Update run_agentic_pipeline.py

run_agentic_pipeline.py

@@ -8,13 +8,25 @@ import pandas as pd
 from typing import Dict, Any, Optional
 import gradio as gr
 
+# Assuming this script is at the same level as 'app.py' and 'insight_and_tasks/' is a subfolder
+# If 'insight_and_tasks' is not in python path, you might need to adjust sys.path
+# For example, if insight_and_tasks is a sibling of the dir containing this file:
+# import sys
+# script_dir = os.path.dirname(os.path.abspath(__file__))
+# project_root = os.path.dirname(script_dir) # Or navigate to the correct root
+# sys.path.insert(0, project_root)
+
 os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "False"
 GOOGLE_API_KEY = os.environ.get("GEMINI_API_KEY")
 os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
 
 # Imports from your project structure
 from features.insight_and_tasks.orchestrators.linkedin_analytics_orchestrator import EnhancedLinkedInAnalyticsOrchestrator
+# setup_logging might be called in app.py, if not, call it here or ensure it's called once.
+# from insight_and_tasks.utils.logging_config import setup_logging 
 from data_processing.analytics_data_processing import prepare_filtered_analytics_data 
+# Placeholder for UI generator import - to be created later
+# from .insights_ui_generator import format_orchestration_results_for_ui 
 
 try:
     from ui.insights_ui_generator import (
@@ -26,39 +38,48 @@ try:
 except ImportError as e:
     logging.error(f"Could not import agentic pipeline modules: {e}. Tabs 3 and 4 will be disabled.")
     AGENTIC_MODULES_LOADED = False
-    async def run_full_analytics_orchestration_streaming(*args, **kwargs): 
-        yield None
-    def format_report_to_markdown(report_string): return "Agentic modules not loaded. Report unavailable."
-    def extract_key_results_for_selection(okrs_dict): return []
-    def format_single_okr_for_display(okr_data, **kwargs): return "Agentic modules not loaded. OKR display unavailable."
+    async def run_full_analytics_orchestration(*args, **kwargs): return None # Placeholder
+    def format_report_to_markdown(report_string): return "Agentic modules not loaded. Report unavailable." # Placeholder
+    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
+
 
 logger = logging.getLogger(__name__)
 
-async def run_full_analytics_orchestration_streaming(
+
+async def run_full_analytics_orchestration(
     token_state: Dict[str, Any],
     date_filter_selection: str,
     custom_start_date: Optional[datetime],
     custom_end_date: Optional[datetime]
-):
+) -> Optional[Dict[str, Any]]:
     """
-    Runs the full analytics pipeline with streaming results.
-    Yields results as they become available.
+    Runs the full analytics pipeline using data from token_state and date filters,
+    and returns the raw orchestration results.
+    Args:
+        token_state: Gradio token_state containing raw data and config.
+        date_filter_selection: String for date filter type.
+        custom_start_date: Optional custom start date.
+        custom_end_date: Optional custom end date.
+    Returns:
+        A dictionary containing the results from the analytics orchestrator,
+        or None if a critical error occurs.
     """
     if not GOOGLE_API_KEY:
         logger.critical("GOOGLE_API_KEY is not set. Analytics pipeline cannot run.")
-        return
+        return None
 
-    logger.info("Starting streaming analytics orchestration process...")
+    logger.info("Starting full analytics orchestration process...")
 
     # 1. Prepare and filter data
     try:
         (
             filtered_posts_df,
             filtered_mentions_df,
-            _date_filtered_follower_stats_df,
-            raw_follower_stats_df,
-            _start_dt,
-            _end_dt
+            _date_filtered_follower_stats_df, # This might be used if FollowerAgent specifically needs pre-filtered time series
+            raw_follower_stats_df, # FollowerAgent typically processes raw historical for some metrics
+            _start_dt, # Filtered start date, for logging or context if needed
+            _end_dt    # Filtered end date
         ) = prepare_filtered_analytics_data(
             token_state, date_filter_selection, custom_start_date, custom_end_date
         )
@@ -66,48 +87,53 @@ async def run_full_analytics_orchestration_streaming(
 
     except Exception as e:
         logger.error(f"Error during data preparation: {e}", exc_info=True)
-        return
+        return None
 
-    # Check if essential dataframes are empty
+    # Check if essential dataframes are empty after filtering, which might make analysis trivial or erroneous
     if filtered_posts_df.empty and filtered_mentions_df.empty and raw_follower_stats_df.empty:
         logger.warning("All essential DataFrames are empty after filtering. Orchestration might yield limited results.")
+        # Depending on requirements, you might return a specific message or empty results structure.
 
