Linkedin_Data_API_Calls.py

import json
import requests
import html
import time # Added for potential rate limiting if needed
from datetime import datetime
from collections import defaultdict
from urllib.parse import quote # Added for URL encoding
from transformers import pipeline

from sessions import create_session
from error_handling import display_error
from posts_categorization import batch_summarize_and_classify
import logging


logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

API_V2_BASE = 'https://api.linkedin.com/v2'
API_REST_BASE = "https://api.linkedin.com/rest"

# Initialize sentiment pipeline (loaded once globally)
sentiment_pipeline = pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis")

# --- Utility Function ---
def extract_text_from_mention_commentary(commentary):
    """
    Extracts clean text from a commentary string, removing potential placeholders like {mention}.
    """
    import re 
    if not commentary:
        return ""
    return re.sub(r"{.*?}", "", commentary).strip()

# --- Core Sentiment Analysis Helper ---
def _get_sentiment_from_text(text_to_analyze):
    """
    Analyzes a single piece of text and returns its sentiment label and raw counts.
    Returns a dict: {"label": "Sentiment Label", "counts": defaultdict(int)}
    """
    sentiment_counts = defaultdict(int)
    dominant_sentiment_label = "Neutral 😐" # Default
    
    if not text_to_analyze or not text_to_analyze.strip():
        return {"label": dominant_sentiment_label, "counts": sentiment_counts}

    try:
        # Truncate to avoid issues with very long texts for the model
        analysis_result = sentiment_pipeline(str(text_to_analyze)[:512]) 
        label = analysis_result[0]['label'].upper()
        
        if label in ['POSITIVE', 'VERY POSITIVE']:
            dominant_sentiment_label = 'Positive 👍'
            sentiment_counts['Positive 👍'] += 1
        elif label in ['NEGATIVE', 'VERY NEGATIVE']:
            dominant_sentiment_label = 'Negative 👎'
            sentiment_counts['Negative 👎'] += 1
        elif label == 'NEUTRAL':
            dominant_sentiment_label = 'Neutral 😐' # Already default, but for clarity
            sentiment_counts['Neutral 😐'] += 1
        else:
            dominant_sentiment_label = 'Unknown' # Catch any other labels from the model
            sentiment_counts['Unknown'] += 1
        
    except Exception as e:
        # Log the error with more context if possible
        logging.error(f"Sentiment analysis failed for text snippet '{str(text_to_analyze)[:50]}...'. Error: {e}")
        sentiment_counts['Error'] += 1
        dominant_sentiment_label = "Error" # Indicate error in sentiment

    return {"label": dominant_sentiment_label, "counts": sentiment_counts}


# --- Post Retrieval Functions ---
def fetch_linkedin_posts_core(comm_client_id, community_token, org_urn, count):
    """
    Fetches raw posts, their basic statistics, and performs summarization/categorization.
    Does NOT fetch comments or analyze sentiment of comments here.
    """
    token_dict = community_token if isinstance(community_token, dict) else {'access_token': community_token, 'token_type': 'Bearer'}
    session = create_session(comm_client_id, token=token_dict)
    session.headers.update({
        "X-Restli-Protocol-Version": "2.0.0",
        "LinkedIn-Version": "202402" 
    })

    posts_url = f"{API_REST_BASE}/posts?author={org_urn}&q=author&count={count}&sortBy=LAST_MODIFIED"
    logging.info(f"Fetching posts from URL: {posts_url}")
    try:
        resp = session.get(posts_url)
        resp.raise_for_status()
        raw_posts_api = resp.json().get("elements", [])
        logging.info(f"Fetched {len(raw_posts_api)} raw posts from API.")
    except requests.exceptions.RequestException as e:
        status = getattr(e.response, 'status_code', 'N/A')
        text = getattr(e.response, 'text', 'No response text')
        logging.error(f"Failed to fetch posts (Status: {status}): {e}. Response: {text}")
        raise ValueError(f"Failed to fetch posts (Status: {status})") from e
    except json.JSONDecodeError as e:
        logging.error(f"Failed to decode JSON from posts response: {e}. Response text: {resp.text if resp else 'No response object'}")
        raise ValueError("Failed to decode JSON from posts response") from e

