refactor: enhance logging and task management in document storage and upload tasks

app/tasks/embed_metadata_into_pdf.py

@@ -7,6 +7,7 @@ import json
 from app.config import settings
 from app.tasks.retry_config import BaseTaskWithRetry
 from app.tasks.finalize_document_storage import finalize_document_storage
+from app.utils import task_logger, log_task
 
 # Import the shared Celery instance
 from app.celery_app import celery
@@ -39,6 +40,7 @@ def persist_metadata(metadata, final_pdf_path):
     return json_path
 
 @celery.task(base=BaseTaskWithRetry)
+@log_task("embed_metadata")
 def embed_metadata_into_pdf(local_file_path: str, extracted_text: str, metadata: dict):
     """
     Embeds extracted metadata into the PDF's standard metadata fields.
@@ -59,8 +61,10 @@ def embed_metadata_into_pdf(local_file_path: str, extracted_text: str, metadata:
         alt_path = os.path.join(settings.workdir, "tmp", os.path.basename(local_file_path))
         if os.path.exists(alt_path):
             local_file_path = alt_path
+            task_logger(f"Using alternative path: {local_file_path}", step_name="embed_metadata")
         else:
-            print(f"[ERROR] Local file {local_file_path} not found, cannot embed metadata.")
+            task_logger(f"Local file {local_file_path} not found, cannot embed metadata.", 
+                      level="error", step_name="embed_metadata")
             return {"error": "File not found"}
 
     # Work on a safe copy in /tmp
@@ -70,9 +74,10 @@ def embed_metadata_into_pdf(local_file_path: str, extracted_text: str, metadata:
 
     # Create a safe copy to work on
     shutil.copy(original_file, processed_file)
+    task_logger(f"Created working copy at {processed_file}", step_name="embed_metadata")
 
     try:
-        print(f"[DEBUG] Embedding metadata into {processed_file}...")
+        task_logger(f"Embedding metadata into {processed_file}", step_name="embed_metadata")
 
         # Open the PDF
         doc = fitz.open(processed_file)
@@ -87,7 +92,7 @@ def embed_metadata_into_pdf(local_file_path: str, extracted_text: str, metadata:
         doc.save(processed_file, incremental=True, encryption=fitz.PDF_ENCRYPT_KEEP)
         doc.close()
 
-        print(f"[INFO] Metadata embedded successfully in {processed_file}")
+        task_logger("Metadata embedded successfully", step_name="embed_metadata")
 
         # Use the suggested filename from metadata; if not provided, use the original basename.
         suggested_filename = metadata.get("filename", os.path.splitext(os.path.basename(local_file_path))[0])
@@ -101,28 +106,34 @@ def embed_metadata_into_pdf(local_file_path: str, extracted_text: str, metadata:
 
         # Move the processed file using shutil.move to handle cross-device moves.
         shutil.move(processed_file, final_file_path)
+        task_logger(f"Moved processed file to {final_file_path}", step_name="embed_metadata")
+        
         # Ensure the temporary file is deleted if it still exists.
         if os.path.exists(processed_file):
             os.remove(processed_file)
 
         # Persist the metadata into a JSON file with the same base name.
         json_path = persist_metadata(metadata, final_file_path)
-        print(f"[INFO] Metadata persisted to {json_path}")
+        task_logger(f"Metadata persisted to {json_path}", step_name="embed_metadata")
 
         # Trigger the next step: final storage.
-        finalize_document_storage.delay(original_file, final_file_path, metadata)
+        finalize_doc_task = finalize_document_storage.delay(original_file, final_file_path, metadata)
+        task_logger(f"Triggered final document storage with task ID: {finalize_doc_task.id}", 
+                  step_name="embed_metadata")
 
