Debugging for inference

backend/runner/inference.py

@@ -721,15 +721,15 @@ def run_inference_streaming(
                     print(f"🔍 Batch {batch_count} completed in {batch_time:.2f}s")
                     print(f"🔍 Progress: {total_processed:,}/{total_items:,} ({progress_pct:.1f}%)")
                     print(f"🔍 Elapsed time: {elapsed_time:.1f}s")
-                    print(f"🔍 Current top score: {results[0]['score']:.4f} if results else 'N/A'")
+                    print(f"🔍 Current top score: {results[0]['score']:.4f}" if results else "🔍 Current top score: N/A")
                     print(f"🔍 Estimated time remaining: {((elapsed_time / total_processed) * (total_items - total_processed)):.1f}s")
                 else:
                     print(f"🔍 Batch {batch_count} completed in {batch_time:.2f}s")
                     print(f"🔍 Total processed: {total_processed:,}")
                     print(f"🔍 Elapsed time: {elapsed_time:.1f}s")
-                    print(f"🔍 Current top score: {results[0]['score']:.4f} if results else 'N/A'")
+                    print(f"🔍 Current top score: {results[0]['score']:.4f}" if results else "🔍 Current top score: N/A")
                 
-                print(f"🔍 Current top result: {results[0]['english_original'][:100]}..." if results else "No results yet")
+                print(f"🔍 Current top result: {results[0]['english_original'][:100]}..." if results else "🔍 No results yet")
                 print("─" * 80)
         
         # Process remaining items
@@ -765,6 +765,10 @@ def process_embedding_batch_streaming(
 ) -> List[Dict[str, Any]]:
     """Process a batch of streaming embeddings"""
     results = []
+    processed_count = 0
+    error_count = 0
+    
+    print(f"🔍 Processing batch of {len(batch)} items...")
     
     for item in batch:
         try:
@@ -776,6 +780,7 @@ def process_embedding_batch_streaming(
             elif model_type == "PaintingCLIP" and 'paintingclip_embedding' in item:
                 embedding = torch.tensor(item['paintingclip_embedding'])
             else:
+                print(f"⚠️  No embedding found for {model_type} in item: {list(item.keys())}")
                 continue
             
             # Calculate similarity
@@ -798,9 +803,12 @@ def process_embedding_batch_streaming(
                 "work": work_id,
                 "rank": len(results) + 1,
             })
+            processed_count += 1
             
         except Exception as e:
+            error_count += 1
             print(f"⚠️  Error processing item in streaming batch: {e}")
             continue
     
+    print(f"🔍 Batch processing complete: {processed_count} successful, {error_count} errors")
     return results