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| import gradio as gr | |
| import pandas as pd | |
| import logging | |
| from typing import Dict, Any, List, Optional | |
| # Import the reconstruction function that now expects a cache dictionary | |
| from services.report_data_handler import fetch_and_reconstruct_data_from_bubble | |
| # UI formatting functions | |
| try: | |
| from ui.insights_ui_generator import ( | |
| format_report_for_display, | |
| 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 display modules: {e}. Tabs 3 and 4 will be disabled.") | |
| AGENTIC_MODULES_LOADED = False | |
| def format_report_for_display(report_data): return "Agentic modules not loaded." | |
| def extract_key_results_for_selection(okrs_dict): return [] | |
| def format_single_okr_for_display(okr_data, **kwargs): return "Agentic modules not loaded." | |
| logger = logging.getLogger(__name__) | |
| def load_and_display_agentic_results(token_state: dict, session_cache: dict): | |
| """ | |
| Loads agentic results from state, populates the report library, and displays | |
| the LATEST report and its fully reconstructed OKRs by default, using a session-specific cache. | |
| Args: | |
| token_state: The main state dictionary with Bubble data. | |
| session_cache: The session-specific cache for reconstructed data. | |
| Returns: | |
| A tuple of Gradio updates, including the updated cache. | |
| """ | |
| initial_updates = ( | |
| "No agentic analysis data found in Bubble.", | |
| gr.update(choices=[], value=None, interactive=False), | |
| gr.update(choices=[], value=[], interactive=False), | |
| "No OKRs to display.", | |
| None, [], [], "Status: No agentic analysis data found.", | |
| session_cache # Return the cache unchanged | |
| ) | |
| if not AGENTIC_MODULES_LOADED: | |
| error_updates = list(initial_updates) | |
| error_updates[7] = "Status: Critical module import error." | |
| return tuple(error_updates) | |
| agentic_df = token_state.get("bubble_agentic_analysis_data") | |
| if agentic_df is None or agentic_df.empty: | |
| logger.warning("Agentic analysis DataFrame is missing or empty.") | |
| return initial_updates | |
| try: | |
| if 'Created Date' not in agentic_df.columns or '_id' not in agentic_df.columns: | |
| raise KeyError("Required columns ('Created Date', '_id') not found.") | |
| agentic_df['Created Date'] = pd.to_datetime(agentic_df['Created Date']) | |
| agentic_df = agentic_df.sort_values(by='Created Date', ascending=False).reset_index(drop=True) | |
| report_choices = [(f"{row.get('report_type', 'Report')} - {row['Created Date'].strftime('%Y-%m-%d %H:%M')}", row['_id']) | |
| for _, row in agentic_df.iterrows()] | |
| if not report_choices: | |
| return initial_updates | |
| quarterly_reports_df = agentic_df[agentic_df['report_type'] == 'Quarter'].copy() | |
| latest_report_series = quarterly_reports_df.iloc[0] | |
| latest_report_id = latest_report_series['_id'] | |
| report_display_md = format_report_for_display(latest_report_series) | |
| report_selector_update = gr.update(choices=report_choices, value=latest_report_id, interactive=True) | |
| # --- MODIFIED: Use the session cache for data reconstruction --- | |
| reconstructed_data, updated_cache = fetch_and_reconstruct_data_from_bubble(latest_report_series, session_cache) | |
| raw_results_state, okr_details_md = None, "No OKRs found in the latest report." | |
| key_results_cbg_update = gr.update(choices=[], value=[], interactive=False) | |
| all_krs_state = [] | |
| if reconstructed_data: | |
| raw_results_state = reconstructed_data | |
| actionable_okrs_dict = raw_results_state.get("actionable_okrs", {}) | |
| if actionable_okrs_dict: | |
| all_krs_state = extract_key_results_for_selection(actionable_okrs_dict) | |
| if all_krs_state: | |
| kr_choices = [(kr['kr_description'], kr['unique_kr_id']) for kr in all_krs_state] | |
| key_results_cbg_update = gr.update(choices=kr_choices, value=[], interactive=True) | |
| okrs_list = actionable_okrs_dict.get("okrs", []) | |
| output_md_parts = [format_single_okr_for_display(okr, okr_main_index=i) for i, okr in enumerate(okrs_list)] | |
| okr_details_md = "\n\n---\n\n".join(output_md_parts) if output_md_parts else okr_details_md | |
| else: | |
| logger.error(f"Failed to reconstruct data for latest report ID {latest_report_id}") | |
| okr_details_md = "Error: Could not reconstruct OKR data for this report." | |
| status_update = f"Status: Loaded {len(agentic_df)} reports. Displaying latest from {latest_report_series['Created Date'].strftime('%Y-%m-%d')}." | |
| return ( | |
| report_display_md, report_selector_update, key_results_cbg_update, | |
| okr_details_md, raw_results_state, [], all_krs_state, status_update, | |
| updated_cache # Return the potentially updated cache | |
| ) | |
| except Exception as e: | |
| logger.error(f"Failed to process and display agentic results: {e}", exc_info=True) | |
| error_updates = list(initial_updates) | |
| error_updates[0] = f"An error occurred while loading reports: {e}" | |
| error_updates[7] = f"Status: Error - {e}" | |
| return tuple(error_updates) | |