    if not raw_posts_api:
        logging.info("No raw posts found.")
        return [], {}, "DefaultOrgName" 

    post_urns_for_stats = [p["id"] for p in raw_posts_api if p.get("id")]

    post_texts_for_nlp = []
    for p in raw_posts_api:
        text_content = p.get("commentary") or \
                       p.get("specificContent", {}).get("com.linkedin.ugc.ShareContent", {}).get("shareCommentaryV2", {}).get("text", "") or \
                       "[No text content]"
        post_texts_for_nlp.append({"text": text_content, "id": p.get("id")})

    logging.info(f"Prepared {len(post_texts_for_nlp)} posts for NLP (summarization/classification).")
    if 'batch_summarize_and_classify' in globals():
        structured_results_list = batch_summarize_and_classify(post_texts_for_nlp)
    else: 
        logging.warning("batch_summarize_and_classify not found, using fallback.")
        structured_results_list = [{"id": p["id"], "summary": "N/A", "category": "N/A"} for p in post_texts_for_nlp]

    structured_results_map = {res["id"]: res for res in structured_results_list if "id" in res}

    stats_map = {}
    if post_urns_for_stats:
        batch_size_stats = 20 
        for i in range(0, len(post_urns_for_stats), batch_size_stats):
            batch_urns = post_urns_for_stats[i:i+batch_size_stats]
            params = {'q': 'organizationalEntity', 'organizationalEntity': org_urn}
            share_idx = 0
            ugc_idx = 0
            for urn_str in batch_urns:
                if ":share:" in urn_str:
                    params[f"shares[{share_idx}]"] = urn_str
                    share_idx += 1
                elif ":ugcPost:" in urn_str:
                    params[f"ugcPosts[{ugc_idx}]"] = urn_str
                    ugc_idx += 1
                else:
                    logging.warning(f"URN {urn_str} is not a recognized share or ugcPost type for stats. Skipping.")
                    continue
            
            if not share_idx and not ugc_idx: 
                continue

            try:
                logging.info(f"Fetching stats for batch of {len(batch_urns)} URNs starting with URN: {batch_urns[0]}")
                stat_resp = session.get(f"{API_REST_BASE}/organizationalEntityShareStatistics", params=params)
                stat_resp.raise_for_status()
                stats_data = stat_resp.json()
                for urn_key, stat_element_values in stats_data.get("results", {}).items(): 
                    stats_map[urn_key] = stat_element_values.get("totalShareStatistics", {})
                
                if stats_data.get("errors"):
                    for urn_errored, error_detail in stats_data.get("errors", {}).items():
                        logging.warning(f"Error fetching stats for URN {urn_errored}: {error_detail.get('message', 'Unknown error')}")

                logging.info(f"Successfully processed stats response for {len(batch_urns)} URNs. Current stats_map size: {len(stats_map)}")
            except requests.exceptions.RequestException as e:
                status_code = getattr(e.response, 'status_code', 'N/A')
                response_text = getattr(e.response, 'text', 'No response text')
                logging.warning(f"Failed to fetch stats for a batch (Status: {status_code}): {e}. Params: {params}. Response: {response_text}")
            except json.JSONDecodeError as e:
                logging.warning(f"Failed to decode JSON from stats response: {e}. Response: {stat_resp.text if stat_resp else 'No response text'}")

    processed_raw_posts = []
    for p in raw_posts_api:
        post_id = p.get("id")
        if not post_id:
            logging.warning("Skipping raw post due to missing ID.")
            continue

        text_content = p.get("commentary") or \
                       p.get("specificContent", {}).get("com.linkedin.ugc.ShareContent", {}).get("shareCommentaryV2", {}).get("text", "") or \
                       "[No text content]"
        
        timestamp = p.get("publishedAt") or p.get("createdAt") or p.get("firstPublishedAt") 
        published_at_iso = datetime.fromtimestamp(timestamp / 1000).isoformat() if timestamp else None
        
        structured_res = structured_results_map.get(post_id, {"summary": "N/A", "category": "N/A"})