         # After triggering final storage, delete the original file if it is in workdir/tmp.
         workdir_tmp = os.path.join(settings.workdir, "tmp")
         if original_file.startswith(workdir_tmp) and os.path.exists(original_file):
             try:
                 os.remove(original_file)
-                print(f"[INFO] Deleted original file from {original_file}")
+                task_logger(f"Deleted original file from {original_file}", step_name="embed_metadata")
             except Exception as e:
-                print(f"[ERROR] Could not delete original file {original_file}: {e}")
+                task_logger(f"Could not delete original file {original_file}: {e}", 
+                          level="warning", step_name="embed_metadata")
 
         return {"file": final_file_path, "metadata_file": json_path, "status": "Metadata embedded"}
 
     except Exception as e:
-        print(f"[ERROR] Failed to embed metadata into {processed_file}: {e}")
+        task_logger(f"Failed to embed metadata into {processed_file}: {e}", 
+                  level="error", step_name="embed_metadata")
         return {"error": str(e)}

app/tasks/extract_metadata_with_gpt.py

@@ -2,9 +2,13 @@
 
 import json
 import re
+import os
 from app.config import settings
 from app.tasks.retry_config import BaseTaskWithRetry
 from app.tasks.embed_metadata_into_pdf import embed_metadata_into_pdf
+from app.utils import log_task_progress, task_step_logging
+from app.database import SessionLocal
+from app.models import FileRecord
 
 # Import the shared Celery instance
 from app.celery_app import celery
@@ -34,9 +38,13 @@ def extract_json_from_text(text):
     return None
 
 @celery.task(base=BaseTaskWithRetry)
+@task_step_logging
 def extract_metadata_with_gpt(s3_filename: str, cleaned_text: str):
     """Uses OpenAI to classify document metadata."""
-    prompt = f"""
+    session = SessionLocal()
+    try:
+        log_task_progress(session, s3_filename, "Starting metadata extraction")
+        prompt = f"""
 You are a specialized document analyzer trained to extract structured metadata from documents.
 Your task is to analyze the given text and return a well-structured JSON object.
 
@@ -68,7 +76,6 @@ Extracted text:
 Return only valid JSON with no additional commentary.
 """
 
-    try:
         print(f"[DEBUG] Sending classification request for {s3_filename}...")
         completion = client.chat.completions.create(
             model=settings.openai_model,
@@ -85,6 +92,7 @@ Return only valid JSON with no additional commentary.
         json_text = extract_json_from_text(content)
         if not json_text:
             print(f"[ERROR] Could not find valid JSON in GPT response for {s3_filename}.")
+            log_task_progress(session, s3_filename, "Failed to extract valid JSON")
             return {}
 
         metadata = json.loads(json_text)
@@ -92,9 +100,19 @@ Return only valid JSON with no additional commentary.
 
         # Trigger the next step: embedding metadata into the PDF
         embed_metadata_into_pdf.delay(s3_filename, cleaned_text, metadata)
+        log_task_progress(session, s3_filename, "Metadata extraction completed")
+
+        # Update database record
+        file_record = session.query(FileRecord).filter(FileRecord.s3_filename == s3_filename).first()
+        if file_record:
+            file_record.metadata = metadata
+            session.commit()
 
         return {"s3_file": s3_filename, "metadata": metadata}
 
     except Exception as e:
         print(f"[ERROR] OpenAI classification failed for {s3_filename}: {e}")
+        log_task_progress(session, s3_filename, f"Error: {e}")
         return {}
+    finally:
+        session.close()

app/tasks/finalize_document_storage.py

@@ -2,25 +2,29 @@
 
 from app.config import settings
 from app.tasks.retry_config import BaseTaskWithRetry
-# Import the shared Celery instance
 from app.celery_app import celery
+from app.utils import task_logger, log_task
 
-# 1) Import the aggregator task
+# Import the aggregator task
 from app.tasks.send_to_all import send_to_all_destinations
 
-
 @celery.task(base=BaseTaskWithRetry)
+@log_task("finalize_storage")
 def finalize_document_storage(original_file: str, processed_file: str, metadata: dict):
     """
     Final storage step after embedding metadata.
     We will now call 'send_to_all_destinations' to push the final PDF to Dropbox/Nextcloud/Paperless.
     """
-    print(f"[INFO] Finalizing document storage for {processed_file}")
+    task_logger(f"Finalizing document storage for {processed_file}", step_name="finalize_storage")
 
