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

mini-nav/benchmarks/tasks/retrieval.py

@@ -6,11 +6,10 @@ import lancedb
 import pyarrow as pa
 from benchmarks.base import BaseBenchmarkTask
 from benchmarks.tasks.registry import RegisterTask
+from rich.progress import track
 from torch import nn
 from torch.utils.data import DataLoader
-from tqdm.auto import tqdm
 from transformers import BitImageProcessorFast
-
 from utils.feature_extractor import extract_batch_features, infer_vector_dim
 
 
@@ -47,11 +46,11 @@ def _establish_eval_database(
         dataloader: DataLoader for the training dataset.
     """
     # Extract all features using the utility function
-    all_features = extract_batch_features(processor, model, dataloader, show_progress=True)
+    all_features = extract_batch_features(processor, model, dataloader)
 
     # Store features to database
     global_idx = 0
-    for batch in tqdm(dataloader, desc="Storing eval database"):
+    for batch in track(dataloader, description="Storing eval database"):
         labels = batch["label"]
         labels_list = labels.tolist()
         batch_size = len(labels_list)
@@ -89,13 +88,13 @@ def _evaluate_recall(
         A tuple of (correct_count, total_count).
     """
     # Extract all features using the utility function
-    all_features = extract_batch_features(processor, model, dataloader, show_progress=True)
+    all_features = extract_batch_features(processor, model, dataloader)
 
     correct = 0
     total = 0
     feature_idx = 0
 
-    for batch in tqdm(dataloader, desc=f"Evaluating Recall@{top_k}"):
+    for batch in track(dataloader, description=f"Evaluating Recall@{top_k}"):
         labels = batch["label"]
         labels_list = labels.tolist()