print statement replaced with logger

surfsense_backend/app/connectors/airtable_connector.py

@@ -305,19 +305,18 @@ class AirtableConnector:
                 )
 
             # Create Airtable formula for date filtering
-            # filter_formula = (
-            #    f"AND("
-            #    f"IS_AFTER({{date_field}}, '{start_date}'), "
-            #    f"IS_BEFORE({{date_field}}, '{end_date}')"
-            #    f")"
-            # ).replace("{date_field}", date_field)
-            # TODO: Investigate how to properly use filter formula
+            filter_formula = (
+                f"AND("
+                f"IS_AFTER({{{date_field}}}, '{start_date}'), "
+                f"IS_BEFORE({{{date_field}}}, '{end_date}')"
+                f")"
+            )
 
             return self.get_all_records(
                 base_id=base_id,
                 table_id=table_id,
                 max_records=max_records,
-                # filter_by_formula=filter_formula,
+                filter_by_formula=filter_formula,
             )
 
         except Exception as e:

surfsense_backend/app/routes/chats_routes.py

@@ -54,8 +54,6 @@ async def handle_chat_data(
         request_data.get("document_ids_to_add_in_context")
     )
     search_mode_str = validate_search_mode(request_data.get("search_mode"))
-    # print("RESQUEST DATA:", request_data)
-    # print("SELECTED CONNECTORS:", selected_connectors)
 
     # Check if the search space belongs to the current user
     try:
@@ -76,7 +74,6 @@ async def handle_chat_data(
             )
         )
         user_preference = language_result.scalars().first()
-        # print("UserSearchSpacePreference:", user_preference)
 
         language = None
         if (

surfsense_backend/app/routes/documents_routes.py

@@ -1,5 +1,6 @@
 # Force asyncio to use standard event loop before unstructured imports
 import asyncio
+import logging
 
 from fastapi import APIRouter, BackgroundTasks, Depends, Form, HTTPException, UploadFile
 from litellm import atranscription
@@ -40,7 +41,7 @@ from app.utils.check_ownership import check_ownership
 try:
     asyncio.set_event_loop_policy(asyncio.DefaultEventLoopPolicy())
 except RuntimeError as e:
-    print("Error setting event loop policy", e)
+    logging.warning(f"Error setting event loop policy: {e}")
     pass
 
 import os
@@ -48,6 +49,7 @@ import os
 os.environ["UNSTRUCTURED_HAS_PATCHED_LOOP"] = "1"
 
 
+logger = logging.getLogger(__name__)
 router = APIRouter()
 
 
@@ -740,7 +742,7 @@ async def process_file_in_background(
             try:
                 os.unlink(file_path)
             except Exception as e:
-                print("Error deleting temp file", e)
+                logger.warning(f"Error deleting temp file {file_path}: {e}")
                 pass
 
             await task_logger.log_task_progress(
@@ -865,7 +867,7 @@ async def process_file_in_background(
             try:
                 os.unlink(file_path)
             except Exception as e:
-                print("Error deleting temp file", e)
+                logger.warning(f"Error deleting temp file {file_path}: {e}")
                 pass
 
             # Process transcription as markdown document
@@ -931,7 +933,7 @@ async def process_file_in_background(
                 try:
                     os.unlink(file_path)
                 except Exception as e:
-                    print("Error deleting temp file", e)
+                    logger.warning(f"Error deleting temp file {file_path}: {e}")
                     pass
 
                 # Pass the documents to the existing background task
@@ -993,7 +995,7 @@ async def process_file_in_background(
                 try:
                     os.unlink(file_path)
                 except Exception as e:
-                    print("Error deleting temp file", e)
+                    logger.warning(f"Error deleting temp file {file_path}: {e}")
                     pass
 
                 # Get markdown documents from the result
@@ -1071,7 +1073,7 @@ async def process_file_in_background(
                 try:
                     os.unlink(file_path)
                 except Exception as e:
-                    print("Error deleting temp file", e)
+                    logger.warning(f"Error deleting temp file {file_path}: {e}")
                     pass
 
                 await task_logger.log_task_progress(
@@ -1121,7 +1123,5 @@ async def process_file_in_background(
             str(e),
             {"error_type": type(e).__name__, "filename": filename},
         )
-        import logging
-
-        logging.error(f"Error processing file in background: {e!s}")
+        logger.error(f"Error processing file in background: {e!s}")
         raise  # Re-raise so the wrapper can also handle it

