style: fix linting issues in retry and metadata extraction code

Co-authored-by: christianlouis <361235+christianlouis@users.noreply.github.com>

app/api/files.py

@@ -525,7 +525,7 @@ def _retry_pipeline_step(file_record: FileRecord, step_name: str, db: Session) -
         # Retrying embed requires re-running metadata extraction first, because
         # embed_metadata_into_pdf needs the actual metadata dict (not empty).
         # Re-trigger extract_metadata_with_gpt which will chain into embed_metadata_into_pdf.
-        
+
         # Check for file in multiple locations:
         # 1. Original location in tmp (file_record.local_filename)
         # 2. Processed location (file_record.processed_file_path)
@@ -536,21 +536,20 @@ def _retry_pipeline_step(file_record: FileRecord, step_name: str, db: Session) -
         elif file_record.processed_file_path and os.path.exists(file_record.processed_file_path):
             # File has been processed and moved to processed directory
             file_path = file_record.processed_file_path
-        else:
-            # Try fallback path in workdir/tmp
-            if file_record.local_filename:
-                workdir = settings.workdir
-                tmp_dir = os.path.join(workdir, "tmp")
-                fallback_path = os.path.join(tmp_dir, os.path.basename(file_record.local_filename))
-                if os.path.exists(fallback_path):
-                    file_path = fallback_path
-        
+        # Try fallback path in workdir/tmp
+        elif file_record.local_filename:
+            workdir = settings.workdir
+            tmp_dir = os.path.join(workdir, "tmp")
+            fallback_path = os.path.join(tmp_dir, os.path.basename(file_record.local_filename))
+            if os.path.exists(fallback_path):
+                file_path = fallback_path
+
         if not file_path:
             raise HTTPException(
-                status_code=400, 
+                status_code=400,
                 detail="File not found in tmp or processed directory. Cannot retry metadata embedding."
             )
-        
+
         extracted_text = _extract_text_from_pdf(file_path)
         # Pass the full path to the task so it can locate the file
         task = extract_metadata_task.delay(file_path, extracted_text, file_id)

app/tasks/extract_metadata_with_gpt.py

@@ -49,7 +49,7 @@ def extract_json_from_text(text):
 def extract_metadata_with_gpt(self, filename: str, cleaned_text: str, file_id: int = None):
     """
     Uses OpenAI to classify document metadata.
-    
+
     Args:
         filename: Can be either a basename (e.g., "file.pdf") or a full path (e.g., "/workdir/processed/file.pdf")
         cleaned_text: The extracted text from the document