-    # 2) Enqueue uploads to all destinations (Dropbox, Nextcloud, Paperless)
-    send_to_all_destinations.delay(processed_file)
+    # Enqueue uploads to all destinations (Dropbox, Nextcloud, Paperless)
+    send_task = send_to_all_destinations.delay(processed_file)
+    
+    task_logger(f"Triggered send to all destinations with task ID: {send_task.id}", 
+               step_name="finalize_storage", status="success")
 
     return {
         "status": "Completed",
-        "file": processed_file
+        "file": processed_file,
+        "send_task_id": send_task.id
     }

app/tasks/process_document.py

@@ -13,7 +13,7 @@ from app.tasks.extract_metadata_with_gpt import extract_metadata_with_gpt
 from app.celery_app import celery
 from app.database import SessionLocal
 from app.models import FileRecord
-from app.utils import hash_file
+from app.utils import hash_file, log_task_progress, task_step_logging
 
 
 @celery.task(base=BaseTaskWithRetry)
@@ -28,77 +28,96 @@ def process_document(original_local_file: str):
          - Check for embedded text. If present, run local GPT extraction
          - Otherwise, queue Textract-based OCR
     """
+    task_id = process_document.request.id
+    log_task_progress(task_id, "process_document", "pending", f"Processing {original_local_file}", file_path=original_local_file)
 
     if not os.path.exists(original_local_file):
-        print(f"[ERROR] File {original_local_file} not found.")
+        log_task_progress(task_id, "process_document", "failure", f"File {original_local_file} not found.", file_path=original_local_file)
         return {"error": "File not found"}
 
     # 0. Compute the file hash and check for duplicates
-    filehash = hash_file(original_local_file)
-    original_filename = os.path.basename(original_local_file)
-    file_size = os.path.getsize(original_local_file)
-    mime_type, _ = mimetypes.guess_type(original_local_file)
-    if not mime_type:
-        mime_type = "application/octet-stream"
+    with task_step_logging(task_id, "compute_hash", file_path=original_local_file):
+        filehash = hash_file(original_local_file)
+        original_filename = os.path.basename(original_local_file)
+        file_size = os.path.getsize(original_local_file)
+        mime_type, _ = mimetypes.guess_type(original_local_file)
+        if not mime_type:
+            mime_type = "application/octet-stream"
 
     # Acquire DB session in the task
+    new_record = None
     with SessionLocal() as db:
-        existing = db.query(FileRecord).filter_by(filehash=filehash).one_or_none()
-        if existing:
-            print(f"[INFO] Duplicate file detected (hash={filehash[:10]}...) Skipping processing.")
-            return {
-                "status": "duplicate_file",
-                "file_id": existing.id,
-                "detail": "File already processed."
-            }
+        with task_step_logging(task_id, "check_duplicates", file_path=original_local_file):
+            existing = db.query(FileRecord).filter_by(filehash=filehash).one_or_none()
+            if existing:
+                log_task_progress(task_id, "process_document", "success", 
+                                f"Duplicate file detected (hash={filehash[:10]}...). Skipping processing.",
+                                file_id=existing.id)
+                return {
+                    "status": "duplicate_file",
+                    "file_id": existing.id,
+                    "detail": "File already processed."
+                }
 
         # Not a duplicate -> insert a new record
-        new_record = FileRecord(
-            filehash=filehash,
-            original_filename=original_filename,
-            local_filename="",  # Will fill in after we move it
-            file_size=file_size,
-            mime_type=mime_type,
-        )
-        db.add(new_record)
-        db.commit()
-        db.refresh(new_record)
+        with task_step_logging(task_id, "create_file_record", file_path=original_local_file):
+            new_record = FileRecord(
+                filehash=filehash,
+                original_filename=original_filename,
+                local_filename="",  # Will fill in after we move it
+                file_size=file_size,
+                mime_type=mime_type,
+            )
+            db.add(new_record)
+            db.commit()
+            db.refresh(new_record)
 