        processed_raw_posts.append({
            "id": post_id,
            "raw_text": text_content,
            "summary": structured_res["summary"],
            "category": structured_res["category"],
            "published_at_timestamp": timestamp,
            "published_at_iso": published_at_iso,
            "organization_urn": p.get("author", f"urn:li:organization:{org_urn.split(':')[-1]}"), 
            "is_ad": 'adContext' in p,
            "media_category": p.get("content",{}).get("com.linkedin.voyager.feed.render.LinkedInVideoComponent",{}).get("mediaCategory") or \
                              p.get("content",{}).get("com.linkedin.voyager.feed.render.ImageComponent",{}).get("mediaCategory") or \
                              p.get("content",{}).get("com.linkedin.voyager.feed.render.ArticleComponent",{}).get("mediaCategory") or "NONE"
        })
    logging.info(f"Processed {len(processed_raw_posts)} posts with core data.")
    return processed_raw_posts, stats_map, "DefaultOrgName"


def fetch_comments(comm_client_id, community_token, post_urns, stats_map):
    """
    Fetches comments for a list of post URNs.
    Uses stats_map to potentially skip posts with 0 comments.
    """
    token_dict = community_token if isinstance(community_token, dict) else {'access_token': community_token, 'token_type': 'Bearer'}
    linkedin_session = create_session(comm_client_id, token=token_dict)
    linkedin_session.headers.update({
        'LinkedIn-Version': "202402", 
        "X-Restli-Protocol-Version": "2.0.0" 
        })
    
    all_comments_by_post = {}
    logging.info(f"Fetching comments for {len(post_urns)} posts.")

    for post_urn in post_urns:
        post_stats = stats_map.get(post_urn, {})
        comment_count_from_stats = post_stats.get("commentSummary", {}).get("totalComments", post_stats.get('commentCount', 0))

        if comment_count_from_stats == 0:
            logging.info(f"Skipping comment fetch for {post_urn} as commentCount is 0 in stats_map.")
            all_comments_by_post[post_urn] = []
            continue
        
        try:
            encoded_post_urn = quote(post_urn, safe='')
            url = f"{API_REST_BASE}/comments?q=entity&entityUrn={encoded_post_urn}&sortOrder=CHRONOLOGICAL"
            
            logging.debug(f"Fetching comments from URL: {url} for post URN: {post_urn}")
            response = linkedin_session.get(url)
            
            if response.status_code == 200:
                elements = response.json().get('elements', [])
                comments_texts = []
                for c in elements:
                    comment_text = c.get('message', {}).get('text')
                    if comment_text: 
                         comments_texts.append(comment_text)
                all_comments_by_post[post_urn] = comments_texts
                logging.info(f"Fetched {len(comments_texts)} comments for {post_urn}.")
            elif response.status_code == 403:
                 logging.warning(f"Forbidden (403) to fetch comments for {post_urn}. URL: {url}. Response: {response.text}. Check permissions or API version.")
                 all_comments_by_post[post_urn] = []
            elif response.status_code == 404:
                 logging.warning(f"Comments not found (404) for {post_urn}. URL: {url}. Response: {response.text}")
                 all_comments_by_post[post_urn] = []
            else:
                logging.error(f"Error fetching comments for {post_urn}. Status: {response.status_code}. URL: {url}. Response: {response.text}")
                all_comments_by_post[post_urn] = []
        except requests.exceptions.RequestException as e:
            logging.error(f"RequestException fetching comments for {post_urn}: {e}")
            all_comments_by_post[post_urn] = []
        except json.JSONDecodeError as e:
            logging.error(f"JSONDecodeError fetching comments for {post_urn}. Response: {response.text if 'response' in locals() else 'N/A'}. Error: {e}")
            all_comments_by_post[post_urn] = []
        except Exception as e:
            logging.error(f"Unexpected error fetching comments for {post_urn}: {e}")
            all_comments_by_post[post_urn] = []
            
    return all_comments_by_post

def analyze_sentiment(all_comments_data):
    """
    Analyzes sentiment for comments grouped by post_urn using the helper function.
    all_comments_data is a dict: {post_urn: [comment_text_1, comment_text_2,...]}
    Returns a dict: {post_urn: {"sentiment": "DominantOverallSentiment", "percentage": X.X, "details": {aggregated_counts}}}
    """
    results_by_post = {}
    logging.info(f"Analyzing aggregated sentiment for comments from {len(all_comments_data)} posts.")
    for post_urn, comments_list in all_comments_data.items():
        aggregated_sentiment_counts = defaultdict(int)
        total_valid_comments_for_post = 0
        
        if not comments_list:
            results_by_post[post_urn] = {"sentiment": "Neutral 😐", "percentage": 0.0, "details": dict(aggregated_sentiment_counts)}
            continue

        for comment_text in comments_list:
            if not comment_text or not comment_text.strip():
                continue
            
