if not data:
            return jsonify({"error": "No data provided"}), 400

        # Extract features from the JSON data
        input_df = pd.DataFrame([data])

        prediction = model_pipeline.predict(input_df)
        return jsonify({"prediction": prediction.tolist()})

    except Exception as e:
        return jsonify({"error": f'Error during prediction: {e}'}), 500

if __name__ == "__main__":  # Correct indentation
    port = int(os.environ.get("PORT", 5000))
    app.run(host="0.0.0.0", port=5000, debug=True)