app.py

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

# Initialize Flask app
app = Flask("SuperKart Sales Predictor")

# Load the trained model pipeline from the file
# This is loaded only once when the application starts
try:
    model = joblib.load("superkart_sales_pipeline.joblib")
    print("Model loaded successfully.")
except FileNotFoundError:
    print("Model file not found. Make sure 'superkart_sales_pipeline.joblib' is in the same directory.")
    model = None
except Exception as e:
    print(f"An error occurred while loading the model: {e}")
    model = None

# Define a root endpoint for a health check
@app.route('/', methods=['GET'])
def home():
    """A simple endpoint to confirm the API is running."""
    return "Welcome to the SuperKart Sales Prediction API!"

# Define the main endpoint for making sales predictions
@app.route('/predict', methods=['POST'])
def predict_sales():
    """
    Receives a JSON object with features and returns a sales prediction.
    """
    if model is None:
        return jsonify({'error': 'Model is not loaded or failed to load.'}), 500

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

    try:
        # Convert the JSON data into a pandas DataFrame
        # The `index=[0]` is crucial for creating a single-row DataFrame
        features_df = pd.DataFrame(input_data, index=[0])

        # Make a prediction using the full pipeline
        # The pipeline handles all preprocessing steps (scaling, encoding, etc.)
        prediction = model.predict(features_df)

        # The prediction is a numpy array, so we extract the single value
        predicted_value = prediction[0]

        # Return the prediction in a JSON response, rounded to 2 decimal places
        return jsonify({'predicted_sales': round(predicted_value, 2)})

    except (KeyError, TypeError) as e:
        # This catches errors if the input JSON is missing keys or malformed
        return jsonify({'error': f'Invalid input data format: {str(e)}'}), 400
    except Exception as e:
        # A general catch-all for any other unexpected errors
        return jsonify({'error': f'An unexpected error occurred: {str(e)}'}), 500

# This block is for deployment environments like Hugging Face Spaces
if __name__ == '__main__':
    # The port is determined by the environment variable, defaulting to 7860
    port = int(os.environ.get("PORT", 7860))
    # Running on 0.0.0.0 makes the app accessible from outside the container
    app.run(host='0.0.0.0', port=port)