            # Use the helper for individual comment sentiment
            single_comment_sentiment = _get_sentiment_from_text(comment_text)
            
            # Aggregate counts
            for label, count in single_comment_sentiment["counts"].items():
                aggregated_sentiment_counts[label] += count
            
            if single_comment_sentiment["label"] != "Error": # Count valid analyses
                total_valid_comments_for_post +=1
        
        dominant_overall_sentiment = "Neutral 😐" # Default
        percentage = 0.0

        if total_valid_comments_for_post > 0:
            # Determine dominant sentiment from aggregated_sentiment_counts
            # Exclude 'Error' from being a dominant sentiment unless it's the only category with counts
            valid_sentiments = {k: v for k, v in aggregated_sentiment_counts.items() if k != 'Error' and v > 0}
            if not valid_sentiments:
                dominant_overall_sentiment = 'Error' if aggregated_sentiment_counts['Error'] > 0 else 'Neutral 😐'
            else:
                # Simple max count logic for dominance
                dominant_overall_sentiment = max(valid_sentiments, key=valid_sentiments.get)
            
            if dominant_overall_sentiment != 'Error':
                percentage = round((aggregated_sentiment_counts[dominant_overall_sentiment] / total_valid_comments_for_post) * 100, 1)
            else: # if dominant is 'Error' or only errors were found
                percentage = 0.0
        elif aggregated_sentiment_counts['Error'] > 0 : # No valid comments, but errors occurred
             dominant_overall_sentiment = 'Error'


        results_by_post[post_urn] = {
            "sentiment": dominant_overall_sentiment, 
            "percentage": percentage,
            "details": dict(aggregated_sentiment_counts) # Store aggregated counts
        }
        logging.debug(f"Aggregated sentiment for post {post_urn}: {results_by_post[post_urn]}")
            
    return results_by_post


def compile_detailed_posts(processed_raw_posts, stats_map, sentiments_per_post):
    """
    Combines processed raw post data with their statistics and overall comment sentiment.
    """
    detailed_post_list = []
    logging.info(f"Compiling detailed data for {len(processed_raw_posts)} posts.")
    for proc_post in processed_raw_posts:
        post_id = proc_post["id"]
        stats = stats_map.get(post_id, {})

        likes = stats.get("likeCount", 0)
        comments_stat_count = stats.get("commentSummary", {}).get("totalComments", stats.get("commentCount", 0))
        
        clicks = stats.get("clickCount", 0)
        shares = stats.get("shareCount", 0)
        impressions = stats.get("impressionCount", 0)
        unique_impressions = stats.get("uniqueImpressionsCount", stats.get("impressionCount", 0)) 

        engagement_numerator = likes + comments_stat_count + clicks + shares
        engagement_rate = (engagement_numerator / impressions * 100) if impressions and impressions > 0 else 0.0
        
        sentiment_info = sentiments_per_post.get(post_id, {"sentiment": "Neutral 😐", "percentage": 0.0, "details": {}})
        
        display_text = html.escape(proc_post["raw_text"][:250]).replace("\n", "<br>") + \
                       ("..." if len(proc_post["raw_text"]) > 250 else "")
        
        when_formatted = datetime.fromtimestamp(proc_post["published_at_timestamp"] / 1000).strftime("%Y-%m-%d %H:%M") \
            if proc_post["published_at_timestamp"] else "Unknown"

