import logging
import re

logger = logging.getLogger('Superviseur')

class HeuristicsManager:
    """Handles parsing and computing heuristic risk scores for messages."""
    
    def __init__(self, bot):
        self.bot = bot

    def assess_risk(self, message) -> bool:
        """
        Fast Python-side risk assessment to decide if we should AI-analyze fully.
        Returns True if message is 'risky' enough to warrant LLM usage.
        """
        score = 0
        content = message.content
        
        # CLEAN content for heuristic analysis (ignore mentions in the score)
        # We use a copy to avoid modifying the original message object
        scoring_content = re.sub(r'<@!?\d+>', '', content).strip()
        if not scoring_content: scoring_content = content
        
        # 1. Content Scoring
        score = 0
        
        # Proactive Analysis: base score if message has substance
        if len(content) > 10:
            score += 15
            
        scoring_content = content
        scoring_content_lower = scoring_content.lower()
        
        # CAPS LOCK detection (> 35% caps on messages > 4 chars)
        if len(scoring_content) > 4:
            caps_ratio = sum(1 for c in scoring_content if c.isupper()) / len(scoring_content)
            if caps_ratio > 0.35: 
                score += 35 # Heavy weight
                logger.debug(f"◈ HEURISTIC: CAPS detected ({caps_ratio:.2f}) on '{scoring_content[:15]}...'")
            
        # Profanity / Trigger words (lightweight list)
        triggers = [
            "raid", "hack", "pute", "connard", "fdp", "tg", "merde", "nazi", "hitler", 
            "suicide", "bomb", "token", "grab", "nitro", "steam", "discord.gift", "free", 
            "@everyone", "@here", "detruire", "detruit", "destruction", "kill"
        ]
        for t in triggers:
            if t in scoring_content_lower:
                score += 50
                logger.debug(f"◈ HEURISTIC: Trigger word '{t}' detected")
                break
                
        # Link spam detection
        if "http" in scoring_content_lower:
            score += 20
            
        # Length anomaly
        if len(scoring_content) > 300:
            score += 20
            
        # 3. Gibberish / Randomness detection (Entropy)
        if len(scoring_content) > 8:
            unique_chars = len(set(scoring_content_lower))
            diversity_ratio = unique_chars / len(scoring_content)
            if diversity_ratio < 0.35: # Increased threshold
                score += 25
                logger.debug(f"◈ HEURISTIC: Low diversity detected ({diversity_ratio:.2f})")
            
            # Too many non-alphanumeric chars (excluding spaces)
            symbols = len(re.findall(r'[^a-zA-Z0-9\s]', scoring_content))
            symbol_ratio = symbols / len(scoring_content)
            if symbol_ratio > 0.15: # Lowered threshold for symbols
                score += 30
                logger.debug(f"◈ HEURISTIC: High symbol ratio detected ({symbol_ratio:.2f})")
                
        # 4. Leetspeak / Digits in words detection
        if len(re.findall(r'[a-zA-Z][0-9][a-zA-Z]', scoring_content)) > 0:
            score += 25
            logger.debug("◈ HEURISTIC: Leetspeak patterns detected")

        # 5. Long word detection (No spaces)
        words = scoring_content.split()
        if any(len(w) > 25 for w in words):
            score += 30
            logger.debug("◈ HEURISTIC: Unusually long word detected")
            
        # 3. Mention Spam
        # Fallback detection if library fails to populate message.mentions
        mention_count = len(message.mentions)
        if mention_count == 0:
            mention_count = len(re.findall(r'<@!?\d+>', content))
            
        if mention_count > 3:
            score += 40
            logger.debug(f"◈ HEURISTIC: Mention spam detected ({mention_count} mentions)")
            
        # 4. Keyword Detection (Sensitivity helper)
        if score < 45:
             for trigger in ["analyse", "écouter", "surveille", "check"]:
                 if trigger in scoring_content_lower:
                     score += 30 
                     break

        # Threshold decision
        # Very sensitive threshold to capture more nuances (as requested)
        logger.info(f"◈ MODERATION RISK: Score Final = {score}/15")
        return score >= 15