From 3fe4ed0b4a3ddae44da19445411a67dc3a166cb7 Mon Sep 17 00:00:00 2001
From: SamoilenkoVadym <samoylenko.vadym@gmail.comm>
Date: Mon, 9 Feb 2026 22:07:34 +0000
Subject: [PATCH] Fix AI upload: process inline instead of background

Background AI processing returned before results were ready,
leaving the UI showing "AI processing..." forever. Now AI
metadata generation runs inline (awaited) so results are
returned immediately in the upload response.

Background SSE-based processing can be re-enabled later for
large batch uploads (10+ files).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 app/routers/upload.py | 52 ++++++++++---------------------------------
 1 file changed, 12 insertions(+), 40 deletions(-)

diff --git a/app/routers/upload.py b/app/routers/upload.py
index 1b40180..7f13239 100644
--- a/app/routers/upload.py
+++ b/app/routers/upload.py
@@ -87,45 +87,23 @@ async def upload_files(
     )
 
     results = []
-    ai_pending = []  # Files needing background AI processing
 
     for upload_file in files:
         try:
             filepath = await file_svc.save_upload(upload_file, user_id)
             filename = Path(filepath).name
 
-            if metadata_source == "ai":
-                # For AI source: save files first, process AI in background
-                file_type = metadata_service.detect_file(filepath)
-                old_metadata = metadata_service.extract_metadata(filepath, file_type)
-                file_result = {
-                    "success": True,
-                    "filename": filename,
-                    "file_type": file_type.value,
-                    "current_metadata": old_metadata,
-                    "suggested_metadata": {"title": "", "subject": "AI processing...", "keywords": ""},
-                    "filepath": filepath,
-                    "metadata_source": "ai",
-                    "ai_status": "pending",
-                }
-                store.add_file_to_session(session_id, file_result)
-                ai_pending.append({
-                    "file_index": len(results),
-                    "filepath": filepath,
-                    "filename": filename,
-                    "file_type": file_type,
-                })
-                results.append(file_result)
-            else:
-                file_result = await metadata_service.process_uploaded_file(
-                    filepath=filepath,
-                    filename=filename,
-                    metadata_source=metadata_source,
-                    lookup=lookup,
-                    import_map=import_map,
-                )
-                store.add_file_to_session(session_id, file_result)
-                results.append(file_result)
+            # Process file through the standard pipeline (including AI inline)
+            file_result = await metadata_service.process_uploaded_file(
+                filepath=filepath,
+                filename=filename,
+                metadata_source=metadata_source,
+                lookup=lookup,
+                import_map=import_map,
+            )
+
+            store.add_file_to_session(session_id, file_result)
+            results.append(file_result)
 
         except ValueError as e:
             results.append({"filename": upload_file.filename, "error": str(e)})
@@ -133,16 +111,10 @@ async def upload_files(
             logger.error(f"Upload error for {upload_file.filename}: {e}")
             results.append({"filename": upload_file.filename, "error": str(e)})
 
-    # Start background AI processing if needed
-    if ai_pending:
-        import asyncio
-        from ..services.ai_service import process_bulk_ai
-        asyncio.create_task(process_bulk_ai(session_id, ai_pending, store, user_id))
-
     # Strip server paths from client response
     safe_results = [{k: v for k, v in r.items() if k != "filepath"} for r in results]
 
-    return {"success": True, "session_id": session_id, "files": safe_results, "ai_processing": bool(ai_pending)}
+    return {"success": True, "session_id": session_id, "files": safe_results}
 
 
 @router.post("/upload-excel")