app.py

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

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

# Load the trained model
model = joblib.load('model.joblib')

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

# Define the prediction endpoint
@app.route('/predict', methods=['POST'])
def predict():
    try:
        # Get JSON data from the request
        data = request.get_json()
        
        # Convert input to pandas DataFrame
        if isinstance(data, dict):
             df = pd.DataFrame([data])
        else:
             df = pd.DataFrame(data)

        # Make prediction
        prediction = model.predict(df)

        # Return the result as JSON
        return jsonify({
            'status': 'success',
            'prediction': prediction.tolist()
        })
        
    except Exception as e:
        return jsonify({
            'status': 'error',
            'message': str(e)
        })

# Run the app on port 7860 for Hugging Face Spaces
if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860)