Refactor image_to_data_uri and main functions for improved readability and consistency

vlm-classify.py

@@ -63,9 +63,11 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
-def image_to_data_uri(image: Union[Image.Image, Dict[str, Any]], max_size: Optional[int] = None) -> str:
+def image_to_data_uri(
+    image: Union[Image.Image, Dict[str, Any]], max_size: Optional[int] = None
+) -> str:
     """Convert image to base64 data URI for VLM processing.
-    
+
     Args:
         image: PIL Image or dict with image bytes
         max_size: Optional maximum dimension (width or height) to resize to.
@@ -77,17 +79,17 @@ def image_to_data_uri(image: Union[Image.Image, Dict[str, Any]], max_size: Optio
         pil_img = Image.open(io.BytesIO(image["bytes"]))
     else:
         raise ValueError(f"Unsupported image type: {type(image)}")
-    
+
     # Resize if max_size is specified and image exceeds it
     if max_size and (pil_img.width > max_size or pil_img.height > max_size):
         # Use thumbnail to preserve aspect ratio
         pil_img = pil_img.copy()  # Don't modify original
         pil_img.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
-    
+
     # Convert to RGB if necessary (handle RGBA, grayscale, etc.)
     if pil_img.mode not in ("RGB", "L"):
         pil_img = pil_img.convert("RGB")
-    
+
     # Convert to base64
     buf = io.BytesIO()
     pil_img.save(buf, format="JPEG", quality=95)
@@ -102,7 +104,7 @@ def create_classification_messages(
 ) -> List[Dict]:
     """Create chat messages for VLM classification."""
     image_uri = image_to_data_uri(image, max_size=max_size)
-    
+
     return [
         {
             "role": "user",
@@ -132,50 +134,52 @@ def main(
     private: bool = False,
 ):
     """Classify images from a dataset using a Vision Language Model."""
-    
+
     # Check GPU availability
     if not torch.cuda.is_available():
         logger.error("CUDA is not available. This script requires a GPU.")
         logger.error("If running locally, ensure you have a CUDA-capable GPU.")
         logger.error("For cloud execution, use: hf jobs uv run --flavor a10g ...")
         sys.exit(1)
-    
+
     # Parse classes
     class_list = [c.strip() for c in classes.split(",")]
     logger.info(f"Classes: {class_list}")
-    
+
     # Create default prompt if not provided
     if prompt is None:
         prompt = f"Classify this image into one of the following categories: {', '.join(class_list)}"
     logger.info(f"Prompt template: {prompt}")
-    
+
     # Login to HF if token provided
     HF_TOKEN = hf_token or os.environ.get("HF_TOKEN")
     if HF_TOKEN:
         login(token=HF_TOKEN)
-    
+
     # Load dataset
     logger.info(f"Loading dataset: {input_dataset}")
     dataset = load_dataset(input_dataset, split=split)
-    
+
     # Validate image column
     if image_column not in dataset.column_names:
-        raise ValueError(f"Column '{image_column}' not found. Available: {dataset.column_names}")
-    
+        raise ValueError(
+            f"Column '{image_column}' not found. Available: {dataset.column_names}"
+        )
+
     # Limit samples if requested
     if max_samples:
         dataset = dataset.select(range(min(max_samples, len(dataset))))
         logger.info(f"Limited to {len(dataset)} samples")
-    
+
     # Log resizing configuration
     if max_size:
         logger.info(f"Image resizing enabled: max dimension = {max_size}px")
-    
+
     # Auto-detect tensor parallel size if not specified
     if tensor_parallel_size is None:
         tensor_parallel_size = torch.cuda.device_count()
         logger.info(f"Auto-detected {tensor_parallel_size} GPUs for tensor parallelism")
-    
+
     # Initialize vLLM
     logger.info(f"Loading model: {model}")
     llm_kwargs = {
@@ -184,25 +188,25 @@ def main(
         "tensor_parallel_size": tensor_parallel_size,
         "trust_remote_code": True,  # Required for some VLMs
     }
-    
+
     if max_model_len:
         llm_kwargs["max_model_len"] = max_model_len
-    
+
     llm = LLM(**llm_kwargs)
-    
+
     # Create guided decoding params for classification
     guided_decoding_params = GuidedDecodingParams(choice=class_list)
     sampling_params = SamplingParams(
         temperature=0.1,  # Low temperature for consistent classification
-        max_tokens=50,    # Classifications are short
+        max_tokens=50,  # Classifications are short