        detailed_post_list.append({
            "id": post_id,
            "when": when_formatted,
            "text_for_display": display_text,
            "raw_text": proc_post["raw_text"],
            "likes": likes,
            "comments_stat_count": comments_stat_count,
            "clicks": clicks,
            "shares": shares,
            "impressions": impressions,
            "uniqueImpressionsCount": unique_impressions,
            "engagement": f"{engagement_rate:.2f}%",
            "engagement_raw": engagement_rate,
            "sentiment": sentiment_info["sentiment"],
            "sentiment_percent": sentiment_info["percentage"],
            "sentiment_details": sentiment_info.get("details", {}),
            "summary": proc_post["summary"],
            "category": proc_post["category"],
            "organization_urn": proc_post["organization_urn"],
            "is_ad": proc_post["is_ad"],
            "media_category": proc_post.get("media_category", "NONE"),
            "published_at": proc_post["published_at_iso"]
        })
    logging.info(f"Compiled {len(detailed_post_list)} detailed posts.")
    return detailed_post_list


def prepare_data_for_bubble(detailed_posts, all_actual_comments_data):
    """
    Prepares data lists for uploading to Bubble.
    - detailed_posts: List of comprehensively compiled post objects.
    - all_actual_comments_data: Dict of {post_urn: [comment_texts]} from fetch_comments.
    """
    li_posts = []
    li_post_stats = []
    li_post_comments = []
    logging.info("Preparing posts data for Bubble.")

    if not detailed_posts:
        logging.warning("No detailed posts to prepare for Bubble.")
        return [], [], []

    org_urn_default = detailed_posts[0]["organization_urn"] if detailed_posts else "urn:li:organization:UNKNOWN"

    for post_data in detailed_posts:
        li_posts.append({
            "organization_urn": post_data["organization_urn"],
            "id": post_data["id"],
            "is_ad": post_data["is_ad"],
            "media_category": post_data.get("media_category", "NONE"),
            "published_at": post_data["published_at"],
            "sentiment": post_data["sentiment"], 
            "text": post_data["raw_text"],
            "summary_text": post_data["summary"], 
            "li_eb_label": post_data["category"] 
        })

        li_post_stats.append({
            "clickCount": post_data["clicks"],
            "commentCount": post_data["comments_stat_count"],
            "engagement": post_data["engagement_raw"], 
            "impressionCount": post_data["impressions"],
            "likeCount": post_data["likes"],
            "shareCount": post_data["shares"],
            "uniqueImpressionsCount": post_data["uniqueImpressionsCount"],
            "post_id": post_data["id"],
            "organization_urn": post_data["organization_urn"] 
        })

    for post_urn, comments_text_list in all_actual_comments_data.items():
        current_post_org_urn = org_urn_default 
        for p in detailed_posts: 
            if p["id"] == post_urn:
                current_post_org_urn = p["organization_urn"]
                break

        for single_comment_text in comments_text_list:
            if single_comment_text and single_comment_text.strip():
                li_post_comments.append({
                    "comment_text": single_comment_text,
                    "post_id": post_urn,
                    "organization_urn": current_post_org_urn
                })
    
    logging.info(f"Prepared {len(li_posts)} posts, {len(li_post_stats)} stats entries, and {len(li_post_comments)} comments for Bubble.")
    return li_posts, li_post_stats, li_post_comments

# --- Mentions Retrieval Functions ---

def fetch_linkedin_mentions_core(comm_client_id, community_token, org_urn, count=20):
    """
    Fetches raw mention notifications and the details of the posts where the organization was mentioned.
    Returns a list of processed mention data (internal structure).
    """
    token_dict = community_token if isinstance(community_token, dict) else {'access_token': community_token, 'token_type': 'Bearer'}
    session = create_session(comm_client_id, token=token_dict)
    session.headers.update({
        "X-Restli-Protocol-Version": "2.0.0",
        "LinkedIn-Version": "202502" 
    })

    encoded_org_urn = quote(org_urn, safe='')
    
    notifications_url_base = (
        f"{API_REST_BASE}/organizationalEntityNotifications"
        f"?q=criteria"
        f"&actions=List(SHARE_MENTION)" 
        f"&organizationalEntity={encoded_org_urn}"
        f"&count={count}" 
    )

    all_notifications = []
    start_index = 0
    processed_mentions_internal = [] 
    page_count = 0 
    max_pages = 10 

    while page_count < max_pages:
        current_url = f"{notifications_url_base}&start={start_index}"
        logging.info(f"Fetching notifications page {page_count + 1} from URL: {current_url}")
        try:
            resp = session.get(current_url)
            resp.raise_for_status()
            data = resp.json()
            elements = data.get("elements", [])
            
            if not elements: 
                logging.info(f"No more notifications found on page {page_count + 1}. Total notifications fetched: {len(all_notifications)}.")
                break 
            
            all_notifications.extend(elements)
            
            paging = data.get("paging", {})
            if 'start' not in paging or 'count' not in paging or len(elements) < paging.get('count', count):
                logging.info(f"Last page of notifications fetched. Total notifications: {len(all_notifications)}.")
                break

            start_index = paging['start'] + paging['count'] 
            page_count += 1

        except requests.exceptions.RequestException as e:
            status = getattr(e.response, 'status_code', 'N/A')
            text = getattr(e.response, 'text', 'No response text')
            logging.error(f"Failed to fetch notifications (Status: {status}): {e}. Response: {text}")
            break 
        except json.JSONDecodeError as e:
            logging.error(f"Failed to decode JSON from notifications response: {e}. Response: {resp.text if resp else 'No resp obj'}")
            break
        if page_count >= max_pages:
            logging.info(f"Reached max_pages ({max_pages}) for fetching notifications.")
            break

    if not all_notifications:
        logging.info("No mention notifications found after fetching.")
        return []

    mention_share_urns = list(set([ 
        n.get("generatedActivity") for n in all_notifications 
        if n.get("action") == "SHARE_MENTION" and n.get("generatedActivity")
    ]))
    
    logging.info(f"Found {len(mention_share_urns)} unique share URNs from SHARE_MENTION notifications.")

    for share_urn in mention_share_urns: 
        encoded_share_urn = quote(share_urn, safe='')
        post_detail_url = f"{API_REST_BASE}/posts/{encoded_share_urn}" 
        logging.info(f"Fetching details for mentioned post: {post_detail_url}")
        try:
            post_resp = session.get(post_detail_url)
            post_resp.raise_for_status()
            post_data = post_resp.json()

            commentary_raw = post_data.get("commentary") 
            if not commentary_raw and "specificContent" in post_data: 
                 share_content = post_data.get("specificContent", {}).get("com.linkedin.ugc.ShareContent", {})
                 commentary_raw = share_content.get("shareCommentaryV2", {}).get("text", "")

            if not commentary_raw:
                logging.warning(f"No commentary found for share URN {share_urn}. Skipping.")
                continue

            mention_text_cleaned = extract_text_from_mention_commentary(commentary_raw)
            timestamp = post_data.get("publishedAt") or post_data.get("createdAt") or post_data.get("firstPublishedAt")
            published_at_iso = datetime.fromtimestamp(timestamp / 1000).isoformat() if timestamp else None
            author_urn = post_data.get("author", "urn:li:unknown") 

            processed_mentions_internal.append({
                "mention_id": f"mention_{share_urn}", 
                "share_urn": share_urn, 
                "mention_text_raw": commentary_raw, 
                "mention_text_cleaned": mention_text_cleaned, 
                "published_at_timestamp": timestamp,
                "published_at_iso": published_at_iso, 
                "mentioned_by_author_urn": author_urn,
                "organization_urn_mentioned": org_urn 
            })
        except requests.exceptions.RequestException as e:
            status = getattr(e.response, 'status_code', 'N/A')
            text = getattr(e.response, 'text', 'No response text')
            logging.warning(f"Failed to fetch post details for share URN {share_urn} (Status: {status}): {e}. Response: {text}")
        except json.JSONDecodeError as e:
            logging.warning(f"Failed to decode JSON for post details {share_urn}: {e}. Response: {post_resp.text if post_resp else 'No resp obj'}")
            
    logging.info(f"Processed {len(processed_mentions_internal)} mentions with their post details.")
    return processed_mentions_internal


def analyze_mentions_sentiment(processed_mentions_list):
    """
    Analyzes sentiment for the text of each processed mention using the helper function.
    Input: list of processed_mention dicts (internal structure from fetch_linkedin_mentions_core).
    Returns: a dict {mention_id: {"sentiment_label": "DominantSentiment", "percentage": 100.0, "details": {counts}}}
    """
    mention_sentiments_map = {}
    logging.info(f"Analyzing individual sentiment for {len(processed_mentions_list)} mentions.")

