app.py

# Import necessary libraries
import numpy as np
import joblib  
import pandas as pd  
import os 
from flask import Flask, request, jsonify 

# Initialize the Flask application
sales_predictor_api = Flask("SuperKart Sales Predictor API")

# Load the trained machine learning model
# CRITICAL FIX: The file name is CORRECTED here to match the file you uploaded!
MODEL_PATH = os.path.join(os.getcwd(), "best_xgb_pipeline.joblib")

try:
    model = joblib.load(MODEL_PATH)
    print(f"Model loaded successfully from: {MODEL_PATH}")
except Exception as e:
    print(f"FATAL ERROR: Could not load model: {e}")
    model = None 

@sales_predictor_api.get('/')
def home():
    """Returns a simple welcome message for the SuperKart API."""
    return "Welcome to the SuperKart Sales Prediction API!"

@sales_predictor_api.post('/v1/sales')
def predict_single_sale():
    """Handles POST requests for a single sales forecast."""
    if model is None:
        return jsonify({'error': 'Internal server error: Model failed to load at startup.'}), 500
        
    try:
        property_data = request.get_json()
        input_data = pd.DataFrame([property_data])
        
        # Predicts actual sales total directly
        predicted_sales = model.predict(input_data)[0]
        predicted_sales = round(float(predicted_sales), 2)

        return jsonify({'Predicted Total Sales': predicted_sales})
    except Exception as e:
        return jsonify({'error': str(e), 'message': f'Prediction failed: {str(e)}'}), 400

@sales_predictor_api.post('/v1/salesbatch')
def predict_sales_batch():
    """Handles POST requests for batch sales forecasts via CSV upload."""
    if model is None:
        return jsonify({'error': 'Internal server error: Model failed to load at startup.'}), 500
        
    try:
        file = request.files['file']
        input_data = pd.read_csv(file)
        predicted_sales = model.predict(input_data).tolist()
        final_predictions = [round(float(sale), 2) for sale in predicted_sales]

        property_ids = input_data['id'].tolist()
        output_dict = dict(zip(property_ids, final_predictions))
        
        return jsonify(output_dict)
    except Exception as e:
        return jsonify({'error': str(e), 'message': f'Batch prediction failed: {str(e)}'}), 400


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