         # 1. Generate a UUID-based filename and place it in /workdir/tmp
-        file_ext = os.path.splitext(original_local_file)[1]
-        file_uuid = str(uuid.uuid4())
-        new_filename = f"{file_uuid}{file_ext}"
+        with task_step_logging(task_id, "copy_to_workdir", file_id=new_record.id, file_path=original_local_file):
+            file_ext = os.path.splitext(original_local_file)[1]
+            file_uuid = str(uuid.uuid4())
+            new_filename = f"{file_uuid}{file_ext}"
 
-        tmp_dir = os.path.join(settings.workdir, "tmp")
-        os.makedirs(tmp_dir, exist_ok=True)
-        new_local_path = os.path.join(tmp_dir, new_filename)
+            tmp_dir = os.path.join(settings.workdir, "tmp")
+            os.makedirs(tmp_dir, exist_ok=True)
+            new_local_path = os.path.join(tmp_dir, new_filename)
 
-        # Copy the file instead of moving it
-        shutil.copy(original_local_file, new_local_path)
+            # Copy the file instead of moving it
+            shutil.copy(original_local_file, new_local_path)
 
-        # Update the DB with final local filename
-        new_record.local_filename = new_local_path
-        db.commit()
+            # Update the DB with final local filename
+            new_record.local_filename = new_local_path
+            db.commit()
 
     # 2. Check for embedded text (outside the DB session to avoid long open transactions)
-    pdf_doc = fitz.open(new_local_path)
-    has_text = any(page.get_text() for page in pdf_doc)
-    pdf_doc.close()
+    with task_step_logging(task_id, "check_embedded_text", file_id=new_record.id, file_path=new_local_path):
+        pdf_doc = fitz.open(new_local_path)
+        has_text = any(page.get_text() for page in pdf_doc)
+        pdf_doc.close()
 
     if has_text:
-        print(f"[INFO] PDF {original_local_file} contains embedded text. Processing locally.")
+        log_task_progress(task_id, "process_document", "in_progress", 
+                        f"PDF {original_local_file} contains embedded text. Processing locally.", 
+                        file_id=new_record.id)
 
         # Extract text locally
         extracted_text = ""
-        pdf_doc = fitz.open(new_local_path)
-        for page in pdf_doc:
-            extracted_text += page.get_text("text") + "\n"
-        pdf_doc.close()
+        with task_step_logging(task_id, "extract_text_locally", file_id=new_record.id, file_path=new_local_path):
+            pdf_doc = fitz.open(new_local_path)
+            for page in pdf_doc:
+                extracted_text += page.get_text("text") + "\n"
+            pdf_doc.close()
 
         # Call metadata extraction directly
+        log_task_progress(task_id, "process_document", "success", 
+                        "Text extracted locally. Queuing for metadata extraction.", 
+                        file_id=new_record.id)
         extract_metadata_with_gpt.delay(new_filename, extracted_text)
-        return {"file": new_local_path, "status": "Text extracted locally"}
+        return {"file": new_local_path, "status": "Text extracted locally", "file_id": new_record.id}
 
     # 3. If no embedded text, queue Textract processing
+    log_task_progress(task_id, "process_document", "success", 
+                    "No embedded text found. Queuing for OCR.", 
+                    file_id=new_record.id)
     process_with_textract.delay(new_filename)
-    return {"file": new_local_path, "status": "Queued for OCR"}
+    return {"file": new_local_path, "status": "Queued for OCR", "file_id": new_record.id}

app/tasks/process_with_textract.py

@@ -8,6 +8,9 @@ from app.config import settings
 from app.tasks.retry_config import BaseTaskWithRetry
 from app.tasks.extract_metadata_with_gpt import extract_metadata_with_gpt
 from app.celery_app import celery
+from app.utils import log_task_progress, task_step_logging
+from app.database import SessionLocal
+from app.models import FileRecord
 
 logger = logging.getLogger(__name__)
 
