Create agentic_module.py

ui/agentic_module.py

@@ -0,0 +1,222 @@
+# ui/agentic_module.py
+import gradio as gr
+import logging
+from collections import defaultdict
+
+# --- Module Imports ---
+try:
+    from run_agentic_pipeline import run_full_analytics_orchestration
+    from ui.insights_ui_generator import (
+        format_report_to_markdown,
+        extract_key_results_for_selection,
+        format_single_okr_for_display
+    )
+    AGENTIC_MODULES_LOADED = True
+except ImportError as e:
+    logging.error(f"Could not import agentic pipeline modules in agentic_module.py: {e}.")
+    AGENTIC_MODULES_LOADED = False
+    async def run_full_analytics_orchestration(*args, **kwargs): return 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."
+
+logger = logging.getLogger(__name__)
+
+# Store references to UI components that handlers need to update
+_agentic_report_display_md = None
+_key_results_cbg = None
+_okr_detail_display_md = None
+_agentic_pipeline_status_md = None
+
+
+def handle_update_okr_display(selected_kr_unique_ids: list, raw_orchestration_results: dict, all_krs_for_selection: list):
+    if not raw_orchestration_results or not AGENTIC_MODULES_LOADED:
+        return gr.update(value="Nessun dato dalla pipeline AI o moduli non caricati.")
+    
+    actionable_okrs_dict = raw_orchestration_results.get("actionable_okrs_and_tasks")
+    if not actionable_okrs_dict or not isinstance(actionable_okrs_dict.get("okrs"), list):
+        return gr.update(value="Nessun OKR trovato nei risultati della pipeline.")
+    
+    okrs_list = actionable_okrs_dict["okrs"]
+    # Rebuild kr_id_to_indices based on the structure of all_krs_for_selection
+    # all_krs_for_selection is: [{'okr_index': int, 'kr_index': int, 'unique_kr_id': str, ...}]
+    kr_id_to_indices = {kr_info['unique_kr_id']: (kr_info['okr_index'], kr_info['kr_index']) 
+                        for kr_info in all_krs_for_selection if isinstance(kr_info, dict) and 'unique_kr_id' in kr_info}
+
+    selected_krs_by_okr_idx = defaultdict(list)
+    if selected_kr_unique_ids:
+        for kr_unique_id in selected_kr_unique_ids:
+            if kr_unique_id in kr_id_to_indices:
+                okr_idx, kr_idx = kr_id_to_indices[kr_unique_id]
+                selected_krs_by_okr_idx[okr_idx].append(kr_idx)
+            else:
+                logger.warning(f"Selected KR ID '{kr_unique_id}' not found in kr_id_to_indices map.")
+
+
+    output_md_parts = []
+    if not okrs_list:
+        output_md_parts.append("Nessun OKR generato.")
+    else:
+        for okr_idx, okr_data in enumerate(okrs_list):
+            accepted_indices_for_this_okr = selected_krs_by_okr_idx.get(okr_idx)
+            # If specific KRs are selected, only show OKRs that have at least one of those selected KRs
+            if selected_kr_unique_ids: # User has made a selection
+                if accepted_indices_for_this_okr is not None: # This OKR has some selected KRs
+                     output_md_parts.append(format_single_okr_for_display(okr_data, accepted_kr_indices=accepted_indices_for_this_okr, okr_main_index=okr_idx))
+            else: # No KRs selected, show all OKRs with all their KRs
+                output_md_parts.append(format_single_okr_for_display(okr_data, accepted_kr_indices=None, okr_main_index=okr_idx))
+    
+    if not output_md_parts and selected_kr_unique_ids:
+        final_md = "Nessun OKR corrisponde alla selezione corrente o i KR selezionati non hanno task dettagliati."
+    elif not output_md_parts and not selected_kr_unique_ids and okrs_list : # OKRs exist but somehow didn't format
+        final_md = "Nessun OKR da visualizzare in base alla selezione (o tutti OKR visualizzati)."
+    elif not output_md_parts and not okrs_list:
+         final_md = "Nessun OKR generato."
+    else:
+        final_md = "\n\n---\n\n".join(output_md_parts)
+        
+    return gr.update(value=final_md)
+
+
+async def handle_run_agentic_pipeline(current_token_state_val, orchestration_raw_results_st_val, key_results_for_selection_st_val, selected_key_result_ids_st_val):
+    logger.info(f"Agentic pipeline check triggered. Current token: {'Set' if current_token_state_val.get('token') else 'Not Set'}")
+
+    if not current_token_state_val or not current_token_state_val.get("token"):
+        logger.info("Agentic pipeline: Token not available in token_state. Skipping.")
+        yield (
+            gr.update(value="Pipeline AI: In attesa dei dati necessari..."), # report_display
+            gr.update(choices=[], value=[], interactive=False), # key_results_cbg
+            gr.update(value="Pipeline AI: In attesa dei dati necessari..."), # okr_detail_display
+            None, # orchestration_raw_results_st
+            [],   # selected_key_result_ids_st
+            [],   # key_results_for_selection_st
+            "Pipeline AI: In attesa dei dati..." # agentic_pipeline_status_md
+        )
+        return
+
+    logger.info("Agentic pipeline starting autonomously with 'Sempre' filter.")
