app.py

# app.py

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

# Define the model filename
MODEL_FILE = 'tuned_xgb_sales_forecaster.pkl'

# Define the 10 feature columns expected by the model pipeline
FEATURE_COLS = [
    'Product_Weight', 'Product_Sugar_Content', 'Product_Allocated_Area',
    'Product_Type', 'Product_MRP', 'Store_Size',
    'Store_Location_City_Type', 'Store_Type', 'Store_Age',
    'Product_Category_Simplified'
]

# --- Load the Model Pipeline ---
try:
    model_pipeline = joblib.load(MODEL_FILE)
    print("Model loaded successfully.")
except Exception as e:
    print(f"CRITICAL ERROR: Model not found: {e}. Check Dockerfile.")
    model_pipeline = None

app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict_sales():
    if model_pipeline is None:
        return jsonify({'error': 'Server setup error: Model not loaded.'}), 500

    try:
        data = request.get_json(force=True)
        # Ensure input data matches the feature columns
        input_df = pd.DataFrame(data, columns=FEATURE_COLS)

        # Prediction on log scale
        log_prediction = model_pipeline.predict(input_df)

        # Inverse transformation: sales = exp(y) - 1
        prediction_original_scale = np.expm1(log_prediction)

        response = {
            'status': 'success',
            'predicted_sales_revenue': round(prediction_original_scale[0], 2)
        }
        return jsonify(response)

    except Exception as e:
        return jsonify({'error': f'Prediction logic failed: {str(e)}'}), 400

if __name__ == '__main__':
    # This runs the server inside the Docker container
    app.run(host='0.0.0.0', port=5000)