@@ -30,38 +33,58 @@ def process_with_textract(s3_filename: str):
       4. Extracts the text content for metadata processing.
       5. Triggers downstream metadata extraction by calling extract_metadata_with_gpt.
     """
+    task_id = process_with_textract.request.id
+    tmp_file_path = os.path.join(settings.workdir, "tmp", s3_filename)
+    
+    # Get the file_id from the database
+    file_id = None
+    with SessionLocal() as db:
+        file_record = db.query(FileRecord).filter(
+            FileRecord.local_filename == tmp_file_path
+        ).first()
+        if file_record:
+            file_id = file_record.id
+    
+    log_task_progress(task_id, "process_with_textract", "pending", 
+                     f"Starting OCR for {s3_filename}", file_id, tmp_file_path)
+    
+    if not os.path.exists(tmp_file_path):
+        log_task_progress(task_id, "process_with_textract", "failure", 
+                         f"Local file not found: {tmp_file_path}", file_id, tmp_file_path)
+        raise FileNotFoundError(f"Local file not found: {tmp_file_path}")
+
     try:
-        tmp_file_path = os.path.join(settings.workdir, "tmp", s3_filename)
-        if not os.path.exists(tmp_file_path):
-            raise FileNotFoundError(f"Local file not found: {tmp_file_path}")
+        with task_step_logging(task_id, "azure_document_intelligence", file_id, tmp_file_path):
+            # Open and send the document for processing
+            with open(tmp_file_path, "rb") as f:
+                poller = document_intelligence_client.begin_analyze_document(
+                    "prebuilt-read", body=f, output=[AnalyzeOutputOption.PDF]
+                )
+            result: AnalyzeResult = poller.result()
+            operation_id = poller.details["operation_id"]
 
-        logger.info(f"Processing {s3_filename} with Azure Document Intelligence OCR.")
-
-        # Open and send the document for processing
-        with open(tmp_file_path, "rb") as f:
-            poller = document_intelligence_client.begin_analyze_document(
-                "prebuilt-read", body=f, output=[AnalyzeOutputOption.PDF]
+        with task_step_logging(task_id, "retrieve_and_save_searchable_pdf", file_id, tmp_file_path):
+            # Retrieve the processed searchable PDF
+            response = document_intelligence_client.get_analyze_result_pdf(
+                model_id=result.model_id, result_id=operation_id
             )
-        result: AnalyzeResult = poller.result()
-        operation_id = poller.details["operation_id"]
-
-        # Retrieve the processed searchable PDF
-        response = document_intelligence_client.get_analyze_result_pdf(
-            model_id=result.model_id, result_id=operation_id
-        )
-        searchable_pdf_path = tmp_file_path  # Overwrite the original PDF location
-        with open(searchable_pdf_path, "wb") as writer:
-            writer.writelines(response)
-        logger.info(f"Searchable PDF saved at: {searchable_pdf_path}")
-
-        # Extract raw text content from the result
-        extracted_text = result.content if result.content else ""
-        logger.info(f"Extracted text for {s3_filename}: {len(extracted_text)} characters")
+            searchable_pdf_path = tmp_file_path  # Overwrite the original PDF location
+            with open(searchable_pdf_path, "wb") as writer:
+                writer.writelines(response)
+            
+            # Extract raw text content from the result
+            extracted_text = result.content if result.content else ""
+            log_task_progress(task_id, "process_with_textract", "in_progress", 
+                             f"Extracted {len(extracted_text)} characters of text", file_id, tmp_file_path)
 
         # Trigger downstream metadata extraction
+        log_task_progress(task_id, "process_with_textract", "success", 
+                         "OCR completed. Queueing metadata extraction.", file_id, tmp_file_path)
         extract_metadata_with_gpt.delay(s3_filename, extracted_text)
 
         return {"s3_file": s3_filename, "searchable_pdf": searchable_pdf_path, "cleaned_text": extracted_text}
     except Exception as e:
+        log_task_progress(task_id, "process_with_textract", "failure", 
+                         f"Error processing with Azure Document Intelligence: {e}", file_id, tmp_file_path)
         logger.error(f"Error processing {s3_filename} with Azure Document Intelligence: {e}")
         raise

app/tasks/send_to_all.py

@@ -4,18 +4,31 @@ from app.celery_app import celery
 from app.tasks.upload_to_dropbox import upload_to_dropbox
 from app.tasks.upload_to_nextcloud import upload_to_nextcloud
 from app.tasks.upload_to_paperless import upload_to_paperless
+from app.utils import task_logger, log_task
 
 @celery.task
+@log_task("send_to_all_destinations")
 def send_to_all_destinations(file_path: str):
     """
     Fires off tasks to upload a single file to Dropbox, Nextcloud, and Paperless.
     These tasks run in parallel (Celery returns immediately from each .delay()).
     """
-    upload_to_dropbox.delay(file_path)
-    upload_to_nextcloud.delay(file_path)
-    upload_to_paperless.delay(file_path)
+    task_logger(f"Sending {file_path} to all destinations", step_name="send_to_all")
+    
+    dropbox_task = upload_to_dropbox.delay(file_path)
+    nextcloud_task = upload_to_nextcloud.delay(file_path)
+    paperless_task = upload_to_paperless.delay(file_path)
+
+    task_logger(f"Enqueued file for all destinations: Dropbox (task: {dropbox_task.id}), "
+               f"Nextcloud (task: {nextcloud_task.id}), Paperless (task: {paperless_task.id})",
+               step_name="send_to_all", status="success")
 
     return {
         "status": "All upload tasks enqueued",
-        "file_path": file_path
+        "file_path": file_path,
+        "task_ids": {
+            "dropbox": dropbox_task.id,
+            "nextcloud": nextcloud_task.id, 
+            "paperless": paperless_task.id
+        }
     }

app/tasks/upload_to_dropbox.py

@@ -6,6 +6,7 @@ import dropbox
 from app.config import settings
 from app.tasks.retry_config import BaseTaskWithRetry
 from app.celery_app import celery
+from app.utils import task_logger, log_task
 
 def get_dropbox_access_token():
     """Refresh the Dropbox access token using the stored refresh token from ENV."""
@@ -25,14 +26,16 @@ def get_dropbox_access_token():
         return response.json()["access_token"]
     else:
         error_msg = f"Failed to refresh Dropbox token: {response.status_code} - {response.text}"
-        print(f"[ERROR] {error_msg}")
+        task_logger(error_msg, level="error", step_name="dropbox_auth")
         raise Exception(error_msg)
 
 @celery.task(base=BaseTaskWithRetry)
+@log_task("upload_to_dropbox")
 def upload_to_dropbox(file_path: str):
     """Uploads a file to Dropbox using the API."""
 
     if not os.path.exists(file_path):
+        task_logger(f"File not found: {file_path}", level="error", step_name="dropbox_upload")
         raise FileNotFoundError(f"File not found: {file_path}")
 
     # Extract filename and set target path
@@ -41,16 +44,20 @@ def upload_to_dropbox(file_path: str):
 
     try:
         # Get fresh access token
+        task_logger(f"Getting Dropbox access token", step_name="dropbox_auth")
         access_token = get_dropbox_access_token()
         dbx = dropbox.Dropbox(access_token)
 
         file_size = os.path.getsize(file_path)
         chunk_size = 4 * 1024 * 1024  # 4MB chunk size
 
+        task_logger(f"Starting upload of {filename} ({file_size} bytes) to Dropbox", step_name="dropbox_upload")
+        
         with open(file_path, "rb") as file_data:
             if file_size <= chunk_size:
                 dbx.files_upload(file_data.read(), dropbox_path)
             else:
+                task_logger(f"Using chunked upload for {filename}", step_name="dropbox_upload")
                 upload_session_start_result = dbx.files_upload_session_start(file_data.read(chunk_size))
                 cursor = dropbox.files.UploadSessionCursor(
                     session_id=upload_session_start_result.session_id,
@@ -65,10 +72,10 @@ def upload_to_dropbox(file_path: str):
                         dbx.files_upload_session_append_v2(file_data.read(chunk_size), cursor)
                         cursor.offset = file_data.tell()
 
