"""
Emotion Detection Module
Detects emotions in text beyond basic sentiment

Developer: RSK World
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277
Year: 2026
"""

from transformers import pipeline
import re

class EmotionDetector:
    """
    Emotion detection class
    Developer: RSK World - https://rskworld.in
    """
    
    def __init__(self):
        """Initialize emotion detector"""
        try:
            # Emotion classification model
            self.emotion_classifier = pipeline(
                "text-classification",
                model="j-hartmann/emotion-english-distilroberta-base",
                device=-1  # Use CPU
            )
        except Exception as e:
            print(f"Warning: Could not load emotion classifier: {e}")
            self.emotion_classifier = None
    
    def detect_emotions(self, text):
        """
        Detect emotions in text
        
        Args:
            text (str): Input text
            
        Returns:
            dict: Emotion detection results
        """
        if self.emotion_classifier is None:
            return self.keyword_based_emotion(text)
        
        try:
            # Truncate if too long
            max_length = 512
            if len(text) > max_length:
                text = text[:max_length]
            
            result = self.emotion_classifier(text, return_all_scores=True)
            
            if result and len(result) > 0:
                emotions = result[0]
                sorted_emotions = sorted(emotions, key=lambda x: x['score'], reverse=True)
                
                return {
                    'primary_emotion': sorted_emotions[0]['label'],
                    'primary_score': sorted_emotions[0]['score'],
                    'all_emotions': [
                        {'emotion': emo['label'], 'score': emo['score']}
                        for emo in sorted_emotions
                    ],
                    'top_3_emotions': [
                        {'emotion': emo['label'], 'score': emo['score']}
                        for emo in sorted_emotions[:3]
                    ]
                }
        except Exception as e:
            print(f"Error in emotion detection: {e}")
            return self.keyword_based_emotion(text)
    
    def keyword_based_emotion(self, text):
        """
        Fallback keyword-based emotion detection
        
        Args:
            text (str): Input text
            
        Returns:
            dict: Emotion detection results
        """
        text_lower = text.lower()
        
        # Emotion keywords
        emotion_keywords = {
            'joy': ['happy', 'joy', 'excited', 'delighted', 'pleased', 'glad', 'cheerful'],
            'sadness': ['sad', 'unhappy', 'depressed', 'sorrow', 'grief', 'melancholy'],
            'anger': ['angry', 'mad', 'furious', 'rage', 'annoyed', 'irritated'],
            'fear': ['afraid', 'scared', 'fear', 'worried', 'anxious', 'terrified'],
            'surprise': ['surprised', 'amazed', 'shocked', 'astonished', 'wow'],
            'disgust': ['disgusted', 'revolted', 'sickened', 'repulsed'],
            'neutral': ['okay', 'fine', 'normal', 'alright']
        }
        
        emotion_scores = {}
        for emotion, keywords in emotion_keywords.items():
            score = sum(1 for keyword in keywords if keyword in text_lower)
            emotion_scores[emotion] = score
        
        # Normalize scores
        total_score = sum(emotion_scores.values())
        if total_score > 0:
            emotion_scores = {k: v / total_score for k, v in emotion_scores.items()}
        else:
            emotion_scores = {'neutral': 1.0}
        
        sorted_emotions = sorted(emotion_scores.items(), key=lambda x: x[1], reverse=True)
        
        return {
            'primary_emotion': sorted_emotions[0][0],
            'primary_score': sorted_emotions[0][1],
            'all_emotions': [
                {'emotion': emo, 'score': score}
                for emo, score in sorted_emotions
            ],
            'top_3_emotions': [
                {'emotion': emo, 'score': score}
                for emo, score in sorted_emotions[:3]
            ]
        }