File size: 2,222 Bytes
5a58b2b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | import joblib
import os
import logging
import numpy as np
from app.core.config import get_settings
logger = logging.getLogger(__name__)
settings = get_settings()
class ModelService:
_model = None
@classmethod
def load_model(cls):
if cls._model is not None:
return
try:
if os.path.exists(settings.MODEL_PATH):
logger.info(f"Loading model from {settings.MODEL_PATH}...")
cls._model = joblib.load(settings.MODEL_PATH)
logger.info("Model loaded successfully!")
else:
logger.error(f"Model file {settings.MODEL_PATH} not found! Using Mock Model for demonstration.")
cls._model = MockModel()
except Exception as e:
logger.error(f"Failed to load model: {e}")
cls._model = MockModel()
@classmethod
def predict(cls, input_vector: list) -> dict:
if cls._model is None:
cls.load_model()
input_array = [input_vector]
# Predict
try:
prediction_prob = cls._model.predict_proba(input_array)[0][1]
prediction_class = int(cls._model.predict(input_array)[0])
except AttributeError:
# Fallback for mock model or weird scikit versions
prediction_prob = 0.5
prediction_class = 0
risk_level, risk_label = cls._calculate_risk_level(prediction_prob)
return {
"probability": float(prediction_prob),
"class": prediction_class,
"risk_level": risk_level,
"risk_label": risk_label
}
@staticmethod
def _calculate_risk_level(prob: float):
if prob <= 0.20:
return 1, "Very Low"
elif prob <= 0.40:
return 2, "Low"
elif prob <= 0.60:
return 3, "Moderate"
elif prob <= 0.80:
return 4, "High"
else:
return 5, "Very High"
class MockModel:
"""Mock model for when the real model is missing or fails to load."""
def predict_proba(self, X):
return np.array([[0.5, 0.5]]) # 50% chance
def predict(self, X):
return np.array([0])
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