Upload folder using huggingface_hub

llm_ocr/document.py

@@ -357,6 +357,65 @@ def display_markdown(sample: Dict[str, Any]) -> None:
     display(Markdown(rendered))
 
 
+def display_samples(dataset, num_samples: int = 2) -> None:
+    """
+    Display a few samples from a dataset with images and figure descriptions.
+    
+    Shows source images, markdown previews, extracted figures with their
+    descriptions from metadata.
+    
+    Args:
+        dataset: A Hugging Face dataset or list of samples
+        num_samples: Number of samples to display (default 2)
+    """
+    from IPython.display import display
+    
+    print(f"Dataset: {len(dataset)} samples")
+    print(f"Columns: {list(dataset.column_names)}")
+    print()
+    
+    for i in range(min(num_samples, len(dataset))):
+        sample = dataset[i]
+        print(f"=== Sample {i}: {sample.get('sample_id', i)} ===")
+        
+        # Show source image
+        if sample.get('source_image'):
+            print("Source image:")
+            img = sample['source_image']
+            img.thumbnail((500, 500))  # Resize to max 500px
+            display(img)
+        
+        # Show markdown preview
+        md = sample.get('document_markdown') or sample.get('document_markdown_text', '')
+        if md:
+            print(f"\nMarkdown preview ({len(md)} chars):")
+            print(md[:500] + '...' if len(md) > 500 else md)
+        
+        # Show final markdown if available
+        final_md = sample.get('document_final_markdown') or sample.get('document_final_markdown_text', '')
+        if final_md:
+            print(f"\nFinal markdown preview ({len(final_md)} chars):")
+            print(final_md[:500] + '...' if len(final_md) > 500 else final_md)
+        
+        # Show figures and their descriptions
+        figures = sample.get('extracted_figures', [])
+        metadata = sample.get('extracted_figures_metadata', [])
+        if figures:
+            print(f"\nExtracted figures: {len(figures)}")
+            for j, fig in enumerate(figures[:2]):  # Show max 2 figures
+                fig.thumbnail((500, 500))
+                display(fig)
+                # Show figure description if available
+                if j < len(metadata):
+                    try:
+                        meta = json.loads(metadata[j]) if isinstance(metadata[j], str) else metadata[j]
+                        if meta.get('description'):
+                            print(f"  📝 Description: {meta['description'][:200]}...")
+                    except Exception:
+                        pass
+        print()
+
+
 __all__ = [
     "encode_image",
     "build_document_markdown",
@@ -364,6 +423,7 @@ __all__ = [
     "render_markdown_with_images",
     "render_sample_markdown",
     "display_markdown",
+    "display_samples",
     "write_text",
     "write_json",
 ]

llm_ocr/storage.py

@@ -82,6 +82,13 @@ class HFHubStorage(DatasetStorage):
             LOGGER.debug("HF Hub not configured, skipping dataset save")
             return False
         
+        # Fail early if token is missing or empty
+        if not self._token:
+            raise ValueError(
+                "HF_TOKEN is required to push datasets to the Hub. "
+                "Set the HF_TOKEN environment variable with a token that has write permissions."
+            )
+        
         try:
             dataset.push_to_hub(
                 self.repo_id,
@@ -93,7 +100,7 @@ class HFHubStorage(DatasetStorage):
             return True
         except Exception as exc:
             LOGGER.exception("HF Hub dataset push failed: %s", exc)
-            return False
+            raise
     
     def load_dataset(self, split: str = "train") -> Optional["Dataset"]:
         if not self.is_configured: