hosting_script.py

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

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = Flask(__name__)

try:
    model = joblib.load('model.joblib')
    columns = joblib.load('columns.joblib')
    logger.info("Model and columns loaded successfully in hosting script.")
except Exception as e:
    logger.error(f"Failed to load model or columns: {e}")
    raise

@app.route('/', methods=['GET'])
def index():
    logger.info("Root endpoint called.")
    return jsonify({'status': 'ok'})

@app.route('/health', methods=['GET'])
def health():
    logger.info("Health check endpoint called.")
    return jsonify({'status': 'healthy'})

@app.route('/predict', methods=['POST'])
def predict():
    try:
        logger.info("Predict endpoint called with data: %s", request.get_json(force=True))
        data = request.get_json(force=True)
        input_df = pd.DataFrame(data)
        categorical_columns = ['Occupation', 'Gender', 'ProductPitched', 'MaritalStatus', 'Designation']
        input_encoded = pd.get_dummies(input_df, columns=categorical_columns, drop_first=True)
        input_encoded = input_encoded.reindex(columns=columns, fill_value=0)
        prediction = model.predict(input_encoded)
        logger.info("Prediction made: %s", prediction.tolist())
        return jsonify({'prediction': prediction.tolist()})
    except Exception as e:
        logger.error(f"Prediction failed: %s", str(e))
        return jsonify({'error': str(e)}), 400

# Do not call app.run(); waitress will serve it via Docker CMD