remove use of @session_logger.set_uuid_logging decorator

app.py

@@ -5,31 +5,20 @@ import sys
 from pathlib import Path
 
 import gradio as gr
-import structlog
 import uvicorn
-from dotenv import load_dotenv
 from fastapi import FastAPI
 from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
 from fastapi.templating import Jinja2Templates
 from samgis_core.utilities import create_folders_if_not_exists
 from samgis_core.utilities import frontend_builder
-from samgis_core.utilities.session_logger import setup_logging
 # for some reason this do
 from spaces import GPU as SPACES_GPU
 
+from lisa_on_cuda import routes, app_logger, project_root_folder, workdir
 from lisa_on_cuda.utils import app_helpers
-from lisa_on_cuda import routes
 
 
-load_dotenv()
-project_root_folder = Path(globals().get("__file__", "./_")).absolute().parent
-workdir = os.getenv("WORKDIR", project_root_folder)
-model_folder = Path(project_root_folder / "machine_learning_models")
-
-log_level = os.getenv("LOG_LEVEL", "INFO")
-setup_logging(log_level=log_level)
-app_logger = structlog.stdlib.get_logger()
 app_logger.info(f"PROJECT_ROOT_FOLDER:{project_root_folder}, WORKDIR:{workdir}.")
 
 folders_map = os.getenv("FOLDERS_MAP", "{}")

lisa_on_cuda/__init__.py

@@ -0,0 +1,16 @@
+import os
+from pathlib import Path
+
+import structlog
+from dotenv import load_dotenv
+from samgis_core.utilities.session_logger import setup_logging
+
+
+load_dotenv()
+project_root_folder = Path(globals().get("__file__", "./_")).absolute().parent
+workdir = os.getenv("WORKDIR", project_root_folder)
+model_folder = Path(project_root_folder / "machine_learning_models")
+
+log_level = os.getenv("LOG_LEVEL", "INFO")
+setup_logging(log_level=log_level)
+app_logger = structlog.stdlib.get_logger()

lisa_on_cuda/routes.py

@@ -1,18 +1,10 @@
 import json
-import os
 
-import structlog
-from dotenv import load_dotenv
 from fastapi import APIRouter
 
-from samgis_core.utilities.session_logger import setup_logging
+from lisa_on_cuda import app_logger
 
 
-load_dotenv()
-
-log_level = os.getenv("LOG_LEVEL", "INFO")
-setup_logging(log_level=log_level)
-app_logger = structlog.stdlib.get_logger()
 router = APIRouter()
 
 

lisa_on_cuda/utils/app_helpers.py

@@ -3,6 +3,7 @@ import logging
 import os
 import re
 from typing import Callable
+
 import cv2
 import gradio as gr
 import nh3
@@ -11,18 +12,16 @@ import torch
 import torch.nn.functional as F
 from transformers import AutoTokenizer, BitsAndBytesConfig, CLIPImageProcessor
 
-from . import constants, session_logger, utils
+from lisa_on_cuda import app_logger
 from lisa_on_cuda.LISA import LISAForCausalLM
 from lisa_on_cuda.llava import conversation as conversation_lib
 from lisa_on_cuda.llava.mm_utils import tokenizer_image_token
 from lisa_on_cuda.segment_anything.utils.transforms import ResizeLongestSide
-
+from . import constants, utils
 
 placeholders = utils.create_placeholder_variables()
-app_logger = logging.getLogger(__name__)
 
 
-@session_logger.set_uuid_logging
 def parse_args(args_to_parse, internal_logger=None):
     if internal_logger is None:
         internal_logger = app_logger
@@ -56,7 +55,6 @@ def parse_args(args_to_parse, internal_logger=None):
     return parser.parse_args(args_to_parse)
 
 
-@session_logger.set_uuid_logging
 def get_cleaned_input(input_str, internal_logger=None):
     if internal_logger is None:
         internal_logger = app_logger
@@ -89,7 +87,6 @@ def get_cleaned_input(input_str, internal_logger=None):
     return input_str
 
 
-@session_logger.set_uuid_logging
 def set_image_precision_by_args(input_image, precision):
     if precision == "bf16":
         input_image = input_image.bfloat16()
@@ -100,7 +97,6 @@ def set_image_precision_by_args(input_image, precision):
     return input_image
 
 
-@session_logger.set_uuid_logging
 def preprocess(
         x,
         pixel_mean=torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1),
@@ -120,7 +116,6 @@ def preprocess(
     return x
 
 
-@session_logger.set_uuid_logging
 def load_model_for_causal_llm_pretrained(
         version, torch_dtype, load_in_8bit, load_in_4bit, seg_token_idx, vision_tower,
         internal_logger: logging = None
@@ -166,7 +161,6 @@ def load_model_for_causal_llm_pretrained(
     return _model
 
 
-@session_logger.set_uuid_logging
 def get_model(args_to_parse, internal_logger: logging = None, inference_decorator: Callable = None):
     if internal_logger is None:
         internal_logger = app_logger
@@ -238,7 +232,6 @@ def get_model(args_to_parse, internal_logger: logging = None, inference_decorato
     return _model, _clip_image_processor, _tokenizer, _transform
 
 
-@session_logger.set_uuid_logging
 def prepare_model_vision_tower(_model, args_to_parse, torch_dtype, internal_logger: logging = None):
     if internal_logger is None:
         internal_logger = app_logger
@@ -272,7 +265,6 @@ def prepare_model_vision_tower(_model, args_to_parse, torch_dtype, internal_logg
     return _model, vision_tower
 
 
-@session_logger.set_uuid_logging
 def get_inference_model_by_args(args_to_parse, internal_logger0: logging = None, inference_decorator: Callable = None):
     if internal_logger0 is None:
         internal_logger0 = app_logger
@@ -281,7 +273,6 @@ def get_inference_model_by_args(args_to_parse, internal_logger0: logging = None,
     internal_logger0.info("created model, preparing inference function")
     no_seg_out = placeholders["no_seg_out"]
 
-    @session_logger.set_uuid_logging
     def inference(
             input_str: str,
             input_image: str | np.ndarray,
@@ -408,7 +399,6 @@ def get_inference_model_by_args(args_to_parse, internal_logger0: logging = None,
     return inference
 
 
-@session_logger.set_uuid_logging
 def get_gradio_interface(
         fn_inference: Callable,
         args: str = None