Update services/report_data_handler.py

services/report_data_handler.py

@@ -20,6 +20,7 @@ from config import (
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+
 def fetch_latest_agentic_analysis(org_urn: str) -> Tuple[Optional[pd.DataFrame], Optional[str]]:
     """
     Fetches all agentic analysis report data for a given org_urn from Bubble.
@@ -54,31 +55,39 @@ def fetch_latest_agentic_analysis(org_urn: str) -> Tuple[Optional[pd.DataFrame],
         return None, str(e)
 
 
-def fetch_and_reconstruct_data_from_bubble(report_series: pd.Series) -> Optional[Dict[str, Any]]:
+def fetch_and_reconstruct_data_from_bubble(report_series: pd.Series, session_cache: dict) -> Tuple[Optional[Dict[str, Any]], dict]:
     """
-    MODIFIED: Takes a pandas Series of a single report, fetches all related child items 
-    (OKRs, KRs, Tasks) from Bubble, and reconstructs the full nested dictionary.
+    MODIFIED: Takes a pandas Series of a single report and a session-specific cache dictionary.
+    It fetches all related child items from Bubble, reconstructs the full nested dictionary,
+    and uses the cache to avoid redundant API calls.
 
     Args:
         report_series: A pandas Series representing a single report to be processed.
+        session_cache: The session-specific cache dictionary from a Gradio State.
 
     Returns:
-        A dictionary containing the reconstructed data ('report_str', 'actionable_okrs'),
-        or None if the report series is invalid or a critical error occurs.
+        A tuple containing:
+        - The reconstructed data dictionary.
+        - The updated session_cache dictionary.
     """
-    logger.info("Starting data reconstruction from a single fetched Bubble report.")
+    logger.info("Attempting to get or reconstruct data for a Bubble report using session cache.")
     if report_series is None or report_series.empty:
         logger.warning("Cannot reconstruct data, the provided report Series is empty.")
-        return None
+        return None, session_cache
 
-    try:
-        report_id = report_series.get('_id')
-        if not report_id:
-            logger.error("Fetched report series is missing a Bubble '_id', cannot reconstruct children.")
-            return None
+    report_id = report_series.get('_id')
+    if not report_id:
+        logger.error("Fetched report series is missing a Bubble '_id', cannot reconstruct children.")
+        return None, session_cache
 
-        logger.info(f"Reconstructing all related data for report, ID: {report_id}")
+    # --- CACHE CHECK ---
+    if report_id in session_cache:
+        logger.info(f"CACHE HIT: Found reconstructed data for report ID {report_id} in session cache.")
+        return session_cache[report_id], session_cache
+    
+    logger.info(f"CACHE MISS: No data for report ID {report_id}. Starting reconstruction from Bubble.io.")
 
+    try:
         # 1. Fetch all related OKRs using the report_id
         okrs_df, error = fetch_linkedin_posts_data_from_bubble(
             data_type=BUBBLE_OKR_TABLE_NAME,
@@ -88,7 +97,7 @@ def fetch_and_reconstruct_data_from_bubble(report_series: pd.Series) -> Optional
         )
         if error:
             logger.error(f"Error fetching OKRs for report_id {report_id}: {error}")
-            return None # Fail reconstruction if children can't be fetched
+            return None, session_cache
 
         # 2. Fetch all related Key Results using the OKR IDs
         okr_ids = okrs_df['_id'].tolist() if not okrs_df.empty else []
@@ -138,9 +147,13 @@ def fetch_and_reconstruct_data_from_bubble(report_series: pd.Series) -> Optional
             "actionable_okrs": actionable_okrs,
             "report_id": report_id
         }
-        logger.info(f"Successfully reconstructed nested data structure for report {report_id}.")
-        return final_reconstructed_data
+        
+        # --- STORE IN SESSION CACHE ---
+        session_cache[report_id] = final_reconstructed_data
+        logger.info(f"Successfully reconstructed and cached data for report {report_id} in the current session.")
+        
+        return final_reconstructed_data, session_cache
 
     except Exception as e:
         logger.exception(f"An unexpected error occurred during data reconstruction: {e}")
-        return None
+        return None, session_cache