import gradio as gr import pandas as pd import os import logging from collections import defaultdict import matplotlib matplotlib.use('Agg') # Set backend for Matplotlib # --- Module Imports --- from utils.gradio_utils import get_url_user_token # Functions from newly created/refactored modules from config import ( PLOT_ID_TO_FORMULA_KEY_MAP, LINKEDIN_CLIENT_ID_ENV_VAR, BUBBLE_APP_NAME_ENV_VAR, BUBBLE_API_KEY_PRIVATE_ENV_VAR, BUBBLE_API_ENDPOINT_ENV_VAR ) # UPDATED: Using the new data loading function from the refactored state manager from services.state_manager import load_data_from_bubble from ui.ui_generators import ( build_analytics_tab_plot_area, build_home_tab_ui, # NEW: Import the function to build the Home tab UI create_enhanced_report_tab, # NEW: Import the function to build the enhanced Report tab UI BOMB_ICON, EXPLORE_ICON, FORMULA_ICON, ACTIVE_ICON ) from ui.analytics_plot_generator import update_analytics_plots_figures, create_placeholder_plot from formulas import PLOT_FORMULAS # --- CHATBOT MODULE IMPORTS --- from features.chatbot.chatbot_prompts import get_initial_insight_prompt_and_suggestions from features.chatbot.chatbot_handler import generate_llm_response # --- AGENTIC PIPELINE (DISPLAY ONLY) IMPORTS --- try: # This is the main function called on initial load to populate the agentic tabs from run_agentic_pipeline import load_and_display_agentic_results # This function is now called when a new report is selected from the dropdown from services.report_data_handler import fetch_and_reconstruct_data_from_bubble # UI formatting functions from ui.insights_ui_generator import ( format_report_for_display, # This will now return header HTML and body 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 display modules: {e}. Tabs 3 and 4 will be disabled.") AGENTIC_MODULES_LOADED = False # Placeholder functions to prevent app from crashing if imports fail def load_and_display_agentic_results(*args, **kwargs): # NOTE: This return signature MUST match agentic_display_outputs # Adjusted return values for the new split report display components empty_header_html = """
📊 Comprehensive Analysis Report
AI-Generated Insights from Your LinkedIn Data
Generated from Bubble.io
""" empty_body_markdown = """
📄
No Report Selected
Please select a report from the library above to view its detailed analysis and insights.
""" # The load_and_display_agentic_results function returns many values. # Ensure the placeholder returns the correct number of gr.update components # matching the `outputs` in the .then() call later. return ( gr.update(value="Modules not loaded."), # agentic_pipeline_status_md gr.update(choices=[], value=None), # report_selector_dd gr.update(choices=[], value=[]), # key_results_cbg gr.update(value="Modules not loaded."), # okr_detail_display_md None, # orchestration_raw_results_st [], # selected_key_result_ids_st [], # key_results_for_selection_st gr.update(value=empty_header_html), # report_header_html_display gr.update(value=empty_body_markdown), # report_body_markdown_display {} # reconstruction_cache_st ) def fetch_and_reconstruct_data_from_bubble(*args, **kwargs): return None, {} def format_report_for_display(report_data): # Placeholder for when modules are not loaded, returns structure matching the new design return {'header_html': '

Agentic modules not loaded.

', 'body_markdown': 'Report display unavailable.'} def extract_key_results_for_selection(okr_data): return [] def format_single_okr_for_display(okr_data, **kwargs): return "Agentic modules not loaded. OKR display unavailable." # --- ANALYTICS TAB MODULE IMPORT --- from services.analytics_tab_module import AnalyticsTab # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(module)s - %(message)s') # API Key Setup user_provided_api_key = os.environ.get("GEMINI_API_KEY") if user_provided_api_key: os.environ["GOOGLE_API_KEY"] = user_provided_api_key logging.info("GOOGLE_API_KEY environment variable has been set from GEMINI_API_KEY.") else: logging.error("CRITICAL ERROR: The API key environment variable 'GEMINI_API_KEY' was not found.") with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"), title="LinkedIn Organization Dashboard") as app: # --- STATE MANAGEMENT --- token_state = gr.State(value={ "token": None, "client_id": None, "org_urn": None, "bubble_posts_df": pd.DataFrame(), "bubble_post_stats_df": pd.DataFrame(), "bubble_mentions_df": pd.DataFrame(), "bubble_follower_stats_df": pd.DataFrame(), "bubble_agentic_analysis_data": pd.DataFrame(), # To store agentic results from Bubble "url_user_token_temp_storage": None, "config_date_col_posts": "published_at", "config_date_col_mentions": "date", "config_date_col_followers": "date", "config_media_type_col": "media_type", "config_eb_labels_col": "li_eb_label" }) # States for analytics tab chatbot chat_histories_st = gr.State({}) current_chat_plot_id_st = gr.State(None) plot_data_for_chatbot_st = gr.State({}) # States for agentic results display orchestration_raw_results_st = gr.State(None) key_results_for_selection_st = gr.State([]) selected_key_result_ids_st = gr.State([]) # --- NEW: Session-specific cache for reconstructed OKR data --- reconstruction_cache_st = gr.State({}) # --- UI LAYOUT --- gr.Markdown("# 🚀 LinkedIn Organization Dashboard") url_user_token_display = gr.Textbox(label="User Token (Hidden)", interactive=False, visible=False) org_urn_display = gr.Textbox(label="Org URN (Hidden)", interactive=False, visible=False) status_box = gr.Textbox(label="Status", interactive=False, value="Initializing...") app.load(fn=get_url_user_token, inputs=None, outputs=[url_user_token_display, org_urn_display], api_name="get_url_params", show_progress=False) def initial_data_load_sequence(url_token, org_urn_val, current_state): """ Handles the initial data loading from Bubble. No longer generates dashboard HTML as the Home tab is now static. """ status_msg, new_state = load_data_from_bubble(url_token, org_urn_val, current_state) return status_msg, new_state analytics_icons = {'bomb': BOMB_ICON, 'explore': EXPLORE_ICON, 'formula': FORMULA_ICON, 'active': ACTIVE_ICON} analytics_tab_instance = AnalyticsTab( token_state=token_state, chat_histories_st=chat_histories_st, current_chat_plot_id_st=current_chat_plot_id_st, plot_data_for_chatbot_st=plot_data_for_chatbot_st, plot_id_to_formula_map=PLOT_ID_TO_FORMULA_KEY_MAP, plot_formulas_data=PLOT_FORMULAS, icons=analytics_icons, fn_build_plot_area=build_analytics_tab_plot_area, fn_update_plot_figures=update_analytics_plots_figures, fn_create_placeholder_plot=create_placeholder_plot, fn_get_initial_insight=get_initial_insight_prompt_and_suggestions, fn_generate_llm_response=generate_llm_response ) def update_report_display(selected_report_id: str, current_token_state: dict): """ Updates the report header and body display when a new report is selected. This function now expects format_report_for_display to return a dict with 'header_html' and 'body_markdown'. """ # Define empty states for header and body empty_header_html = """
📊 Comprehensive Analysis Report
AI-Generated Insights from Your LinkedIn Data
Generated from Bubble.io
""" empty_body_markdown_no_selection = """
📋
Select a Report
Choose a report from the dropdown above to view its detailed analysis and insights.
""" empty_body_markdown_no_data = """
⚠️
Data Not Available
Analysis data is not loaded or is empty. Please try refreshing the page.
""" empty_body_markdown_not_found = lambda _id: f"""
❌
Report Not Found
Report with ID '{_id}' was not found in the database.
""" if not selected_report_id: # When no report is selected, update both header and body return gr.update(value=empty_header_html), gr.update(value=empty_body_markdown_no_selection) agentic_df = current_token_state.get("bubble_agentic_analysis_data") if agentic_df is None or agentic_df.empty: # When no data is available, update both header and body return gr.update(value=empty_header_html), gr.update(value=empty_body_markdown_no_data) selected_report_series_df = agentic_df[agentic_df['_id'] == selected_report_id] if selected_report_series_df.empty: # When report is not found, update both header and body return gr.update(value=empty_header_html), gr.update(value=empty_body_markdown_not_found(selected_report_id)) selected_report_series = selected_report_series_df.iloc[0] # Call the format_report_for_display, which now returns a dict formatted_content_parts = format_report_for_display(selected_report_series) # Update the two separate Gradio components return ( gr.update(value=formatted_content_parts['header_html']), gr.update(value=formatted_content_parts['body_markdown']) ) with gr.Tabs() as tabs: # --- NEW HOME TAB --- with gr.TabItem("1️⃣ Home", id="tab_home"): # Call the new function from ui_generators to build the Home tab content btn_graphs, btn_reports, btn_okr, btn_help = build_home_tab_ui() # Link buttons to tab selection btn_graphs.click(fn=lambda: gr.update(selected="tab_analytics_module"), outputs=tabs) btn_reports.click(fn=lambda: gr.update(selected="tab_agentic_report"), outputs=tabs) btn_okr.click(fn=lambda: gr.update(selected="tab_agentic_okrs"), outputs=tabs) # btn_help.click(fn=lambda: gr.update(selected="tab_help"), outputs=tabs) # Uncomment if you add a help tab analytics_tab_instance.create_tab_ui() # This is the "Graphs" tab, assuming its ID is "tab_analytics" # --- REPLACED: Agentic Analysis Report Tab with enhanced UI --- # The create_enhanced_report_tab function now builds this entire tab's UI. # It also returns the relevant Gradio components needed for callbacks. with gr.TabItem("3️⃣ Agentic Analysis Report", id="tab_agentic_report", visible=AGENTIC_MODULES_LOADED): # The create_enhanced_report_tab function handles the