Update services/report_data_handler.py

services/report_data_handler.py

@@ -23,14 +23,24 @@ def fetch_latest_agentic_analysis(org_urn: str) -> Tuple[Optional[pd.DataFrame],
     Returns the full dataframe and any error message, or None, None.
     """
     logger.info(f"Starting fetch_latest_agentic_analysis for org_urn: {org_urn}")
+
+    current_year = today.year
+    current_quarter = (today.month - 1) // 3 + 1
+    
     if not org_urn:
         logger.warning("fetch_latest_agentic_analysis: org_urn is missing.")
         return None, "org_urn is missing."
 
+    additional_constraint = [
+        {"key": 'quarter', "constraint_type": "equals", "value": current_quarter},
+        {"key": 'year', "constraint_type": "equals", "value": current_year}
+    ]
     try:
         report_data_df, error = fetch_linkedin_posts_data_from_bubble(
             data_type=BUBBLE_REPORT_TABLE_NAME,
-            org_urn=org_urn
+            constraint_value=org_urn,
+            constraint_key='organization_urn',
+            constraint_type = 'equals'
         )
 
         if error:
@@ -336,3 +346,110 @@ def save_actionable_okrs(org_urn: str, actionable_okrs: Dict[str, Any], report_i
         
     except Exception as e:
         logger.exception(f"An unhandled exception occurred during the save_actionable_okrs orchestration for org_urn {org_urn}: {e}")
+
+
+def fetch_and_reconstruct_data_from_bubble(report_df: pd.DataFrame) -> Optional[Dict[str, Any]]:
+    """
+    Fetches the latest report, OKRs, Key Results, and Tasks from Bubble for a given organization
+    and reconstructs them into the nested structure expected by the application.
+
+    Args:
+        org_urn: The URN of the organization.
+
+    Returns:
+        A dictionary containing the reconstructed data ('report_str', 'actionable_okrs', etc.)
+        or None if the report is not found or an error occurs.
+    """
+    # logger.info(f"Starting data fetch and reconstruction for org_urn: {org_urn}")
+    # try:
+    #     # 1. Fetch the latest report for the organization
+    #     # We add a sort field to get the most recent one. 
+    #     report_df, error = fetch_linkedin_posts_data_from_bubble(
+    #         data_type=BUBBLE_REPORT_TABLE_NAME,
+    #         org_urn=org_urn,
+    #         constraint_key="organization_urn"
+    #     )
+
+    #     if error or report_df is None or report_df.empty:
+    #         logger.error(f"Could not fetch latest report for org_urn {org_urn}. Error: {error}")
+    #         return None
+
+        # Get the most recent report (assuming the first one is the latest)
+        latest_report = report_df.iloc[0]
+        report_id = latest_report.get('_id')
+        if not report_id:
+            logger.error("Fetched report is missing a Bubble '_id'.")
+            return None
+        
+        logger.info(f"Fetched latest report with ID: {report_id}")
+
+        # 2. Fetch all related OKRs using the report_id
+        okrs_df, error = fetch_linkedin_posts_data_from_bubble(
+            data_type=BUBBLE_OKR_TABLE_NAME,
+            constraint_value=report_id,
+            constraint_key='report',
+            constraint_type = 'equals'
+        )
+        if error:
+            logger.error(f"Error fetching OKRs for report_id {report_id}: {error}")
+            okrs_df = pd.DataFrame()
+
+        # 3. Fetch all related Key Results using the OKR IDs
+        okr_ids = okrs_df['_id'].tolist() if not okrs_df.empty else []
+        krs_df = pd.DataFrame()
+        if okr_ids:
+            krs_df, error = fetch_linkedin_posts_data_from_bubble(
+                data_type=BUBBLE_KEY_RESULTS_TABLE_NAME,
+                constraint_value=okr_ids,
+                constraint_key='okr',
+                constraint_type='in'
+            )
+            if error:
+                logger.error(f"Error fetching Key Results for OKR IDs {okr_ids}: {error}")
+                krs_df = pd.DataFrame()
+
+        # 4. Fetch all related Tasks using the Key Result IDs
+        kr_ids = krs_df['_id'].tolist() if not krs_df.empty else []
+        tasks_df = pd.DataFrame()
+        if kr_ids:
+            tasks_df, error = fetch_linkedin_posts_data_from_bubble(
+                data_type=BUBBLE_TASKS_TABLE_NAME,
+                constraint_value=kr_ids,
+                constraint_key='key_result',
+                constraint_type='in'
+            )
+            if error:
+                logger.error(f"Error fetching Tasks for KR IDs {kr_ids}: {error}")
+                tasks_df = pd.DataFrame()
+
+        # 5. Reconstruct the nested 'actionable_okrs' dictionary
+        tasks_by_kr_id = tasks_df.groupby('key_result').apply(lambda x: x.to_dict('records')).to_dict()
+        krs_by_okr_id = krs_df.groupby('okr').apply(lambda x: x.to_dict('records')).to_dict()
+
+        reconstructed_okrs = []
+        for okr_data in okrs_df.to_dict('records'):
+            okr_id = okr_data['_id']
+            key_results_list = krs_by_okr_id.get(okr_id, [])
+            
+            for kr_data in key_results_list:
+                kr_id = kr_data['_id']
+                # Attach tasks to each key result
+                kr_data['tasks'] = tasks_by_kr_id.get(kr_id, [])
+            
+            # Attach key results to the objective
+            okr_data['key_results'] = key_results_list
+            reconstructed_okrs.append(okr_data)
+            
+        actionable_okrs = {"okrs": reconstructed_okrs}
+
+        return {
+            "report_str": latest_report.get("report_text", "Nessun report trovato."),
+            "quarter": latest_report.get("quarter"),
+            "year": latest_report.get("year"),
+            "actionable_okrs": actionable_okrs,
+            "report_id": report_id
+        }
+
+    except Exception as e:
+        logger.exception(f"An unexpected error occurred during data reconstruction for org_urn {org_urn}: {e}")
+        return None