services/report_data_handler.py

# services/report_data_handler.py
import pandas as pd
import logging
from apis.Bubble_API_Calls import fetch_linkedin_posts_data_from_bubble, bulk_upload_to_bubble
from config import (
    BUBBLE_REPORT_TABLE_NAME,
    BUBBLE_OKR_TABLE_NAME,
    BUBBLE_KEY_RESULTS_TABLE_NAME,
    BUBBLE_TASKS_TABLE_NAME,
    BUBBLE_KR_UPDATE_TABLE_NAME,
)
import json # For handling JSON data
from typing import List, Dict, Any, Optional, Tuple
from datetime import date

# It's good practice to configure the logger at the application entry point,
# but setting a default handler here prevents "No handler found" warnings.
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def fetch_latest_agentic_analysis(org_urn: str) -> Tuple[Optional[pd.DataFrame], Optional[str]]:
    """
    Fetches all agentic analysis data for a given org_urn from Bubble.
    Returns the full dataframe and any error message, or None, None.
    """
    logger.info(f"Starting fetch_latest_agentic_analysis for org_urn: {org_urn}")

    today = date.today()
    current_year = today.year
    current_quarter = (today.month - 1) // 3 + 1
    
    if not org_urn:
        logger.warning("fetch_latest_agentic_analysis: org_urn is missing.")
        return None, "org_urn is missing."

    additional_constraint = [
        {"key": 'quarter', "constraint_type": "equals", "value": current_quarter},
        {"key": 'year', "constraint_type": "equals", "value": current_year}
    ]
    try:
        report_data_df, error = fetch_linkedin_posts_data_from_bubble(
            data_type=BUBBLE_REPORT_TABLE_NAME,
            constraint_value=org_urn,
            constraint_key='organization_urn',
            constraint_type = 'equals'
        )

        if error:
            logger.error(f"Error fetching data from Bubble for org_urn {org_urn}: {error}")
            return None, str(error)

        if report_data_df is None or report_data_df.empty:
            logger.info(f"No existing agentic analysis found in Bubble for org_urn {org_urn}.")
            return None, None

        logger.info(f"Successfully fetched {len(report_data_df)} records for org_urn {org_urn}")
        return report_data_df, None  # Return full dataframe and no error

    except Exception as e:
        logger.exception(f"An unexpected error occurred in fetch_latest_agentic_analysis for org_urn {org_urn}: {e}")
        return None, str(e)


def save_report_results(
    org_urn: str,
    report_markdown: str,
    quarter: int,
    year: int,
    report_type: str,
) -> Optional[str]:
    """Saves the agentic pipeline results to Bubble. Returns the new record ID or None."""
    logger.info(f"Starting save_report_results for org_urn: {org_urn}")
    if not org_urn:
        logger.error("Cannot save agentic results: org_urn is missing.")
        return None

    try:
        payload = {
            "organization_urn": org_urn,
            "report_text": report_markdown if report_markdown else "N/A",
            "quarter": quarter,
            "year": year,
            "report_type": report_type,
        }
        logger.info(f"Attempting to save agentic analysis to Bubble for org_urn: {org_urn}")
        response = bulk_upload_to_bubble([payload], BUBBLE_REPORT_TABLE_NAME)

        # Handle API response which could be a list of dicts (for bulk) or a single dict.
        if response and isinstance(response, list) and len(response) > 0 and isinstance(response[0], dict) and 'id' in response[0]:
            record_id = response[0]['id'] # Get the ID from the first dictionary in the list
            logger.info(f"Successfully saved agentic analysis to Bubble. Record ID: {record_id}")
            return record_id
        elif response and isinstance(response, dict) and "id" in response: # Handle non-bulk response
            record_id = response["id"]
            logger.info(f"Successfully saved agentic analysis to Bubble. Record ID: {record_id}")
            return record_id
        else:
            # Catches None, False, empty lists, or other unexpected formats.
            logger.error(f"Failed to save agentic analysis to Bubble. Unexpected API Response: {response}")
            return None
            
    except Exception as e:
        logger.exception(f"An unexpected error occurred in save_report_results for org_urn {org_urn}: {e}")
        return None


# --- Data Saving Functions ---

def save_objectives(
    org_urn: str,
    report_id: str,
    objectives_data: List[Dict[str, Any]]
) -> Optional[List[str]]:
    """
    Saves Objective records to Bubble.
    Returns a list of the newly created Bubble record IDs for the objectives, or None on failure.
    """
    logger.info(f"Starting save_objectives for report_id: {report_id}")
    if not objectives_data:
        logger.info("No objectives to save.")
        return []

    try:
        payloads = []
        for obj in objectives_data:
            timeline = obj.get("objective_timeline")
            payloads.append({
                "description": obj.get("objective_description"),
                # FIX: Convert Enum to its value before sending.
                "timeline": timeline.value if hasattr(timeline, 'value') else timeline,
                "owner": obj.get("objective_owner"),
                "report": report_id,
            })

        logger.info(f"Attempting to save {payloads} objectives for report_id: {report_id}")
        response_data = bulk_upload_to_bubble(payloads, BUBBLE_OKR_TABLE_NAME)

        # Validate response and extract IDs from the list of dictionaries.
        if not response_data or not isinstance(response_data, list):
            logger.error(f"Failed to save objectives. API response was not a list: {response_data}")
            return None
        
        try:
            # Extract the ID from each dictionary in the response list.
            extracted_ids = [item['id'] for item in response_data]
        except (TypeError, KeyError):
            logger.error(f"Failed to parse IDs from API response. Response format invalid: {response_data}", exc_info=True)
            return None

        # Check if we extracted the expected number of IDs
        if len(extracted_ids) != len(payloads):
            logger.error(f"Failed to save all objectives for report_id: {report_id}. "
                         f"Expected {len(payloads)} IDs, but got {len(extracted_ids)} from response: {response_data}")
            return None

        logger.info(f"Successfully saved {len(extracted_ids)} objectives.")
        return extracted_ids
        
    except Exception as e:
        logger.exception(f"An unexpected error occurred in save_objectives for report_id {report_id}: {e}")
        return None


def save_key_results(
    org_urn: str,
    objectives_with_ids: List[Tuple[Dict[str, Any], str]]
) -> Optional[List[Tuple[Dict[str, Any], str]]]:
    """
    Saves Key Result records to Bubble, linking them to their parent objectives.
    Returns a list of tuples containing the original key result data and its new Bubble ID, or None on failure.
    """
    logger.info(f"Starting save_key_results for {len(objectives_with_ids)} objectives.")
    key_result_payloads = []
    # This list preserves the original KR data in the correct order to match the returned IDs
    key_results_to_process = []
    
    if not objectives_with_ids:
        logger.info("No objectives provided to save_key_results.")
        return []

    try:
        for objective_data, parent_objective_id in objectives_with_ids:
            # Defensive check to ensure the parent_objective_id is a valid-looking string.
            if not isinstance(parent_objective_id, str) or not parent_objective_id:
                 logger.error(f"Invalid parent_objective_id found: '{parent_objective_id}'. Skipping KRs for this objective.")
                 continue # Skip this loop iteration

            for kr in objective_data.get("key_results", []):
                kr_type = kr.get("key_result_type")
                key_results_to_process.append(kr)
                key_result_payloads.append({
                    "okr": parent_objective_id,
                    "description": kr.get("key_result_description"),
                    "target_metric": kr.get("target_metric"),
                    "target_value": kr.get("target_value"),
                    # FIX: Convert Enum to its value before sending.
                    "kr_type": kr_type.value if hasattr(kr_type, 'value') else kr_type,
                    "data_subject": kr.get("data_subject"),
                })

        if not key_result_payloads:
            logger.info("No key results to save.")
            return []

        logger.info(f"Attempting to save {key_result_payloads} key results for org_urn: {org_urn}")
        response_data = bulk_upload_to_bubble(key_result_payloads, BUBBLE_KEY_RESULTS_TABLE_NAME)

        # Validate response and extract IDs.
        if not response_data or not isinstance(response_data, list):
            logger.error(f"Failed to save key results. API response was not a list: {response_data}")
            return None
        
        try:
            extracted_ids = [item['id'] for item in response_data]
        except (TypeError, KeyError):
            logger.error(f"Failed to parse IDs from key result API response: {response_data}", exc_info=True)
            return None

        if len(extracted_ids) != len(key_result_payloads):
            logger.error(f"Failed to save all key results for org_urn: {org_urn}. "
                         f"Expected {len(key_result_payloads)} IDs, but got {len(extracted_ids)} from response: {response_data}")
            return None

        logger.info(f"Successfully saved {len(extracted_ids)} key results.")
        return list(zip(key_results_to_process, extracted_ids))

    except Exception as e:
        logger.exception(f"An unexpected error occurred in save_key_results for org_urn {org_urn}: {e}")
        return None


