Initialisation du repository de Beta

superviseur/heuristic.py

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+    def _assess_heuristic_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
+        
+        # 1. Social Score Factor (Base scrutiny)
+        # If user is Suspect (<40), we start with higher risk score
+        social_data = self.social_manager.get_user_data(message.author.id)
+        reliability = social_data.get('score', 50)
+        
+        if reliability < 30: score += 40  # Very suspicious user -> almost auto-check
+        elif reliability < 50: score += 20
+        
+        # 2. Pattern Matching (Fast Regex/Keywords)
+        content_lower = content.lower()
+        
+        # CAPS LOCK detection (> 60% caps on long messages)
+        if len(content) > 10:
+            caps_ratio = sum(1 for c in content if c.isupper()) / len(content)
+            if caps_ratio > 0.6: score += 30
+            
+        # 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"]
+        for t in triggers:
+            if t in content_lower:
+                score += 50
+                break
+                
+        # Link spam detection
+        if "http" in content_lower:
+            score += 15
+            
+        # Length anomaly (very long messages often rants)
+        if len(content) > 400:
+            score += 20
+            
+        # 3. Mention Spam
+        if len(message.mentions) > 3:
+            score += 40
+            
+        # Threshold decision
+        # If score > 45, we analyze.
+        return score >= 45