Sync turing folder from GitHub

turing/api/app.py

@@ -1,17 +1,19 @@
 import base64
+import logging
 import os
 from typing import Literal
 
 from fastapi import FastAPI, HTTPException, Query
 from fastapi.responses import JSONResponse
 import gradio as gr
-from loguru import logger
 
 from turing.api.demo import create_demo
 from turing.api.resource_monitoring import PrometheusBodyMiddleware, instrumentator
 from turing.api.schemas import PredictionRequest, PredictionResponse
 from turing.modeling.predict import ModelInference
 
+logger = logging.getLogger(__name__)
+
 
 def get_logo_b64_src(filename="logo_header.svg"):
     """read SVG and convert it into a string Base64 for HTML."""

turing/config.py

@@ -1,6 +1,10 @@
+import logging
+import os
 from pathlib import Path
+import sys
 
 from dotenv import load_dotenv
+from logtail import LogtailHandler
 from loguru import logger
 
 # Load environment variables from .env file if it exists
@@ -112,3 +116,27 @@ try:
     logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True)
 except (ModuleNotFoundError, ValueError):
     pass
+
+
+# setup logging for Better Stack using LogtailHandler
+try:
+    better_stack_handler = LogtailHandler(
+        source_token=os.getenv("BETTER_STACK_TOKEN"),
+        host=os.getenv("BETTER_STACK_HOST"),
+    )
+
+    root_logger = logging.getLogger()
+    root_logger.setLevel(logging.INFO)
+
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setLevel(logging.DEBUG)
+
+    better_stack_handler.setLevel(logging.WARNING)
+
+    root_logger.addHandler(console_handler)
+    root_logger.addHandler(better_stack_handler)
+
+    logging.info("LogtailHandler for Better Stack configured successfully.")
+
+except Exception as e:
+    logging.error(f"Failed to configure LogtailHandler: {e}")

turing/modeling/predict.py

@@ -1,8 +1,8 @@
 import importlib
+import logging
 import warnings
 
 import dagshub
-from loguru import logger
 import mlflow
 import numpy as np
 import pandas as pd
@@ -12,6 +12,8 @@ from turing.dataset import DatasetManager
 from turing.modeling.model_selector import get_best_model_info
 from turing.modeling.models.codeBerta import CodeBERTa
 
+logger = logging.getLogger(__name__)
+
 
 class ModelInference:
     # Model Configuration (Fallback Registry)
@@ -120,7 +122,7 @@ class ModelInference:
         mlflow.artifacts.download_artifacts(
             run_id=run_id, artifact_path=artifact_name, dst_path=str(local_path.parent)
         )
-        logger.success(f"Model downloaded and cached at: {local_path}")
+        logger.info(f"Model downloaded and cached at: {local_path}")
 
         return str(local_path)
 
@@ -159,7 +161,7 @@ class ModelInference:
 
             # Load Model and store in cache
             self.loaded_models[language] = model_class(language=language, path=model_path)
-            logger.success(f"Model for {language} loaded into memory.")
+            logger.info(f"Model for {language} loaded into memory.")
         
         model = self.loaded_models[language]