def save_tasks(
    org_urn: str,
    key_results_with_ids: List[Tuple[Dict[str, Any], str]]
) -> Optional[List[str]]:
    """
    Saves Task records to Bubble, linking them to their parent key results.
    Returns a list of the newly created Bubble record IDs for the tasks, or None on failure.
    """
    logger.info(f"Starting save_tasks for {len(key_results_with_ids)} key results.")
    if not key_results_with_ids:
        logger.info("No key results provided to save_tasks.")
        return []
        
    try:
        task_payloads = []
        for key_result_data, parent_key_result_id in key_results_with_ids:
            for task in key_result_data.get("tasks", []):
                priority = task.get("priority")
                effort = task.get("effort")
                timeline = task.get("timeline")
                task_payloads.append({
                    "key_result": parent_key_result_id,
                    "description": task.get("task_description"),
                    "deliverable": task.get("objective_deliverable"),
                    "category": task.get("task_category"),
                    # FIX: Convert Enum to its value before sending.
                    "priority": priority.value if hasattr(priority, 'value') else priority,
                    "priority_justification": task.get("priority_justification"),
                    "effort": effort.value if hasattr(effort, 'value') else effort,
                    "timeline": timeline.value if hasattr(timeline, 'value') else timeline,
                    "responsible_party": task.get("responsible_party"),
                    "success_criteria_metrics": task.get("success_criteria_metrics"),
                    "dependencies": task.get("dependencies_prerequisites"),
                    "why": task.get("why_proposed"),
                })


        if not task_payloads:
            logger.info("No tasks to save.")
            return []

        logger.info(f"Attempting to save {task_payloads} tasks for org_urn: {org_urn}")
        response_data = bulk_upload_to_bubble(task_payloads, BUBBLE_TASKS_TABLE_NAME)

        # Validate response and extract IDs.
        if not response_data or not isinstance(response_data, list):
            logger.error(f"Failed to save tasks. API response was not a list: {response_data}")
            return None
        
        try:
            extracted_ids = [item['id'] for item in response_data]
        except (TypeError, KeyError):
            logger.error(f"Failed to parse IDs from task API response: {response_data}", exc_info=True)
            return None

        if len(extracted_ids) != len(task_payloads):
            logger.error(f"Failed to save all tasks for org_urn: {org_urn}. "
                         f"Expected {len(task_payloads)} IDs, but got {len(extracted_ids)} from response: {response_data}")
            return None

        logger.info(f"Successfully saved {len(extracted_ids)} tasks.")
        return extracted_ids

    except Exception as e:
        logger.exception(f"An unexpected error occurred in save_tasks for org_urn {org_urn}: {e}")
        return None


# --- Orchestrator Function ---

def save_actionable_okrs(org_urn: str, actionable_okrs: Dict[str, Any], report_id: str):
    """
    Orchestrates the sequential saving of objectives, key results, and tasks.
    """
    logger.info(f"--- Starting OKR save process for org_urn: {org_urn}, report_id: {report_id} ---")
    
    try:
        objectives_data = actionable_okrs.get("okrs", [])

        # Defensive check: If data is a string, try to parse it as JSON.
        if isinstance(objectives_data, str):
            logger.warning("The 'okrs' data is a string. Attempting to parse as JSON.")
            try:
                objectives_data = json.loads(objectives_data)
                logger.info("Successfully parsed 'okrs' data from JSON string.")
            except json.JSONDecodeError:
                logger.error("Failed to parse 'okrs' data. The string is not valid JSON.", exc_info=True)
                return # Abort if data is malformed

        if not objectives_data:
            logger.warning(f"No OKRs found in the input for org_urn: {org_urn}. Aborting save process.")
            return

        # Step 1: Save the top-level objectives
        objective_ids = save_objectives(org_urn, report_id, objectives_data)
        if objective_ids is None:
            logger.error("OKR save process aborted due to failure in saving objectives.")
            return

        # Combine the original objective data with their new IDs for the next step
        objectives_with_ids = list(zip(objectives_data, objective_ids))

        # Step 2: Save the key results, linking them to the objectives
        key_results_with_ids = save_key_results(org_urn, objectives_with_ids)
        if key_results_with_ids is None:
            logger.error("OKR save process aborted due to failure in saving key results.")
            return

        # Step 3: Save the tasks, linking them to the key results
        task_ids = save_tasks(org_urn, key_results_with_ids)
        if task_ids is None:
            logger.error("Task saving failed, but objectives and key results were saved.")
            # For now, we just log the error and complete.
            return

        logger.info(f"--- OKR save process completed successfully for org_urn: {org_urn} ---")
        
    except Exception as e:
        logger.exception(f"An unhandled exception occurred during the save_actionable_okrs orchestration for org_urn {org_urn}: {e}")


def fetch_and_reconstruct_data_from_bubble(report_df: pd.DataFrame) -> Optional[Dict[str, Any]]:
    """
    Fetches the latest report, OKRs, Key Results, and Tasks from Bubble for a given organization
    and reconstructs them into the nested structure expected by the application.

