risk_model.py

import numpy as np
from joblib import load
import os
import logging

# Setup logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

# Define model path
MODEL_PATH = os.path.join(os.path.dirname(__file__), "models", "heating_model_with_risk_score.pkl")

# Load the trained model
try:
    model = load(MODEL_PATH)
    logger.info(f"Model loaded successfully from '{MODEL_PATH}'.")
except FileNotFoundError:
    logger.error(f"Trained model '{MODEL_PATH}' not found. Please run train_model.py first.")
    raise FileNotFoundError(f"Trained model '{MODEL_PATH}' not found. Please run train_model.py first.")

def predict_risk(temp, duration):
    """
    Predicts the risk level, risk score, and alert using the trained Random Forest model.

    Args:
        temp (float): The maximum temperature of the heating mantle.
        duration (float): The duration the mantle is used.

    Returns:
        tuple: (risk_level, risk_score, alert)
    """
    # Input validation
    if not (50 <= temp <= 200):
        logger.error("Temperature must be between 50 and 200°C.")
        raise ValueError("Temperature must be between 50 and 200°C.")
    if not (5 <= duration <= 120):
        logger.error("Duration must be between 5 and 120 minutes.")
        raise ValueError("Duration must be between 5 and 120 minutes.")

    # Prepare input for model
    input_data = np.array([[temp, duration]])

    # Predict risk level
    try:
        risk_level = model.predict(input_data)[0]
    except Exception as e:
        logger.error(f"Error during prediction: {e}")
        raise

    # Get probability scores
    try:
        probabilities = model.predict_proba(input_data)[0]
        class_order = model.classes_
        risk_score = round(probabilities[np.where(class_order == risk_level)][0] * 100, 2)
    except Exception as e:
        logger.error(f"Error calculating risk score: {e}")
        raise

    # Assign alert based on risk level
    alert_map = {"Low": "Safe", "Moderate": "Risk", "High": "High Risk"}
    alert = alert_map.get(risk_level, "Unknown")
    logger.info(f"Prediction: Risk Level={risk_level}, Risk Score={risk_score}%, Alert={alert}")

    return risk_level, risk_score, alert