import json
import requests
import html
from datetime import datetime
from collections import defaultdict
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" # Corrected from API_REST_BASE to API_REST_BASE

# Initialize sentiment pipeline (consider loading it once globally if this module is imported multiple times)
sentiment_pipeline = pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis")

def fetch_linkedin_posts_core(comm_client_id, community_token, org_urn, count=20):
    """
    Fetches raw posts, their basic statistics, and performs summarization/categorization.
    Does NOT fetch comments or analyze sentiment.
    """
    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)
    org_name = "GRLS" # Placeholder or fetch if necessary

    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')
        logging.error(f"Failed to fetch posts (Status: {status}): {e}")
        raise ValueError(f"Failed to fetch posts (Status: {status})") from e

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

    # Filter for valid post types if necessary, e.g., shares or ugcPosts
    # post_urns_for_stats = [p["id"] for p in raw_posts_api if ":share:" in p["id"] or ":ugcPost:" in p["id"]]
    post_urns_for_stats = [p["id"] for p in raw_posts_api if p.get("id")]


    # Prepare texts for summarization/classification
    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.")
    structured_results_list = batch_summarize_and_classify(post_texts_for_nlp)
    # Create a dictionary for easy lookup of structured results by post ID
    structured_results_map = {res["id"]: res for res in structured_results_list if "id" in res}


    # Fetch statistics
    stats_map = {}
    if post_urns_for_stats:
        for i in range(0, len(post_urns_for_stats), 20): # LinkedIn API often has batch limits
            batch_urns = post_urns_for_stats[i:i+20]
            params = {'q': 'organizationalEntity', 'organizationalEntity': org_urn}
            for idx, urn_str in enumerate(batch_urns):
                # Determine if it's a share or ugcPost based on URN structure (simplified)
                key_prefix = "shares" if ":share:" in urn_str else "ugcPosts"
                params[f"{key_prefix}[{idx}]"] = urn_str
            
            try:
                logging.info(f"Fetching stats for batch starting with URN: {batch_urns[0]}")
                stat_resp = session.get(f"{API_REST_BASE}/organizationalEntityShareStatistics", params=params)
                stat_resp.raise_for_status()
                for stat_element in stat_resp.json().get("elements", []):
                    urn = stat_element.get("share") or stat_element.get("ugcPost")
                    if urn:
                        stats_map[urn] = stat_element.get("totalShareStatistics", {})
                logging.info(f"Successfully fetched stats for {len(batch_urns)} URNs. Current stats_map size: {len(stats_map)}")
            except requests.exceptions.RequestException as e:
                logging.warning(f"Failed to fetch stats for a batch: {e}. Response: {e.response.text if e.response else 'No response'}")
                # Continue to next batch, some stats might be missing
            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")
        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,
            # These are placeholders for actual fields from LinkedIn API response. Verify field names.
            "organization_urn": p.get("author", "urn:li:unknown"), # e.g., "urn:li:person:xxxx" or "urn:li:organization:xxxx"
            "is_ad": p.get("is_ad", False) # LinkedIn might use a different field like 'sponsored' or 'promoted'
            #"media_type": p.get("mediaCategory", "NONE") # e.g., ARTICLE, IMAGE, VIDEO, NONE
        })
    logging.info(f"Processed {len(processed_raw_posts)} posts with core data.")
    return processed_raw_posts, stats_map, org_name


def fetch_comments(comm_client_id, token_dict, post_urns, stats_map):
    """
    Fetches comments for a list of post URNs.
    Uses stats_map to potentially skip posts with 0 comments.
    """
    from requests_oauthlib import OAuth2Session # Keep import here if OAuth2Session is specific to this
    
    linkedin_session = OAuth2Session(comm_client_id, token=token_dict)
    # LinkedIn API versions can change, ensure this is up-to-date.
    # Using a recent version like "202402" or as per current LinkedIn docs.
    # The user had "202502", which might be a future version. Using a slightly older one for safety.
    linkedin_session.headers.update({'LinkedIn-Version': "202502"})
    
    all_comments_by_post = {}
    logging.info(f"Fetching comments for {len(post_urns)} posts.")

    for post_urn in post_urns:
        # Optimization: if stats show 0 comments, skip API call for this post's comments
        if stats_map.get(post_urn, {}).get('commentCount', 0) == 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:
            # According to LinkedIn docs, comments are often under /socialActions/{activityUrn}/comments
            # or /commentsV2?q=entity&entity={activityUrn}
            # The user's URL was /socialActions/{post_urn}/comments - this seems plausible for URNs like ugcPost URNs.
            url = f"{API_REST_BASE}/socialActions/{post_urn}/comments"
            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 = [
                    c.get('message', {}).get('text') 
                    for c in elements 
                    if c.get('message') and c.get('message', {}).get('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: # Forbidden, often permissions or versioning
                 logging.warning(f"Forbidden (403) to fetch comments for {post_urn}. URL: {url}. Response: {response.text}")
                 all_comments_by_post[post_urn] = []
            elif response.status_code == 404: # Not found
                 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}. 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 Exception as e: # Catch any other unexpected errors
            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.
    all_comments_data is a dict: {post_urn: [comment_text_1, comment_text_2,...]}
    Returns a dict: {post_urn: {"sentiment": "DominantSentiment", "percentage": X.X}}
    """
    results_by_post = {}
    logging.info(f"Analyzing sentiment for comments from {len(all_comments_data)} posts.")
    for post_urn, comments_list in all_comments_data.items():
        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": sentiment_counts}
            continue