surfsense_backend/app/tasks/podcast_tasks.py

@@ -11,8 +11,57 @@ from app.services.task_logging_service import TaskLoggingService
 async def generate_document_podcast(
     session: AsyncSession, document_id: int, search_space_id: int, user_id: int
 ):
-    # TODO: Need to fetch the document chunks, then concatenate them and pass them to the podcast generation model
-    pass
+    """
+    Generate a podcast from a document by fetching its chunks and concatenating them.
+    
+    Args:
+        session: Database session
+        document_id: ID of the document to convert to podcast
+        search_space_id: Search space ID
+        user_id: User ID
+        
+    Returns:
+        Podcast object or raises exception
+    """
+    task_logger = TaskLoggingService(session, search_space_id)
+    
+    # Log task start
+    log_entry = await task_logger.log_task_start(
+        task_name="generate_document_podcast",
+        source="podcast_task",
+        message=f"Starting podcast generation for document {document_id}",
+        metadata={
+            "document_id": document_id,
+            "search_space_id": search_space_id,
+            "user_id": str(user_id),
+        },
+    )
+    
+    try:
+        # TODO: Implement document chunk fetching and podcast generation
+        # This would involve:
+        # 1. Fetching the document and its chunks
+        # 2. Concatenating the chunks into readable content
+        # 3. Passing the content to the podcast generation model
+        # 4. Creating and saving the podcast
+        
+        await task_logger.log_task_failure(
+            log_entry,
+            "Document podcast generation not yet implemented",
+            "NotImplementedError",
+            {"error_type": "NotImplementedError"},
+        )
+        raise NotImplementedError("Document podcast generation is not yet implemented")
+        
+    except Exception as e:
+        await session.rollback()
+        await task_logger.log_task_failure(
+            log_entry,
+            f"Error during document podcast generation for document {document_id}",
+            str(e),
+            {"error_type": type(e).__name__},
+        )
+        raise
 
 
 async def generate_chat_podcast(

surfsense_backend/app/utils/document_converters.py

@@ -1,7 +1,10 @@
 import hashlib
+import logging
 
 from litellm import get_model_info, token_counter
 
+logger = logging.getLogger(__name__)
+
 from app.config import config
 from app.db import Chunk, DocumentType
 from app.prompts import SUMMARY_PROMPT_TEMPLATE
@@ -14,8 +17,8 @@ def get_model_context_window(model_name: str) -> int:
         context_window = model_info.get("max_input_tokens", 4096)  # Default fallback
         return context_window
     except Exception as e:
-        print(
-            f"Warning: Could not get model info for {model_name}, using default 4096 tokens. Error: {e}"
+        logger.warning(
+            f"Could not get model info for {model_name}, using default 4096 tokens. Error: {e}"
         )
         return 4096  # Conservative fallback
 
@@ -41,9 +44,16 @@ def optimize_content_for_context_window(
     context_window = get_model_context_window(model_name)
 
     # Reserve tokens for: system prompt, metadata, template overhead, and output
-    # Conservative estimate: 2000 tokens for prompt + metadata + output buffer
-    # TODO: Calculate Summary System Prompt Token Count Here
-    reserved_tokens = 2000
+    # Calculate actual system prompt token count
+    try:
+        system_prompt_tokens = token_counter(
+            messages=[{"role": "system", "content": SUMMARY_PROMPT_TEMPLATE}], 
+            model=model_name
+        )
+        reserved_tokens = system_prompt_tokens + 1000  # 1000 tokens for output buffer and overhead
+    except Exception as e:
+        logger.warning(f"Could not calculate system prompt tokens, using default: {e}")
+        reserved_tokens = 2000  # Conservative fallback
 
     # Add metadata token cost if present
     if document_metadata:
@@ -58,7 +68,7 @@ def optimize_content_for_context_window(
     available_tokens = context_window - reserved_tokens
 
     if available_tokens <= 100:  # Minimum viable content
-        print(f"Warning: Very limited tokens available for content: {available_tokens}")
+        logger.warning(f"Very limited tokens available for content: {available_tokens}")
         return content[:500]  # Fallback to first 500 chars
 
     # Binary search to find optimal content length
@@ -86,7 +96,7 @@ def optimize_content_for_context_window(
     )
 
     if optimal_length < len(content):
-        print(
+        logger.info(
             f"Content optimized: {len(content)} -> {optimal_length} chars "
             f"to fit in {available_tokens} available tokens"
         )