         guided_decoding=guided_decoding_params,
     )
-    
+
     # Process images in batches to avoid memory issues
     logger.info(f"Processing {len(dataset)} images in batches of {batch_size}")
-    
+
     all_classifications = []
-    
+
     # Process in batches using lazy loading
     for batch_indices in tqdm(
         partition_all(batch_size, range(len(dataset))),
@@ -210,11 +214,11 @@ def main(
         desc="Classifying images",
     ):
         batch_indices = list(batch_indices)
-        
+
         # Load only this batch's images
         batch_images = []
         valid_batch_indices = []
-        
+
         for idx in batch_indices:
             try:
                 image = dataset[idx][image_column]
@@ -223,24 +227,24 @@ def main(
             except Exception as e:
                 logger.warning(f"Skipping image at index {idx}: {e}")
                 all_classifications.append(None)
-        
+
         if not batch_images:
             continue
-            
+
         try:
             # Create messages for just this batch
             batch_messages = [
-                create_classification_messages(img, prompt, max_size=max_size) 
+                create_classification_messages(img, prompt, max_size=max_size)
                 for img in batch_images
             ]
-            
+
             # Process with vLLM
             outputs = llm.chat(
                 messages=batch_messages,
                 sampling_params=sampling_params,
                 use_tqdm=False,  # Already have outer progress bar
             )
-            
+
             # Extract classifications
             for output in outputs:
                 if output.outputs:
@@ -249,35 +253,37 @@ def main(
                 else:
                     all_classifications.append(None)
                     logger.warning("Empty output for an image")
-                    
+
         except Exception as e:
             logger.error(f"Error processing batch: {e}")
             # Add None for failed batch
             all_classifications.extend([None] * len(batch_images))
-    
+
     # Ensure we have the right number of classifications
     while len(all_classifications) < len(dataset):
         all_classifications.append(None)
-    
+
     # Add classifications to dataset
     logger.info("Adding classifications to dataset...")
-    dataset = dataset.add_column("label", all_classifications[:len(dataset)])
-    
+    dataset = dataset.add_column("label", all_classifications[: len(dataset)])
+
     # Push to hub
     logger.info(f"Pushing to {output_dataset}...")
     dataset.push_to_hub(output_dataset, private=private, token=HF_TOKEN)
-    
+
     # Print summary
     logger.info("Classification complete!")
     logger.info(f"Processed {len(all_classifications)} images")
     logger.info(f"Output dataset: {output_dataset}")
-    
+
     # Show distribution of classifications
     label_counts = Counter(all_classifications)
     logger.info("Classification distribution:")
     for label, count in sorted(label_counts.items()):
         if label is not None:  # Skip None values in summary
-            percentage = (count / len(all_classifications)) * 100 if all_classifications else 0
+            percentage = (
+                (count / len(all_classifications)) * 100 if all_classifications else 0
+            )
             logger.info(f"  {label}: {count} ({percentage:.1f}%)")
 
 
@@ -309,7 +315,7 @@ Examples:
         --classes "title-page,content,index,other"
         """,
     )
-    
+
     parser.add_argument(
         "input_dataset",
         help="Input dataset ID on Hugging Face Hub",
@@ -389,15 +395,15 @@ Examples:
         action="store_true",
         help="Make output dataset private",
     )
-    
+
     args = parser.parse_args()
-    
+
     # Show example command if no arguments
     if len(sys.argv) == 1:
         parser.print_help()
-        print("\n" + "="*60)
+        print("\n" + "=" * 60)
         print("Example HF Jobs command:")
-        print("="*60)
+        print("=" * 60)
         print("""
 hf jobs uv run \\
     --flavor a10g \\
@@ -410,7 +416,7 @@ hf jobs uv run \\
     --max-samples 100
         """)
         sys.exit(0)
-    
+
     main(
         input_dataset=args.input_dataset,
         output_dataset=args.output_dataset,
@@ -427,4 +433,4 @@ hf jobs uv run \\
         split=args.split,
         hf_token=args.hf_token,
         private=args.private,
-    )
+    )