        for comment_text in comments_list:
            if not comment_text or not comment_text.strip(): # Skip empty comments
                continue
            try:
                # The pipeline expects a string or list of strings.
                # Ensure comment_text is a string.
                analysis_result = sentiment_pipeline(str(comment_text))
                label = analysis_result[0]['label'].upper()
                
                if label in ['POSITIVE', 'VERY POSITIVE']:
                    sentiment_counts['Positive 👍'] += 1
                elif label in ['NEGATIVE', 'VERY NEGATIVE']:
                    sentiment_counts['Negative 👎'] += 1
                elif label == 'NEUTRAL':
                    sentiment_counts['Neutral 😐'] += 1
                else: # Other labels from the model
                    sentiment_counts['Unknown'] += 1
                total_valid_comments_for_post += 1
            except Exception as e:
                logging.error(f"Sentiment analysis failed for comment under {post_urn}: '{comment_text[:50]}...'. Error: {e}")
                sentiment_counts['Error'] += 1
        
        if total_valid_comments_for_post > 0:
            dominant_sentiment = max(sentiment_counts, key=sentiment_counts.get, default='Neutral 😐')
            percentage = round((sentiment_counts[dominant_sentiment] / total_valid_comments_for_post) * 100, 1)
        else: # No valid comments to analyze
            dominant_sentiment = 'Neutral 😐'
            percentage = 0.0
            if sentiment_counts['Error'] > 0 : # If there were only errors
                 dominant_sentiment = 'Error'

        results_by_post[post_urn] = {
            "sentiment": dominant_sentiment, 
            "percentage": percentage,
            "details": dict(sentiment_counts) # Store counts for more detailed reporting if needed
        }
        logging.debug(f"Sentiment for {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 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)
        # Use 'commentSummary' from stats for comment count if available, else 'commentCount'
        # LinkedIn sometimes has commentSummary.totalComments
        comments_stat_count = stats.get("commentSummary", {}).get("totalComments") if "commentSummary" in stats else stats.get("commentCount", 0)
        
        clicks = stats.get("clickCount", 0)
        shares = stats.get("shareCount", 0)
        impressions = stats.get("impressionCount", 0)
        unique_impressions = stats.get("uniqueImpressionsCount", 0) # Ensure this field is in API response

        # Calculate engagement: (likes + comments + clicks + shares) / impressions
        # Ensure impressions is not zero to avoid DivisionByZeroError
        engagement_numerator = likes + comments_stat_count + clicks + shares
        engagement_rate = (engagement_numerator / impressions * 100) if impressions else 0.0
        
        sentiment_info = sentiments_per_post.get(post_id, {"sentiment": "Neutral 😐", "percentage": 0.0})
        
        # Format text for display (escaped and truncated)
        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, # Shortened, escaped text
            "raw_text": proc_post["raw_text"], # Full original text
            "likes": likes,
            "comments_stat_count": comments_stat_count, # Count from post statistics
            "clicks": clicks,
            "shares": shares,
            "impressions": impressions,
            "uniqueImpressionsCount": unique_impressions,
            "engagement": f"{engagement_rate:.2f}%", # Formatted string
            "engagement_raw": engagement_rate, # Raw float for potential calculations
            "sentiment": sentiment_info["sentiment"],
            "sentiment_percent": sentiment_info["percentage"],
            "sentiment_details": sentiment_info.get("details", {}), # Detailed counts
            "summary": proc_post["summary"],
            "category": proc_post["category"],
            "organization_urn": proc_post["organization_urn"],
            "is_ad": proc_post["is_ad"],
            #"media_type": proc_post["media_type"],
            "published_at": proc_post["published_at_iso"] # ISO format datetime string
        })
    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 = [] # For individual comments
    logging.info("Preparing data for Bubble.")
    org_urn = detailed_posts[0]["organization_urn"]
    for post_data in detailed_posts:
        # Data for LI_post table in Bubble
        li_posts.append({
            "organization_urn": post_data["organization_urn"],
            "id": post_data["id"], # Post URN
            "is_ad": post_data["is_ad"],
            #"media_type": post_data["media_type"],
            "published_at": post_data["published_at"], # ISO datetime string
            "sentiment": post_data["sentiment"], # Overall sentiment of the post based on its comments
            "text": post_data["raw_text"], # Storing the full raw text
            #"summary_text": post_data["summary"],
            "li_eb_label": post_data["category"]
            # Add any other fields from post_data needed for LI_post table
        })

        # Data for LI_post_stats table in Bubble
        li_post_stats.append({
            "clickCount": post_data["clicks"],
            "commentCount": post_data["comments_stat_count"], # From post's own stats
            "engagement": post_data["engagement"], # Formatted string e.g., "12.34%"
            "impressionCount": post_data["impressions"],
            "likeCount": post_data["likes"],
            "shareCount": post_data["shares"],
            "uniqueImpressionsCount": post_data["uniqueImpressionsCount"],
            "post_id": post_data["id"], # Foreign key to LI_post
            "organization_urn": org_urn
        })

    # Data for LI_post_comments table in Bubble (individual comments)
    # This iterates through the actual comments fetched, not just the count.
    for post_urn, comments_text_list in all_actual_comments_data.items():
        for single_comment_text in comments_text_list:
            if single_comment_text and single_comment_text.strip(): # Ensure comment text is not empty
                li_post_comments.append({
                    "comment_text": single_comment_text,
                    "post_id": post_urn, # Foreign key to LI_post
                    "organization_urn": org_urn
                    # Could add sentiment per comment here if analyzed at that granularity
                })
    
    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