Upload glm-ocr.py with huggingface_hub

glm-ocr.py

@@ -6,7 +6,6 @@
 #     "huggingface-hub",
 #     "pillow",
 #     "vllm",
-#     "tqdm",
 #     "toolz",
 #     "torch",
 # ]
@@ -58,7 +57,6 @@ from datasets import load_dataset
 from huggingface_hub import DatasetCard, login
 from PIL import Image
 from toolz import partition_all
-from tqdm.auto import tqdm
 from vllm import LLM, SamplingParams
 
 logging.basicConfig(level=logging.INFO)
@@ -291,15 +289,20 @@ def main(
     logger.info(f"Output will be written to column: {output_column}")
 
     all_outputs = []
+    total_batches = (len(dataset) + batch_size - 1) // batch_size
+    processed = 0
 
-    for batch_indices in tqdm(
-        partition_all(batch_size, range(len(dataset))),
-        total=(len(dataset) + batch_size - 1) // batch_size,
-        desc="GLM-OCR processing",
+    for batch_num, batch_indices in enumerate(
+        partition_all(batch_size, range(len(dataset))), 1
     ):
         batch_indices = list(batch_indices)
         batch_images = [dataset[i][image_column] for i in batch_indices]
 
+        logger.info(
+            f"Batch {batch_num}/{total_batches} "
+            f"({processed}/{len(dataset)} images done)"
+        )
+
         try:
             batch_messages = [
                 make_ocr_message(img, task=task)
@@ -312,9 +315,12 @@ def main(
                 text = output.outputs[0].text.strip()
                 all_outputs.append(text)
 
+            processed += len(batch_images)
+
         except Exception as e:
             logger.error(f"Error processing batch: {e}")
             all_outputs.extend(["[OCR ERROR]"] * len(batch_images))
+            processed += len(batch_images)
 
     processing_duration = datetime.now() - start_time
     processing_time_str = f"{processing_duration.total_seconds() / 60:.1f} min"