-        print(f"[INFO] Successfully uploaded {filename} to Dropbox at {dropbox_path}.")
+        task_logger(f"Successfully uploaded {filename} to Dropbox at {dropbox_path}", step_name="dropbox_upload", status="success")
         return {"status": "Completed", "file": file_path}
 
     except Exception as e:
-        error_msg = f"[ERROR] Failed to upload {filename} to Dropbox: {str(e)}"
-        print(error_msg)
+        error_msg = f"Failed to upload {filename} to Dropbox: {str(e)}"
+        task_logger(error_msg, level="error", step_name="dropbox_upload", status="failure")
         raise Exception(error_msg)

app/tasks/upload_to_nextcloud.py

@@ -5,12 +5,15 @@ import requests
 from app.config import settings
 from app.tasks.retry_config import BaseTaskWithRetry
 from app.celery_app import celery
+from app.utils import task_logger, log_task
 
 @celery.task(base=BaseTaskWithRetry)
+@log_task("upload_to_nextcloud")
 def upload_to_nextcloud(file_path: str):
     """Uploads a file to Nextcloud in the configured folder."""
 
     if not os.path.exists(file_path):
+        task_logger(f"File not found: {file_path}", level="error", step_name="nextcloud_upload")
         raise FileNotFoundError(f"File not found: {file_path}")
 
     # Extract filename
@@ -18,6 +21,8 @@ def upload_to_nextcloud(file_path: str):
 
     # Construct the full upload URL
     nextcloud_url = f"{settings.nextcloud_upload_url}/{settings.nextcloud_folder}/{filename}"
+    
+    task_logger(f"Starting upload of {filename} to Nextcloud", step_name="nextcloud_upload")
 
     # Read file content
     with open(file_path, "rb") as file_data:
@@ -29,9 +34,10 @@ def upload_to_nextcloud(file_path: str):
 
     # Check if upload was successful
     if response.status_code in (200, 201):
-        print(f"[INFO] Successfully uploaded {filename} to Nextcloud at {nextcloud_url}.")
+        task_logger(f"Successfully uploaded {filename} to Nextcloud at {nextcloud_url}", 
+                   step_name="nextcloud_upload", status="success")
         return {"status": "Completed", "file": file_path}
     else:
-        error_msg = f"[ERROR] Failed to upload {filename} to Nextcloud: {response.status_code} - {response.text}"
-        print(error_msg)
+        error_msg = f"Failed to upload {filename} to Nextcloud: {response.status_code} - {response.text}"
+        task_logger(error_msg, level="error", step_name="nextcloud_upload", status="failure")
         raise Exception(error_msg)

app/tasks/upload_to_paperless.py

@@ -10,6 +10,7 @@ from typing import Dict, Any
 from app.config import settings
 from app.tasks.retry_config import BaseTaskWithRetry
 from app.celery_app import celery
+from app.utils import task_logger, log_task
 
 logger = logging.getLogger(__name__)
 
@@ -46,13 +47,15 @@ def poll_task_for_document_id(task_id: str) -> int:
 
