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): # Ensure this placeholder returns a dictionary matching the expected structure return {'header_html': '

Agentic modules not loaded.

', 'body_markdown': 'Report display unavailable.'} 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. """ # Define placeholder content for empty or error states to match 10 outputs empty_header_html = """
📊 Comprehensive Analysis Report
AI-Generated Insights from Your LinkedIn Data
Generated from Bubble.io
""" empty_body_markdown_no_selection = """
📄
No Report Selected
Please select a report from the library above to view its detailed analysis and insights.
""" initial_updates = ( gr.update(value="Status: No agentic analysis data found."), # 0: agentic_pipeline_status_md gr.update(choices=[], value=None, interactive=False), # 1: report_selector_dd gr.update(choices=[], value=[], interactive=False), # 2: key_results_cbg gr.update(value="No OKRs to display."), # 3: okr_detail_display_md gr.update(value=None), # 4: orchestration_raw_results_st gr.update(value=[]), # 5: selected_key_result_ids_st gr.update(value=[]), # 6: key_results_for_selection_st gr.update(value=empty_header_html), # 7: report_header_html_display gr.update(value=empty_body_markdown_no_selection), # 8: report_body_markdown_display gr.update(value=session_cache) # 9: reconstruction_cache_st ) if not AGENTIC_MODULES_LOADED: logger.error("Agentic modules not loaded, returning placeholder updates.") # Ensure error updates match the 10-item signature error_header_html = '
Error Loading Report
Agentic modules not loaded.
Error
' error_body_markdown = '
❌
Module Error
Agentic analysis modules could not be loaded. Report display unavailable.
' return ( gr.update(value="Status: Critical module import error."), # 0 gr.update(choices=[], value=None, interactive=False), # 1 gr.update(choices=[], value=[], interactive=False), # 2 gr.update(value="Agentic modules not loaded. No OKRs to display."), # 3 gr.update(value=None), # 4 gr.update(value=[]), # 5 gr.update(value=[]), # 6 gr.update(value=error_header_html), # 7 gr.update(value=error_body_markdown), # 8 gr.update(value=session_cache) # 9 ) 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() if quarterly_reports_df.empty: logger.warning("No quarterly reports found, falling back to latest available report.") latest_report_series = agentic_df.iloc[0] # Use the absolute latest if no quarterly else: latest_report_series = quarterly_reports_df.iloc[0] latest_report_id = latest_report_series['_id'] # Split the formatted report content into header and body formatted_report_parts = format_report_for_display(latest_report_series) report_header_content = formatted_report_parts['header_html'] report_body_content = formatted_report_parts['body_markdown'] 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 = None okr_details_md = "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.info(f"No actionable_okrs found in reconstructed data for report ID {latest_report_id}.") 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 ( gr.update(value=status_update), # 0: agentic_pipeline_status_md report_selector_update, # 1: report_selector_dd key_results_cbg_update, # 2: key_results_cbg gr.update(value=okr_details_md), # 3: okr_detail_display_md gr.update(value=raw_results_state), # 4: orchestration_raw_results_st gr.update(value=[]), # 5: selected_key_result_ids_st (always start empty for selection) gr.update(value=all_krs_state), # 6: key_results_for_selection_st gr.update(value=report_header_content), # 7: report_header_html_display gr.update(value=report_body_content), # 8: report_body_markdown_display gr.update(value=updated_cache) # 9: reconstruction_cache_st ) except Exception as e: logger.error(f"Failed to process and display agentic results: {e}", exc_info=True) # Ensure error returns match the 10-item signature error_header_html = """
⚠️ Error Loading Report
An error occurred during data processing.
Error
""" error_body_markdown = f"""
🚨
Report Loading Failed
An error occurred while loading or processing the report data: {e}. Please try again or contact support if the issue persists.
""" return ( gr.update(value=f"Status: An error occurred: {e}"), # 0 gr.update(choices=[], value=None, interactive=False), # 1 gr.update(choices=[], value=[], interactive=False), # 2 gr.update(value="Error: Could not display OKRs due to an error."), # 3 gr.update(value=None), # 4 gr.update(value=[]), # 5 gr.update(value=[]), # 6 gr.update(value=error_header_html), # 7 gr.update(value=error_body_markdown), # 8 gr.update(value=session_cache) # 9 )