+    yield (
+        gr.update(value="Analisi AI (Sempre) in corso..."),
+        gr.update(choices=[], value=[], interactive=False),
+        gr.update(value="Dettagli OKR (Sempre) in corso di generazione..."),
+        orchestration_raw_results_st_val, # Preserve existing results
+        selected_key_result_ids_st_val,
+        key_results_for_selection_st_val,
+        "Esecuzione pipeline AI (Sempre)..."
+    )
+
+    if not AGENTIC_MODULES_LOADED:
+        logger.warning("Agentic modules not loaded. Skipping autonomous pipeline.")
+        yield (
+            gr.update(value="Moduli AI non caricati. Report non disponibile."),
+            gr.update(choices=[], value=[], interactive=False),
+            gr.update(value="Moduli AI non caricati. OKR non disponibili."),
+            None, [], [], "Pipeline AI: Moduli non caricati."
+        )
+        return
+
+    try:
+        date_filter_val_agentic = "Sempre"
+        custom_start_val_agentic = None
+        custom_end_val_agentic = None
+        
+        orchestration_output = await run_full_analytics_orchestration(
+            current_token_state_val, date_filter_val_agentic, 
+            custom_start_val_agentic, custom_end_val_agentic
+        )
+        agentic_status_text = "Pipeline AI (Sempre) completata."
+        logger.info(f"Autonomous agentic pipeline finished. Output keys: {orchestration_output.keys() if orchestration_output else 'None'}")
+
+        if orchestration_output:
+            orchestration_results_update = orchestration_output
+            report_str = orchestration_output.get('comprehensive_analysis_report')
+            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 # This is the list of dicts for the state
+            
+            kr_choices_for_cbg = [(kr['kr_description'], kr['unique_kr_id']) for kr in krs_for_ui_selection_list if isinstance(kr, dict)]
+            key_results_cbg_update = gr.update(choices=kr_choices_for_cbg, value=[], interactive=True)
+            
+            # Default display for OKRs: show all, as if no KR is selected yet.
+            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 selection
+        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, selected_krs_update, krs_for_selection_update, agentic_status_text)
+
+    except Exception as e:
+        logger.error(f"Error during autonomous agentic pipeline execution: {e}", exc_info=True)
+        agentic_status_text = f"Errore pipeline AI (Sempre): {str(e)}"
+        yield (
+            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
+        )
+
+
+def build_and_wire_tabs(orchestration_raw_results_st, key_results_for_selection_st, selected_key_result_ids_st):
+    """Builds the UI for Agentic Tabs and wires up internal event handlers."""
+    global _agentic_report_display_md, _key_results_cbg, _okr_detail_display_md, _agentic_pipeline_status_md
+
+    with gr.TabItem("3️⃣ Agentic Analysis Report", id="tab_agentic_report", visible=AGENTIC_MODULES_LOADED):
+        gr.Markdown("## 🤖 Comprehensive Analysis Report (AI Generated)")
+        _agentic_pipeline_status_md = gr.Markdown("Stato Pipeline AI (filtro 'Sempre'): In attesa...", visible=True)
+        gr.Markdown("Questo report è generato da un agente AI con filtro 'Sempre' sui dati disponibili. Rivedi criticamente.")
+        _agentic_report_display_md = gr.Markdown("La pipeline AI si avvierà automaticamente dopo il caricamento iniziale dei dati o dopo una sincronizzazione.")
+        if not AGENTIC_MODULES_LOADED:
+            gr.Markdown("🔴 **Error:** Agentic pipeline modules could not be loaded. This tab is disabled.")
+
+    with gr.TabItem("4️⃣ Agentic OKRs & Tasks", id="tab_agentic_okrs", visible=AGENTIC_MODULES_LOADED):
+        gr.Markdown("## 🎯 AI Generated OKRs and Actionable Tasks (filtro 'Sempre')")
+        gr.Markdown("Basato sull'analisi AI (filtro 'Sempre'), l'agente ha proposto i seguenti OKR e task. Seleziona i Key Results per dettagli.")
+        if not AGENTIC_MODULES_LOADED:
+            gr.Markdown("🔴 **Error:** Agentic pipeline modules could not be loaded. This tab is disabled.")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("### Suggested Key Results (da analisi 'Sempre')")
+                _key_results_cbg = gr.CheckboxGroup(label="Select Key Results", choices=[], value=[], interactive=True)
+            with gr.Column(scale=3):
+                gr.Markdown("### Detailed OKRs and Tasks for Selected Key Results")
+                _okr_detail_display_md = gr.Markdown("I dettagli OKR appariranno qui dopo l'esecuzione della pipeline AI.")
+        
+        if AGENTIC_MODULES_LOADED:
+            _key_results_cbg.change(
+                fn=handle_update_okr_display, # This handler now correctly returns gr.update()
+                inputs=[_key_results_cbg, orchestration_raw_results_st, key_results_for_selection_st],
+                outputs=[_okr_detail_display_md]
+            )
+
+    # Components to be updated by handle_run_agentic_pipeline
+    # Order must match the yield tuple in handle_run_agentic_pipeline
+    agentic_pipeline_outputs_components = [
+        _agentic_report_display_md, 
+        _key_results_cbg, 
+        _okr_detail_display_md,
+        # orchestration_raw_results_st, # State
+        # selected_key_result_ids_st,   # State
+        # key_results_for_selection_st, # State
+        _agentic_pipeline_status_md
+    ]
+    
+    return agentic_pipeline_outputs_components
+