     while attempts < POLL_MAX_ATTEMPTS:
         try:
+            task_logger(f"Polling Paperless for task {task_id}, attempt {attempts+1}/{POLL_MAX_ATTEMPTS}", 
+                      step_name="paperless_poll")
             resp = requests.get(url, headers=_get_headers(), params={"task_id": task_id})
             resp.raise_for_status()
             tasks_data = resp.json()
         except requests.exceptions.RequestException as exc:
-            logger.warning(
-                "Failed to poll for task_id='%s'. Attempt=%d Error=%s",
-                task_id, attempts + 1, exc
+            task_logger(
+                f"Failed to poll for task_id='{task_id}'. Attempt={attempts + 1}/{POLL_MAX_ATTEMPTS} Error={exc}",
+                level="warning", step_name="paperless_poll"
             )
             time.sleep(POLL_INTERVAL_SEC)
             attempts += 1
@@ -67,21 +70,29 @@ def poll_task_for_document_id(task_id: str) -> int:
             if status == "SUCCESS":
                 doc_str = task_info.get("related_document")
                 if doc_str:
+                    task_logger(f"Task {task_id} completed successfully with document ID: {doc_str}", 
+                              step_name="paperless_poll", status="success")
                     return int(doc_str)
                 raise RuntimeError(
                     f"Task {task_id} completed but no doc ID found. Task info: {task_info}"
                 )
             elif status == "FAILURE":
-                raise RuntimeError(f"Task {task_id} failed: {task_info.get('result')}")
+                error_msg = f"Task {task_id} failed: {task_info.get('result')}"
+                task_logger(error_msg, level="error", step_name="paperless_poll", status="failure")
+                raise RuntimeError(error_msg)
+            else:
+                task_logger(f"Task {task_id} status: {status}, waiting {POLL_INTERVAL_SEC}s", 
+                          step_name="paperless_poll")
 
         attempts += 1
         time.sleep(POLL_INTERVAL_SEC)
 
-    raise TimeoutError(
-        f"Task {task_id} didn't reach SUCCESS within {POLL_MAX_ATTEMPTS} attempts."
-    )
+    timeout_msg = f"Task {task_id} didn't reach SUCCESS within {POLL_MAX_ATTEMPTS} attempts."
+    task_logger(timeout_msg, level="error", step_name="paperless_poll", status="failure")
+    raise TimeoutError(timeout_msg)
 
 @celery.task(base=BaseTaskWithRetry)
+@log_task("upload_to_paperless")
 def upload_to_paperless(file_path: str) -> Dict[str, Any]:
     """
     Uploads a PDF to Paperless with minimal metadata (filename and date only).
@@ -93,9 +104,11 @@ def upload_to_paperless(file_path: str) -> Dict[str, Any]:
     Returns a dict with status, the paperless_task_id, paperless_document_id, and file_path.
     """
     if not os.path.exists(file_path):
+        task_logger(f"File not found: {file_path}", level="error", step_name="paperless_upload")
         raise FileNotFoundError(f"File not found: {file_path}")
 
     base_name = os.path.basename(file_path)
+    task_logger(f"Starting upload of {base_name} to Paperless", step_name="paperless_upload")
 
     # Upload the PDF
     post_url = _paperless_api_url("/api/documents/post_document/")
@@ -106,22 +119,21 @@ def upload_to_paperless(file_path: str) -> Dict[str, Any]:
         data = {"title": base_name}  # Title = Filename (no additional metadata)
 
         try:
-            logger.debug("Posting document to Paperless: file=%s", base_name)
+            task_logger(f"Posting document to Paperless: file={base_name}", step_name="paperless_upload")
             resp = requests.post(post_url, headers=_get_headers(), files=files, data=data)
             resp.raise_for_status()
         except requests.exceptions.RequestException as exc:
-            logger.error(
-                "Failed to upload document '%s' to Paperless. Error: %s. Response=%s",
-                file_path, exc, getattr(exc.response, "text", "<no response>")
-            )
+            error_msg = f"Failed to upload document '{file_path}' to Paperless. Error: {exc}. Response={getattr(exc.response, 'text', '<no response>')}"
+            task_logger(error_msg, level="error", step_name="paperless_upload", status="failure")
             raise
 
         raw_task_id = resp.text.strip().strip('"').strip("'")
-        logger.info(f"Received Paperless task ID: {raw_task_id}")
+        task_logger(f"Received Paperless task ID: {raw_task_id}", step_name="paperless_upload")
 
     # Poll tasks until success/fail => get doc_id
     doc_id = poll_task_for_document_id(raw_task_id)
-    logger.info(f"Document {file_path} successfully ingested => ID={doc_id}")
+    task_logger(f"Document {file_path} successfully ingested => ID={doc_id}", 
+              step_name="paperless_upload", status="success")
 
     return {
         "status": "Completed",