    for mention_data in processed_mentions_list:
        mention_internal_id = mention_data["mention_id"] # Internal ID from fetch_linkedin_mentions_core
        text_to_analyze = mention_data.get("mention_text_cleaned", "")
        
        sentiment_result = _get_sentiment_from_text(text_to_analyze)
        
        # For single text, percentage is 100% for the dominant label if not error
        percentage = 0.0
        if sentiment_result["label"] != "Error" and any(sentiment_result["counts"].values()):
            percentage = 100.0

        mention_sentiments_map[mention_internal_id] = {
            "sentiment_label": sentiment_result["label"], # The dominant sentiment label
            "percentage": percentage, 
            "details": dict(sentiment_result["counts"]) # Raw counts for this specific mention
        }
        logging.debug(f"Individual sentiment for mention {mention_internal_id}: {mention_sentiments_map[mention_internal_id]}")
            
    return mention_sentiments_map


def compile_detailed_mentions(processed_mentions_list, mention_sentiments_map):
    """
    Combines processed mention data (internal structure) with their sentiment analysis 
    into the user-specified output format.
    processed_mentions_list: list of dicts from fetch_linkedin_mentions_core
    mention_sentiments_map: dict from analyze_mentions_sentiment, keyed by "mention_id" (internal)
                           and contains "sentiment_label".
    """
    detailed_mentions_output = []
    logging.info(f"Compiling detailed data for {len(processed_mentions_list)} mentions into specified format.")

    for mention_core_data in processed_mentions_list:
        mention_internal_id = mention_core_data["mention_id"] 
        sentiment_info = mention_sentiments_map.get(mention_internal_id, {"sentiment_label": "Neutral 😐"}) 
        
        date_formatted = "Unknown"
        if mention_core_data["published_at_timestamp"]:
            try:
                date_formatted = datetime.fromtimestamp(mention_core_data["published_at_timestamp"] / 1000).strftime("%Y-%m-%d %H:%M")
            except TypeError: 
                logging.warning(f"Could not format timestamp for mention_id {mention_internal_id}")

        detailed_mentions_output.append({
            "date": date_formatted, # User-specified field name
            "id": mention_core_data["share_urn"], # User-specified field name (URN of the post with mention)
            "mention_text": mention_core_data["mention_text_cleaned"], # User-specified field name
            "organization_urn": mention_core_data["organization_urn_mentioned"], # User-specified field name
            "sentiment_label": sentiment_info["sentiment_label"] # User-specified field name
        })
    logging.info(f"Compiled {len(detailed_mentions_output)} detailed mentions with specified fields.")
    return detailed_mentions_output


def prepare_mentions_for_bubble(compiled_detailed_mentions_list):
    """
    Prepares mention data for uploading to a Bubble table.
    The input `compiled_detailed_mentions_list` is already in the user-specified format:
    [{"date": ..., "id": ..., "mention_text": ..., "organization_urn": ..., "sentiment_label": ...}, ...]
    This function directly uses these fields as per user's selection for Bubble upload.
    """
    li_mentions_bubble = []
    logging.info(f"Preparing {len(compiled_detailed_mentions_list)} compiled mentions for Bubble upload.")

    if not compiled_detailed_mentions_list:
        return []

    for mention_data in compiled_detailed_mentions_list:
        # The mention_data dictionary already has the keys:
        # "date", "id", "mention_text", "organization_urn", "sentiment_label"
        # These are used directly for the Bubble upload list.
        li_mentions_bubble.append({
            "date": mention_data["date"],
            "id": mention_data["id"], 
            "mention_text": mention_data["mention_text"],
            "organization_urn": mention_data["organization_urn"],
            "sentiment_label": mention_data["sentiment_label"]
            # If Bubble table has different field names, mapping would be done here.
            # Example: "bubble_mention_date": mention_data["date"],
            # For now, using direct mapping as per user's selected code for the append.
        })
    
    logging.info(f"Prepared {len(li_mentions_bubble)} mention entries for Bubble, using direct field names from compiled data.")
    return li_mentions_bubble