244 lines
6.9 KiB
Python
244 lines
6.9 KiB
Python
import logging
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from celery import shared_task
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logger = logging.getLogger(__name__)
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POSITIVE_WORDS = {
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"excellent",
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"amazing",
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"wonderful",
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"great",
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"good",
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"fantastic",
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"outstanding",
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"helpful",
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"friendly",
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"professional",
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"kind",
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"caring",
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"clean",
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"comfortable",
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"satisfied",
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"impressed",
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"thankful",
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"grateful",
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"appreciate",
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"perfect",
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"superb",
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"brilliant",
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"exceptional",
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"positive",
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"recommend",
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"pleasant",
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"courteous",
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"efficient",
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"prompt",
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"compassionate",
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"skilled",
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"best",
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}
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NEGATIVE_WORDS = {
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"terrible",
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"awful",
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"horrible",
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"bad",
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"poor",
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"worst",
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"rude",
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"unprofessional",
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"dirty",
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"uncomfortable",
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"dissatisfied",
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"disappointed",
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"frustrated",
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"angry",
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"slow",
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"waiting",
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"waited",
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"delay",
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"delayed",
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"neglect",
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"neglected",
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"ignored",
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"complaint",
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"unacceptable",
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"never",
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"not",
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"no",
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"cannot",
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"painful",
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"careless",
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"arrogant",
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"unhelpful",
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"overpriced",
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"expensive",
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"messy",
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"noisy",
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"unsafe",
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"incompetent",
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"wrong",
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"mistake",
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"error",
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}
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def _analyze_text(text):
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if not text:
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return 0.0, 0, 0
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words = text.lower().split()
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total = len(words) or 1
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positive_hits = sum(1 for w in words if w in POSITIVE_WORDS)
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negative_hits = sum(1 for w in words if w in NEGATIVE_WORDS)
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score = (positive_hits - negative_hits) / total
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return max(-1.0, min(1.0, score)), positive_hits, negative_hits
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@shared_task
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def analyze_feedback_sentiment(feedback_id):
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from apps.feedback.models import Feedback, SentimentChoices
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try:
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feedback = Feedback.objects.get(id=feedback_id)
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except Feedback.DoesNotExist:
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logger.error(f"Feedback {feedback_id} not found")
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return {"status": "error", "reason": "not_found"}
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title_score, title_pos, title_neg = _analyze_text(feedback.title or "")
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message_score, msg_pos, msg_neg = _analyze_text(feedback.message or "")
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total_pos = title_pos + msg_pos
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total_neg = title_neg + msg_neg
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combined_score = (title_score + message_score) / 2 if (title_score or message_score) else 0.0
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if combined_score > 0.05:
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sentiment = SentimentChoices.POSITIVE
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elif combined_score < -0.05:
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sentiment = SentimentChoices.NEGATIVE
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else:
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sentiment = SentimentChoices.NEUTRAL
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sentiment_score = round(combined_score, 4)
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feedback.sentiment = sentiment
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feedback.sentiment_score = sentiment_score
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feedback.save(update_fields=["sentiment", "sentiment_score"])
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logger.info(
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f"Sentiment analysis for feedback {feedback_id}: "
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f"sentiment={sentiment}, score={sentiment_score} "
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f"(pos={total_pos}, neg={total_neg})"
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)
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return {
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"status": "success",
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"feedback_id": str(feedback_id),
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"sentiment": sentiment,
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"sentiment_score": sentiment_score,
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"positive_hits": total_pos,
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"negative_hits": total_neg,
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}
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@shared_task
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def process_pending_sentiment_analysis():
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from apps.feedback.models import Feedback
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pending = Feedback.objects.filter(sentiment="neutral", sentiment_score__isnull=True)[:100]
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dispatched = 0
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for feedback in pending:
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analyze_feedback_sentiment.delay(str(feedback.id))
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dispatched += 1
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logger.info(f"Dispatched sentiment analysis for {dispatched} pending feedbacks")
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return {"dispatched": dispatched}
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@shared_task
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def analyze_suggestion_with_ai(feedback_id):
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from apps.feedback.models import Feedback, FeedbackCategory, FeedbackResponse
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from apps.core.ai_service import AIService
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try:
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feedback = Feedback.objects.get(id=feedback_id)
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except Feedback.DoesNotExist:
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logger.error(f"Feedback {feedback_id} not found for AI analysis")
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return {"status": "error", "reason": "not_found"}
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try:
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analysis = AIService.analyze_suggestion(
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message=feedback.message or "",
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title=feedback.title or None,
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)
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category_map = {
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"clinical_care": FeedbackCategory.CLINICAL_CARE,
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"facility": FeedbackCategory.FACILITY,
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"staff_service": FeedbackCategory.STAFF_SERVICE,
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"communication": FeedbackCategory.COMMUNICATION,
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"technology": FeedbackCategory.TECHNOLOGY,
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"food_service": FeedbackCategory.FOOD_SERVICE,
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"appointment": FeedbackCategory.APPOINTMENT,
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"process_improvement": FeedbackCategory.OTHER,
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"other": FeedbackCategory.OTHER,
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}
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ai_category = category_map.get(analysis.get("category", "other"), FeedbackCategory.OTHER)
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ai_priority = analysis.get("priority", "medium")
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old_category = feedback.get_category_display()
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old_priority = feedback.get_priority_display()
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feedback.category = ai_category
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feedback.priority = ai_priority
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if not feedback.title or feedback.title.startswith(feedback.message[:80] if feedback.message else ""):
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ai_title = analysis.get("short_description_en", "")
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if ai_title:
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feedback.title = ai_title[:500]
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if not feedback.metadata:
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feedback.metadata = {}
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feedback.metadata["ai_analysis"] = {
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"short_description_en": analysis.get("short_description_en", ""),
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"short_description_ar": analysis.get("short_description_ar", ""),
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"category": analysis.get("category", "other"),
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"priority": ai_priority,
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"suggested_actions": analysis.get("suggested_actions", []),
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"suggested_action_en": analysis.get("suggested_action_en", ""),
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"suggested_action_ar": analysis.get("suggested_action_ar", ""),
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"reasoning_en": analysis.get("reasoning_en", ""),
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"reasoning_ar": analysis.get("reasoning_ar", ""),
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"old_category": old_category,
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"old_priority": old_priority,
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"analyzed_at": str(timezone.now().isoformat()),
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}
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feedback.save(update_fields=["title", "category", "priority", "metadata"])
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FeedbackResponse.objects.create(
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feedback=feedback,
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response_type="note",
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message=f"AI analysis completed. Category: {feedback.get_category_display()}, Priority: {feedback.get_priority_display()}",
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created_by=None,
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is_internal=True,
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metadata={"ai_generated": True},
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)
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logger.info(
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f"Suggestion AI analysis completed for {feedback_id}: "
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f"category={analysis.get('category')}, priority={ai_priority}"
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)
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return {"status": "success", "feedback_id": str(feedback_id)}
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except Exception as e:
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logger.error(f"Error analyzing suggestion {feedback_id}: {e}")
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return {"status": "error", "reason": str(e)}
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