Update run_agentic_pipeline.py

run_agentic_pipeline.py

@@ -44,7 +44,7 @@ except ImportError as e:
     def extract_key_results_for_selection(okrs_dict): return [] # Placeholder
     def format_single_okr_for_display(okr_data, **kwargs): return "Agentic modules not loaded. OKR display unavailable." # Placeholder
 
-from services.report_data_handler import save_report_results, save_actionable_okrs
+from services.report_data_handler import save_report_results, save_actionable_okrs, fetch_and_reconstruct_data_from_bubble
 
 logger = logging.getLogger(__name__)
 
@@ -165,20 +165,54 @@ async def run_agentic_pipeline_autonomously(current_token_state_val, orchestrati
         )
         return
 
-    #DA AGGIORNARE CON RIPORTO DEI DATI PRESI DA BUBBLE E FORMATTATI NEL MODO GIUSTO
     if not current_token_state_val.get("agentic_pipeline_should_run_now", False):
-        logging.warning("no need for agentic pipeline to run. Data stored in db.")
+        logging.info("Fetching existing data from Bubble as pipeline run is not required.")
+        
+        # Call the new function to get reconstructed data
+        retrieved_data = fetch_and_reconstruct_data_from_bubble(org_urn)
+
+        if not retrieved_data:
+            logging.warning(f"No data found in Bubble for org_urn {org_urn}. Informing user.")
+            yield (
+                gr.update(value="Nessun dato di analisi precedente trovato in Bubble."),
+                gr.update(choices=[], value=[], interactive=False),
+                gr.update(value="Eseguire la pipeline per generare un nuovo report."),
+                None, [], [], "Pipeline AI: Dati non disponibili"
+            )
+            return
+
+        # If data is found, format it for the UI
+        report_str = retrieved_data.get('report_str')
+        actionable_okrs = retrieved_data.get('actionable_okrs')
+
+        agentic_report_md_update = gr.update(value=format_report_to_markdown(report_str))
+        
+        krs_for_ui_selection_list = extract_key_results_for_selection(actionable_okrs)
+        kr_choices_for_cbg = [(kr['kr_description'], kr['unique_kr_id']) for kr in krs_for_ui_selection_list]
+        key_results_cbg_update = gr.update(choices=kr_choices_for_cbg, value=[], interactive=True)
+        
+        all_okrs_md_parts = []
+        if actionable_okrs and isinstance(actionable_okrs.get("okrs"), list):
+            for okr_idx, okr_item in enumerate(actionable_okrs["okrs"]):
+                all_okrs_md_parts.append(format_single_okr_for_display(okr_item, accepted_kr_indices=None, okr_main_index=okr_idx))
+        
+        if not all_okrs_md_parts:
+            okr_detail_display_md_update = gr.update(value="Nessun OKR trovato per il report più recente.")
+        else:
+            okr_detail_display_md_update = gr.update(value="\n\n---\n\n".join(all_okrs_md_parts))
+
+        # Yield the updates for the Gradio interface
         yield (
-            gr.update(value="Data stored in db"),
-            gr.update(choices=[], value=[], interactive=False),
-            gr.update(value="Data stored in db"),
-            None, [], [], "Pipeline AI: Data stored in db"
+            agentic_report_md_update,
+            key_results_cbg_update,
+            okr_detail_display_md_update,
+            retrieved_data,  # Store full retrieved data in state
+            [],  # Reset selected KRs state
+            krs_for_ui_selection_list,  # Update state with list of KR dicts
+            "Pipeline AI: Dati caricati da Bubble"
         )
         return
 
-    # else:
-    #     continue
-
         
     try:
         # Parameters for 'Sempre' filter for the agentic pipeline