import logging

from celery import shared_task

logger = logging.getLogger(__name__)

POSITIVE_WORDS = {
    "excellent",
    "amazing",
    "wonderful",
    "great",
    "good",
    "fantastic",
    "outstanding",
    "helpful",
    "friendly",
    "professional",
    "kind",
    "caring",
    "clean",
    "comfortable",
    "satisfied",
    "impressed",
    "thankful",
    "grateful",
    "appreciate",
    "perfect",
    "superb",
    "brilliant",
    "exceptional",
    "positive",
    "recommend",
    "pleasant",
    "courteous",
    "efficient",
    "prompt",
    "compassionate",
    "skilled",
    "best",
}

NEGATIVE_WORDS = {
    "terrible",
    "awful",
    "horrible",
    "bad",
    "poor",
    "worst",
    "rude",
    "unprofessional",
    "dirty",
    "uncomfortable",
    "dissatisfied",
    "disappointed",
    "frustrated",
    "angry",
    "slow",
    "waiting",
    "waited",
    "delay",
    "delayed",
    "neglect",
    "neglected",
    "ignored",
    "complaint",
    "unacceptable",
    "never",
    "not",
    "no",
    "cannot",
    "painful",
    "careless",
    "arrogant",
    "unhelpful",
    "overpriced",
    "expensive",
    "messy",
    "noisy",
    "unsafe",
    "incompetent",
    "wrong",
    "mistake",
    "error",
}


def _analyze_text(text):
    if not text:
        return 0.0, 0, 0

    words = text.lower().split()
    total = len(words) or 1

    positive_hits = sum(1 for w in words if w in POSITIVE_WORDS)
    negative_hits = sum(1 for w in words if w in NEGATIVE_WORDS)

    score = (positive_hits - negative_hits) / total

    return max(-1.0, min(1.0, score)), positive_hits, negative_hits


@shared_task
def analyze_feedback_sentiment(feedback_id):
    from apps.feedback.models import Feedback, SentimentChoices

    try:
        feedback = Feedback.objects.get(id=feedback_id)
    except Feedback.DoesNotExist:
        logger.error(f"Feedback {feedback_id} not found")
        return {"status": "error", "reason": "not_found"}

    title_score, title_pos, title_neg = _analyze_text(feedback.title or "")
    message_score, msg_pos, msg_neg = _analyze_text(feedback.message or "")

    total_pos = title_pos + msg_pos
    total_neg = title_neg + msg_neg

    combined_score = (title_score + message_score) / 2 if (title_score or message_score) else 0.0

    if combined_score > 0.05:
        sentiment = SentimentChoices.POSITIVE
    elif combined_score < -0.05:
        sentiment = SentimentChoices.NEGATIVE
    else:
        sentiment = SentimentChoices.NEUTRAL

    sentiment_score = round(combined_score, 4)

    feedback.sentiment = sentiment
    feedback.sentiment_score = sentiment_score
    feedback.save(update_fields=["sentiment", "sentiment_score"])

    logger.info(
        f"Sentiment analysis for feedback {feedback_id}: "
        f"sentiment={sentiment}, score={sentiment_score} "
        f"(pos={total_pos}, neg={total_neg})"
    )

    return {
        "status": "success",
        "feedback_id": str(feedback_id),
        "sentiment": sentiment,
        "sentiment_score": sentiment_score,
        "positive_hits": total_pos,
        "negative_hits": total_neg,
    }


@shared_task
def process_pending_sentiment_analysis():
    from apps.feedback.models import Feedback

    pending = Feedback.objects.filter(sentiment="neutral", sentiment_score__isnull=True)[:100]

    dispatched = 0
    for feedback in pending:
        analyze_feedback_sentiment.delay(str(feedback.id))
        dispatched += 1

    logger.info(f"Dispatched sentiment analysis for {dispatched} pending feedbacks")
    return {"dispatched": dispatched}