-    # 2. Initialize and run the orchestrator with streaming
+    # 2. Initialize and run the orchestrator
     try:
-        llm_model_for_run = "gemini-2.5-flash-preview-05-20"
+        # You can pass a specific model name or let the orchestrator use its default
+        llm_model_for_run = "gemini-2.5-flash-preview-05-20" #token_state.get("config_llm_model_override") # Example: if you store this in token_state
         
         orchestrator = EnhancedLinkedInAnalyticsOrchestrator(
             api_key=GOOGLE_API_KEY,
-            llm_model_name=llm_model_for_run,
+            llm_model_name=llm_model_for_run, # Pass None to use orchestrator's default
             current_date_for_tasks=datetime.utcnow().date()
         )
 
-        logger.info("Orchestrator initialized. Starting streaming analysis...")
-        
-        # Use the new streaming method
-        async for result in orchestrator.generate_full_analysis_and_tasks_streaming(
-            follower_stats_df=raw_follower_stats_df,
+        logger.info("Orchestrator initialized. Generating full analysis and tasks...")
+        # The orchestrator expects the primary follower stats DF to be the one it can process for
+        # time-series ('follower_gains_monthly') and demographics.
+        # The `raw_follower_stats_df` is usually better for this, as FollowerAgent does its own processing.
+        orchestration_results = await orchestrator.generate_full_analysis_and_tasks(
+            follower_stats_df=raw_follower_stats_df, # Pass the full history for followers
             post_df=filtered_posts_df,
             mentions_df=filtered_mentions_df
-        ):
-            yield result
+        )
+        logger.info("Orchestration process completed.")
+        return orchestration_results
 
     except Exception as e:
         logger.critical(f"Critical error during analytics orchestration: {e}", exc_info=True)
-        return
+        return None
+
 
-async def run_agentic_pipeline_autonomously(current_token_state_val, orchestration_raw_results_st, selected_key_result_ids_st, key_results_for_selection_st):
+
+async def run_agentic_pipeline_autonomously(current_token_state_val, orchestration_raw_results_st,selected_key_result_ids_st, key_results_for_selection_st):
     logging.info(f"Agentic pipeline check triggered for token_state update. Current token: {'Set' if current_token_state_val.get('token') else 'Not Set'}")
-    
     # Initial state before pipeline runs or if skipped
     initial_yield = (
-        gr.update(value="Pipeline AI: In attesa dei dati necessari..."),
-        gr.update(choices=[], value=[], interactive=False),
-        gr.update(value="Pipeline AI: In attesa dei dati necessari..."),
-        orchestration_raw_results_st,
-        selected_key_result_ids_st,
-        key_results_for_selection_st,
-        "Pipeline AI: In attesa dei dati..."
+        gr.update(value="Pipeline AI: In attesa dei dati necessari..."), # agentic_report_display_md
+        gr.update(choices=[], value=[], interactive=False),             # key_results_cbg
+        gr.update(value="Pipeline AI: In attesa dei dati necessari..."), # okr_detail_display_md
+        orchestration_raw_results_st, # Preserve current raw results
+        selected_key_result_ids_st,   # Preserve current selection
+        key_results_for_selection_st, # Preserve current options
+        "Pipeline AI: In attesa dei dati..." # agentic_pipeline_status_md
     )
 
     if not current_token_state_val or not current_token_state_val.get("token"):
@@ -116,15 +142,14 @@ async def run_agentic_pipeline_autonomously(current_token_state_val, orchestrati
         return
 
     logging.info("Agentic pipeline starting autonomously with 'Sempre' filter.")
-    
     # Update status to indicate processing
     yield (
         gr.update(value="Analisi AI (Sempre) in corso..."),
-        gr.update(choices=[], value=[], interactive=False),
+        gr.update(choices=[], value=[], interactive=False), # Keep CBG disabled during run
         gr.update(value="Dettagli OKR (Sempre) in corso di generazione..."),
-        orchestration_raw_results_st,
-        selected_key_result_ids_st,
-        key_results_for_selection_st,
+        orchestration_raw_results_st, # Preserve
+        selected_key_result_ids_st,   # Preserve
+        key_results_for_selection_st, # Preserve
         "Esecuzione pipeline AI (Sempre)..."
     )
 
@@ -144,75 +169,58 @@ async def run_agentic_pipeline_autonomously(current_token_state_val, orchestrati
         custom_start_val_agentic = None
         custom_end_val_agentic = None
 
