Fix temp file handling in DeepSeek-OCR

Replace NamedTemporaryFile with simple temp directory approach:
- Create temp dir once at start
- Reuse single temp file path for all images
- Clean up temp dir at end

This matches the official example pattern where model.infer()
expects a real file path string.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

deepseek-ocr.py

@@ -40,6 +40,7 @@ import argparse
 import json
 import logging
 import os
+import shutil
 import sys
 import tempfile
 from datetime import datetime
@@ -199,39 +200,30 @@ def process_single_image(
     base_size: int,
     image_size: int,
     crop_mode: bool,
+    temp_image_path: str,
 ) -> str:
     """Process a single image through DeepSeek-OCR."""
-    # model.infer expects a file path, so save to temp file
-    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
-        try:
-            # Convert to RGB if needed
-            if image.mode != "RGB":
-                image = image.convert("RGB")
-
-            # Save image
-            image.save(tmp.name, format="PNG")
-
-            # Run inference
-            result = model.infer(
-                tokenizer,
-                prompt=prompt,
-                image_file=tmp.name,
-                output_path="",  # Don't save intermediate files
-                base_size=base_size,
-                image_size=image_size,
-                crop_mode=crop_mode,
-                save_results=False,
-                test_compress=False,
-            )
-
-            return result if isinstance(result, str) else str(result)
+    # Convert to RGB if needed
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    # Save to temp file (model.infer expects a file path)
+    image.save(temp_image_path, format="PNG")
+
+    # Run inference
+    result = model.infer(
+        tokenizer,
+        prompt=prompt,
+        image_file=temp_image_path,
+        output_path="",  # Don't save intermediate files
+        base_size=base_size,
+        image_size=image_size,
+        crop_mode=crop_mode,
+        save_results=False,
+        test_compress=False,
+    )
 
-        finally:
-            # Clean up temp file
-            try:
-                os.unlink(tmp.name)
-            except:
-                pass
+    return result if isinstance(result, str) else str(result)
 
 
 def main(
@@ -341,35 +333,48 @@ def main(
     logger.info(f"Processing {len(dataset)} images (sequential, no batching)")
     logger.info("Note: This may be slower than vLLM-based scripts")
 
-    for i in tqdm(range(len(dataset)), desc="OCR processing"):
-        try:
-            image = dataset[i][image_column]
-
-            # Handle different image formats
-            if isinstance(image, dict) and "bytes" in image:
-                from io import BytesIO
-                image = Image.open(BytesIO(image["bytes"]))
-            elif isinstance(image, str):
-                image = Image.open(image)
-            elif not isinstance(image, Image.Image):
-                raise ValueError(f"Unsupported image type: {type(image)}")
-
-            # Process image
-            result = process_single_image(
-                model_obj,
-                tokenizer,
-                image,
-                prompt,
-                final_base_size,
-                final_image_size,
-                final_crop_mode,
-            )
-
-            all_markdown.append(result)
+    # Create temp directory for image files
+    temp_dir = tempfile.mkdtemp()
+    temp_image_path = os.path.join(temp_dir, "temp_image.png")
 
-        except Exception as e:
-            logger.error(f"Error processing image {i}: {e}")
-            all_markdown.append("[OCR FAILED]")
+    try:
+        for i in tqdm(range(len(dataset)), desc="OCR processing"):
+            try:
+                image = dataset[i][image_column]
+
+                # Handle different image formats
+                if isinstance(image, dict) and "bytes" in image:
+                    from io import BytesIO
+                    image = Image.open(BytesIO(image["bytes"]))
+                elif isinstance(image, str):
+                    image = Image.open(image)
+                elif not isinstance(image, Image.Image):
+                    raise ValueError(f"Unsupported image type: {type(image)}")
+
+                # Process image
+                result = process_single_image(
+                    model_obj,
+                    tokenizer,
+                    image,
+                    prompt,
+                    final_base_size,
+                    final_image_size,
+                    final_crop_mode,
+                    temp_image_path,
+                )
+
+                all_markdown.append(result)
+
+            except Exception as e:
+                logger.error(f"Error processing image {i}: {e}")
+                all_markdown.append("[OCR FAILED]")
+
+    finally:
+        # Clean up temp directory
+        try:
+            shutil.rmtree(temp_dir)
+        except:
+            pass
 
     # Add markdown column to dataset
     logger.info("Adding markdown column to dataset")