refactor(ui): replace tqdm with rich for enhanced console output

mini-nav/utils/feature_extractor.py

@@ -7,7 +7,7 @@ from PIL import Image
 from torch import nn
 from torch.utils.data import DataLoader
 from transformers import BitImageProcessorFast
-from tqdm.auto import tqdm
+from rich.progress import track
 
 
 def _extract_features_from_output(output: Any) -> torch.Tensor:
@@ -86,7 +86,6 @@ def extract_batch_features(
     model: nn.Module,
     images: Union[List[Any], Any],
     batch_size: int = 32,
-    show_progress: bool = False,
 ) -> torch.Tensor:
     """Extract features from a batch of images.
 
@@ -95,7 +94,6 @@ def extract_batch_features(
         model: Feature extraction model.
         images: List of images, DataLoader, or other iterable.
         batch_size: Batch size for processing.
-        show_progress: Whether to show progress bar.
 
     Returns:
         Tensor of shape [batch_size, feature_dim].
@@ -106,8 +104,7 @@ def extract_batch_features(
     # Handle DataLoader input
     if isinstance(images, DataLoader):
         all_features = []
-        iterator = tqdm(images, desc="Extracting features") if show_progress else images
-        for batch in iterator:
+        for batch in track(images, description="Extracting features"):
             imgs = batch["img"] if isinstance(batch, dict) else batch[0]
             inputs = processor(images=imgs, return_tensors="pt")
             inputs.to(device)
@@ -118,8 +115,7 @@ def extract_batch_features(
 
     # Handle list of images
     all_features = []
-    iterator = tqdm(range(0, len(images), batch_size), desc="Extracting features") if show_progress else range(0, len(images), batch_size)
-    for i in iterator:
+    for i in track(range(0, len(images), batch_size), description="Extracting features"):
         batch_imgs = images[i : i + batch_size]
         inputs = processor(images=batch_imgs, return_tensors="pt")
         inputs.to(device)