    Args:
        org_urn: The URN of the organization.

    Returns:
        A dictionary containing the reconstructed data ('report_str', 'actionable_okrs', etc.)
        or None if the report is not found or an error occurs.
    """
    # logger.info(f"Starting data fetch and reconstruction for org_urn: {org_urn}")
    # try:
    #     # 1. Fetch the latest report for the organization
    #     # We add a sort field to get the most recent one. 
    #     report_df, error = fetch_linkedin_posts_data_from_bubble(
    #         data_type=BUBBLE_REPORT_TABLE_NAME,
    #         org_urn=org_urn,
    #         constraint_key="organization_urn"
    #     )

    #     if error or report_df is None or report_df.empty:
    #         logger.error(f"Could not fetch latest report for org_urn {org_urn}. Error: {error}")
    #         return None

    logger.info(f"Starting data fetch and reconstruction")
    try:
        # Get the most recent report (assuming the first one is the latest)
        latest_report = report_df.iloc[0]
        report_id = latest_report.get('_id')
        if not report_id:
            logger.error("Fetched report is missing a Bubble '_id'.")
            return None
        
        logger.info(f"Fetched latest report with ID: {report_id}")

        # 2. Fetch all related OKRs using the report_id
        okrs_df, error = fetch_linkedin_posts_data_from_bubble(
            data_type=BUBBLE_OKR_TABLE_NAME,
            constraint_value=str(report_id),
            constraint_key='report',
            constraint_type = 'equals'
        )
        if error:
            logger.error(f"Error fetching OKRs for report_id {report_id}: {error}")
            okrs_df = pd.DataFrame()

            
        logger.info(f" okr_df {okrs_df}")
        # 3. Fetch all related Key Results using the OKR IDs
        okr_ids = okrs_df['_id'].tolist() if not okrs_df.empty else []
        logger.info(f" retrieved {len(okr_ids)} okr ID: {okr_ids}")
        krs_df = pd.DataFrame()
        if okr_ids:
            krs_df, error = fetch_linkedin_posts_data_from_bubble(
                data_type=BUBBLE_KEY_RESULTS_TABLE_NAME,
                constraint_value=okr_ids,
                constraint_key='okr',
                constraint_type='in'
            )
            if error:
                logger.error(f"Error fetching Key Results for OKR IDs {okr_ids}: {error}")
                krs_df = pd.DataFrame()

        # 4. Fetch all related Tasks using the Key Result IDs
        kr_ids = krs_df['_id'].tolist() if not krs_df.empty else []
        tasks_df = pd.DataFrame()
        if kr_ids:
            tasks_df, error = fetch_linkedin_posts_data_from_bubble(
                data_type=BUBBLE_TASKS_TABLE_NAME,
                constraint_value=kr_ids,
                constraint_key='key_result',
                constraint_type='in'
            )
            if error:
                logger.error(f"Error fetching Tasks for KR IDs {kr_ids}: {error}")
                tasks_df = pd.DataFrame()

        # 5. Reconstruct the nested 'actionable_okrs' dictionary
        tasks_by_kr_id = tasks_df.groupby('key_result').apply(lambda x: x.to_dict('records')).to_dict()
        krs_by_okr_id = krs_df.groupby('okr').apply(lambda x: x.to_dict('records')).to_dict()

        reconstructed_okrs = []
        for okr_data in okrs_df.to_dict('records'):
            okr_id = okr_data['_id']
            key_results_list = krs_by_okr_id.get(okr_id, [])
            
            for kr_data in key_results_list:
                kr_id = kr_data['_id']
                # Attach tasks to each key result
                kr_data['tasks'] = tasks_by_kr_id.get(kr_id, [])
            
            # Attach key results to the objective
            okr_data['key_results'] = key_results_list
            reconstructed_okrs.append(okr_data)
            
        actionable_okrs = {"okrs": reconstructed_okrs}

        return {
            "report_str": latest_report.get("report_text", "Nessun report trovato."),
            "quarter": latest_report.get("quarter"),
            "year": latest_report.get("year"),
            "actionable_okrs": actionable_okrs,
            "report_id": report_id
        }

    except Exception as e:
        logger.exception(f"An unexpected error occurred during data reconstruction for org_urn {org_urn}: {e}")
        return None