Create report_data_handler.py

services/report_data_handler.py

@@ -0,0 +1,83 @@
+# 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
+
+logger = logging.getLogger(__name__)
+
+def fetch_latest_agentic_analysis(org_urn: str):
+    """
+    Fetches the most recent agentic analysis for a given org_urn from Bubble.
+    Returns the analysis data (dict) and its timestamp, or None, None.
+    """
+    if not org_urn:
+        logger.warning("fetch_latest_agentic_analysis: org_urn is missing.")
+        return None
+
+    # Simpler: fetch all from this table (if it's not too large) or filter by org_urn
+    # This example assumes you might fetch more and then filter.
+    report_data_df, error = fetch_linkedin_posts_data_from_bubble(
+        data_type=BUBBLE_REPORT_TABLE_NAME,
+        org_urn=org_urn # Or use this if your function supports it for filtering
+    )
+
+    if error or 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} or error: {error}")
+        return None
+
+    # Filter for the specific org_urn if not done by the fetch call
+    quarter_report_data_df = report_data_df[report_data_df['type'] == 'Quarter'].copy()
+
+    if quarter_report_data_df.empty:
+        logger.info(f"No agentic analysis found for org_urn {org_urn} after filtering.")
+        return None
+
+    latest_analysis = quarter_report_data_df.sort_values(by='Created Date', ascending=False).iloc[0]
+
+    logger.info(f"Latest agentic analysis found for org_urn {org_urn}}")
+    return latest_report
+
+
+def save_agentic_analysis_results(
+    org_urn: str,
+    report_markdown: str,
+    orchestration_raw_results: dict, # This is the full output from agentic pipeline
+    # key_results_for_selection: list, # Pass this too if you want to save it
+    generation_timestamp: pd.Timestamp
+):
+    """Saves the agentic pipeline results to Bubble."""
+    if not org_urn:
+        logger.error("Cannot save agentic results: org_urn missing.")
+        return False
+
+    # Extract what you need from orchestration_raw_results
+    actionable_okrs_dict = orchestration_raw_results.get("actionable_okrs_and_tasks", {})
+    # Assuming key_results_for_selection is also part of orchestration_raw_results or passed separately
+    key_results_selection_list = orchestration_raw_results.get("key_results_for_selection", [])
+
+
+    payload = {
+        "org_urn": org_urn,
+        BUBBLE_AGENTIC_TIMESTAMP_COLUMN: generation_timestamp.isoformat(), # Store as ISO string
+        "report_markdown": report_markdown if report_markdown else "N/A",
+        "okrs_json": json.dumps(actionable_okrs_dict) if actionable_okrs_dict else json.dumps({}),
+        "key_results_selection_json": json.dumps(key_results_selection_list) if key_results_selection_list else json.dumps([]),
+        # Add other fields as needed
+    }
+    logger.info(f"Attempting to save agentic analysis to Bubble for org_urn: {org_urn}")
+    success_ids = bulk_upload_to_bubble([payload], BUBBLE_AGENTIC_RESULTS_TABLE_NAME)
+
+    if success_ids and success_ids[0]: # bulk_upload_to_bubble returns list of IDs or False
+        logger.info(f"Successfully saved agentic analysis to Bubble. Record ID: {success_ids[0]}")
+        return True
+    else:
+        logger.error("Failed to save agentic analysis to Bubble.")
+        return False