-        # Use the streaming orchestration
-        async for orchestration_output in run_full_analytics_orchestration_streaming(
+        orchestration_output = await run_full_analytics_orchestration(
             current_token_state_val,
             date_filter_val_agentic,
             custom_start_val_agentic,
             custom_end_val_agentic
-        ):
-            if not orchestration_output:
-                continue
-                
-            status = orchestration_output.get("status", "unknown")
+        )
+        agentic_status_text = "Pipeline AI (Sempre) completata."
+        logging.info(f"Autonomous agentic pipeline finished. Output keys: {orchestration_output.keys() if orchestration_output else 'None'}")
+
+        if orchestration_output:
+            orchestration_results_update = orchestration_output # Store full results in state
+            report_str = orchestration_output.get('comprehensive_analysis_report', "Nessun report dettagliato fornito.")
+            agentic_report_md_update = gr.update(value=format_report_to_markdown(report_str))
+
+            actionable_okrs = orchestration_output.get('actionable_okrs_and_tasks') # This is the dict containing 'okrs' list
+            krs_for_ui_selection_list = extract_key_results_for_selection(actionable_okrs) # Expects the dict
             
-            if status == "report_ready":
-                # Report is ready, but OKRs are not yet
-                logging.info("Report ready, displaying preliminary results...")
-                
-                report_str = orchestration_output.get('comprehensive_analysis_report', "Nessun report dettagliato fornito.")
-                agentic_report_md_update = gr.update(value=format_report_to_markdown(report_str))
-                
-                yield (
-                    agentic_report_md_update,
-                    gr.update(choices=[], value=[], interactive=False),  # Keep OKRs disabled
-                    gr.update(value="OKR ancora in elaborazione..."),
-                    orchestration_output,  # Store partial results
-                    selected_key_result_ids_st,
-                    key_results_for_selection_st,
-                    "Report AI completato. Generazione OKR in corso..."
-                )
+            krs_for_selection_update = krs_for_ui_selection_list # Update state with list of KR dicts
             
-            elif status == "complete":
-                # Everything is ready
-                logging.info("Complete results ready, displaying final output...")
-                
-                orchestration_results_update = orchestration_output
-                report_str = orchestration_output.get('comprehensive_analysis_report', "Nessun report dettagliato fornito.")
-                agentic_report_md_update = gr.update(value=format_report_to_markdown(report_str))
-
-                actionable_okrs = orchestration_output.get('actionable_okrs_and_tasks')
-                krs_for_ui_selection_list = extract_key_results_for_selection(actionable_okrs)
-                
-                krs_for_selection_update = krs_for_ui_selection_list
-                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)
-
-                # Display all OKRs by default
-                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 generato o trovato (Sempre).")
-                else:
-                    okr_detail_display_md_update = gr.update(value="\n\n---\n\n".join(all_okrs_md_parts))
-                
-                selected_krs_update = []
-                agentic_status_text = "Pipeline AI (Sempre) completata completamente."
-
-                yield (
-                    agentic_report_md_update,
-                    key_results_cbg_update,
-                    okr_detail_display_md_update,
-                    orchestration_results_update,
-                    selected_krs_update,
-                    krs_for_selection_update,
-                    agentic_status_text
-                )
-                break  # Final result yielded, exit the loop
+            # Choices for CheckboxGroup: list of (label, value) tuples
+            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) # Reset selection
+
+            # Display all OKRs by default after pipeline run
+            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 generato o trovato (Sempre).")
+            else:
+                okr_detail_display_md_update = gr.update(value="\n\n---\n\n".join(all_okrs_md_parts))
+            
+            selected_krs_update = [] # Reset selected KRs state
+        else:
+            agentic_report_md_update = gr.update(value="Nessun report generato dalla pipeline AI (Sempre).")
+            key_results_cbg_update = gr.update(choices=[], value=[], interactive=False)
+            okr_detail_display_md_update = gr.update(value="Nessun OKR generato o errore nella pipeline AI (Sempre).")
+            orchestration_results_update = None
+            selected_krs_update = []
+            krs_for_selection_update = []
 
+        yield (
+            agentic_report_md_update,
+            key_results_cbg_update,
+            okr_detail_display_md_update,
+            orchestration_results_update, # state
+            selected_krs_update,          # state
+            krs_for_selection_update,     # state
+            agentic_status_text
+        )
     except Exception as e:
         logging.error(f"Error during autonomous agentic pipeline execution: {e}", exc_info=True)
         agentic_status_text = f"Errore pipeline AI (Sempre): {str(e)}"
@@ -220,5 +228,5 @@ async def run_agentic_pipeline_autonomously(current_token_state_val, orchestrati
             gr.update(value=f"Errore generazione report AI (Sempre): {str(e)}"),
             gr.update(choices=[], value=[], interactive=False),
             gr.update(value=f"Errore generazione OKR AI (Sempre): {str(e)}"),
-            None, [], [], agentic_status_text
+            None, [], [], agentic_status_text # Reset states on error
         )