run_agentic_pipeline.py

# run_agentic_pipeline.py
import asyncio
import os
import json
import logging
from datetime import datetime
import pandas as pd
from typing import Dict, Any, Optional

# 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)


# Imports from your project structure
from 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 .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 

logger = logging.getLogger(__name__)

# GOOGLE_API_KEY should be set in the environment where app.py runs
GOOGLE_API_KEY = os.environ.get("GEMINI_API_KEY")

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 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 None

    logger.info("Starting full analytics orchestration process...")

    # 1. Prepare and filter data
    try:
        (
            filtered_posts_df,
            filtered_mentions_df,
            _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
        )
        logger.info(f"Data prepared: Posts({len(filtered_posts_df)}), Mentions({len(filtered_mentions_df)}), FollowerStatsRaw({len(raw_follower_stats_df)})")

    except Exception as e:
        logger.error(f"Error during data preparation: {e}", exc_info=True)
        return None

    # 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
    try:
        # 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, # Pass None to use orchestrator's default
            current_date_for_tasks=datetime.utcnow().date()
        )

        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
        )
        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 None

# Example of how app.py might call this (for testing this module standalone)
if __name__ == "__main__":
    # This block is for testing `run_analytics_pipeline.py` directly.
    # In a real scenario, `app.py` would import and call `run_full_analytics_orchestration`.
    
    # Ensure logging is set up for standalone test
    from insight_and_tasks.utils.logging_config import setup_logging
    setup_logging()

    if not GOOGLE_API_KEY:
        print("Please set the GOOGLE_API_KEY environment variable to run this test.")
    else:
        # Create mock token_state and filter parameters
        mock_token_state = {
            "bubble_posts_df": pd.DataFrame({
                'id': [1, 2, 3], 'published_at': pd.to_datetime(['2023-01-01', '2023-07-15', '2023-12-31']),
                'li_eb_label': ['A', 'B', 'A'], 'media_type': ['X', 'Y', 'X'], 'is_ad': [False, True, False],
                'sentiment': ['pos', 'neg', 'neu'], 'engagement': [10,5,8], 'impressionCount': [100,50,80],
                'clickCount': [1,2,3], 'likeCount': [8,3,6], 'commentCount': [1,1,1], 'shareCount': [1,1,1]
            }),
            "bubble_post_stats_df": pd.DataFrame({'post_id': [1, 2, 3]}), # Simplified
            "bubble_mentions_df": pd.DataFrame({
                'date': pd.to_datetime(['2023-06-01', '2023-08-20']), 'sentiment_label': ['Positive 👍', 'Negative 👎']
            }),
            "bubble_follower_stats_df": pd.DataFrame({
                'category_name': ['2023-01-01', 'Industry A', '2023-02-01'], 
                'follower_count_organic': [100, 500, 120],
                'follower_count_paid': [10, 50, 20],
                'follower_count_type': ['follower_gains_monthly', 'follower_industry', 'follower_gains_monthly']
            }),
            "config_date_col_posts": "published_at",
            "config_date_col_mentions": "date",
        }
        mock_date_filter = "Ultimi 365 Giorni" # Example, adjust prepare_filtered_analytics_data if new options
        # For "Ultimi 365 Giorni", prepare_filtered_analytics_data needs to handle it or be updated.
        # Let's use a known filter for testing:
        mock_date_filter = "Sempre"
        mock_custom_start = None
        mock_custom_end = None

        logger.info("Running standalone test of run_full_analytics_orchestration...")
        
        async def test_run():
            results = await run_full_analytics_orchestration(
                mock_token_state, mock_date_filter, mock_custom_start, mock_custom_end
            )

            if results:
                print("\n\n" + "="*30 + " MOCK ORCHESTRATION RESULTS " + "="*30)
                print("\n--- Comprehensive Analysis Report ---")
                print(results.get("comprehensive_analysis_report", "N/A"))
                
                print("\n--- Actionable OKRs and Tasks ---")
                okrs_data = results.get("actionable_okrs_and_tasks")
                if okrs_data:
                    print(json.dumps(okrs_data, indent=2))
                else:
                    print("N/A")
                
                # Optionally print detailed metrics if needed for debugging
                # print("\n--- Detailed Agent Metrics ---")
                # print(json.dumps(results.get("detailed_metrics"), indent=2, default=str))
            else:
                print("Standalone test: Orchestration returned no results or failed.")

        asyncio.run(test_run())