CSS and HTML structure # MODIFIED: Unpacked 4 values instead of 3 agentic_pipeline_status_md, report_selector_dd, report_header_html_display, report_body_markdown_display = \ create_enhanced_report_tab(AGENTIC_MODULES_LOADED) with gr.TabItem("4️⃣ Agentic OKRs & Tasks", id="tab_agentic_okrs", visible=AGENTIC_MODULES_LOADED): gr.Markdown("## 🎯 AI Generated OKRs and Actionable Tasks (from Bubble.io)") gr.Markdown("Basato sull'analisi AI, l'agente ha proposto i seguenti OKR. Seleziona i Key Results per dettagli.") if not AGENTIC_MODULES_LOADED: gr.Markdown("🔴 **Error:** Agentic modules could not be loaded.") with gr.Row(): with gr.Column(scale=1): gr.Markdown("### Suggested Key Results") key_results_cbg = gr.CheckboxGroup(label="Select Key Results", choices=[], value=[], interactive=True) with gr.Column(scale=3): gr.Markdown("### Detailed OKRs and Tasks") okr_detail_display_md = gr.Markdown("I dettagli OKR appariranno qui.") def update_okr_display_on_selection(selected_kr_ids: list, raw_results: dict, all_krs: list): if not raw_results or not AGENTIC_MODULES_LOADED: return gr.update(value="Nessun dato di analisi caricato.") actionable_okrs = raw_results.get("actionable_okrs") if not actionable_okrs or not isinstance(actionable_okrs.get("okrs"), list): return gr.update(value="Nessun OKR trovato.") okrs_list, kr_id_map = actionable_okrs["okrs"], {kr['unique_kr_id']: (kr['okr_index'], kr['kr_index']) for kr in all_krs} selected_krs_by_okr_idx = defaultdict(list) if selected_kr_ids: for kr_id in selected_kr_ids: if kr_id in kr_id_map: okr_idx, kr_idx = kr_id_map[kr_id] selected_krs_by_okr_idx[okr_idx].append(kr_idx) output_parts = [] for okr_idx, okr in enumerate(okrs_list): if not selected_kr_ids: output_parts.append(format_single_okr_for_display(okr, okr_main_index=okr_idx)) elif okr_idx in selected_krs_by_okr_idx: accepted_indices = selected_krs_by_okr_idx.get(okr_idx) output_parts.append(format_single_okr_for_display(okr, accepted_kr_indices=accepted_indices, okr_main_index=okr_idx)) final_md = "\n\n---\n\n".join(output_parts) if output_parts else "Nessun OKR corrisponde alla selezione." return gr.update(value=final_md) if AGENTIC_MODULES_LOADED: key_results_cbg.change( fn=update_okr_display_on_selection, inputs=[key_results_cbg, orchestration_raw_results_st, key_results_for_selection_st], outputs=[okr_detail_display_md] ) if AGENTIC_MODULES_LOADED: report_selector_dd.change( fn=update_report_display, # This now calls the enhanced function # MODIFIED: Updated outputs to match the two new display components inputs=[report_selector_dd, token_state], outputs=[report_header_html_display, report_body_markdown_display], show_progress="minimal" ) # Ensure agentic_display_outputs correctly maps to the newly created components # This list must match the outputs of load_and_display_agentic_results agentic_display_outputs = [ agentic_pipeline_status_md, # 0: Status Markdown (hidden) report_selector_dd, # 1: Dropdown for selecting reports key_results_cbg, # 2: Checkbox group for OKRs okr_detail_display_md, # 3: Markdown for detailed OKR display orchestration_raw_results_st, # 4: Raw results state selected_key_result_ids_st, # 5: Selected KR IDs state key_results_for_selection_st, # 6: All KRs for selection state report_header_html_display, # 7: New HTML output for header report_body_markdown_display, # 8: New Markdown output for body reconstruction_cache_st # 9: Reconstruction cache state ] initial_load_event = org_urn_display.change( fn=initial_data_load_sequence, inputs=[url_user_token_display, org_urn_display, token_state], outputs=[status_box, token_state], show_progress="full" ) initial_load_event.then( fn=analytics_tab_instance._refresh_analytics_graphs_ui, inputs=[token_state, analytics_tab_instance.date_filter_selector, analytics_tab_instance.custom_start_date_picker, analytics_tab_instance.custom_end_date_picker, chat_histories_st], outputs=analytics_tab_instance.graph_refresh_outputs_list, show_progress="full" ).then( fn=load_and_display_agentic_results, inputs=[token_state, reconstruction_cache_st], # MODIFIED: Updated outputs to match all components returned by load_and_display_agentic_results outputs=agentic_display_outputs, show_progress="minimal" ) if __name__ == "__main__": if not os.environ.get(LINKEDIN_CLIENT_ID_ENV_VAR): logging.warning(f"WARNING: '{LINKEDIN_CLIENT_ID_ENV_VAR}' is not set.") if not all(os.environ.get(var) for var in [BUBBLE_APP_NAME_ENV_VAR, BUBBLE_API_KEY_PRIVATE_ENV_VAR, BUBBLE_API_ENDPOINT_ENV_VAR]): logging.warning("WARNING: One or more Bubble environment variables are not set.") if not AGENTIC_MODULES_LOADED: logging.warning("CRITICAL: Agentic modules failed to load.") if not os.environ.get("GEMINI_API_KEY"): logging.warning("WARNING: 'GEMINI_API_KEY' is not set.") app.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)), debug=True)