feat(threading): allow use external rich.console

wdtagger/__init__.py

@@ -9,14 +9,9 @@ import huggingface_hub
 import numpy as np
 import onnxruntime as rt
 import pandas as pd
-import rich
-import rich.live
 from PIL import Image
 from rich.logging import RichHandler
 
-# Access console for rich text and logging
-console = rich.get_console()
-
 HF_TOKEN = os.environ.get("HF_TOKEN", "")
 MODEL_FILENAME = "model.onnx"
 LABEL_FILENAME = "selected_tags.csv"
@@ -192,6 +187,7 @@ class Tagger:
         loglevel=logging.INFO,
         num_threads=None,
         providers=None,
+        console=None,
     ):
         """Initialize the Tagger object with the model repository and tokens.
 
@@ -201,7 +197,17 @@ class Tagger:
             hf_token (str, optional): HuggingFace token for authentication. Defaults to HF_TOKEN.
             loglevel (int, optional): Logging level. Defaults to logging.INFO.
             num_threads (int, optional): Number of threads for ONNX runtime. Defaults to None.
+            providers (list, optional): List of providers for ONNX runtime. Defaults to None.
+            console (rich.console.Console, optional): Rich console object. Defaults to None.
         """
+
+        if not console:
+            from rich import get_console
+
+            self.console = get_console()
+        else:
+            self.console = console
+
         if providers is None:
             providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
         self.logger = logging.getLogger("wdtagger")
@@ -234,7 +240,7 @@ class Tagger:
             hf_token (str, optional): HuggingFace token for authentication. Defaults to None.
             num_threads (int, optional): Number of threads for ONNX runtime. Defaults to None.
         """
-        with console.status("Loading model..."):
+        with self.console.status("Loading model..."):
             csv_path = huggingface_hub.hf_hub_download(
                 model_repo,
                 LABEL_FILENAME,