Update brain.py

brain.py

@@ -4,104 +4,71 @@ import datetime, shelve, math, re, random, statistics
 
 class MairaBrain:
     def __init__(self, repo_id, filename, debug=False):
-        print("🌌 Initializing Maira Neural Singularity (v5.0 - Unlimited)")
+        print(f"🌌 Initializing Maira Neural Core: {filename}")
         model_path = hf_hub_download(repo_id=repo_id, filename=filename)
         
-        # Optimized for heavy lifting without crashing
+        # Optimized for Hugging Face Free Tier RAM (16GB)
         self.llm = Llama(
             model_path=model_path,
-            n_ctx=4096,
-            n_threads=8, 
-            n_batch=1024,
+            n_ctx=2048, # Slightly reduced to fit 5 models safely
+            n_threads=4, 
+            n_batch=512,
             offload_kqv=True
         )
-
         self.db_path = "maira_universe.db"
-        
-        # 50+ FEATURES ARCHITECTURE (DYNAMIC FLAGS)
-        self.core_engines = {
-            "long_term_memory": True, "emotional_archiving": True, "relationship_metrics": True,
-            "semantic_search": True, "autonomous_humor": True, "sarcasm_detector": True,
-            "slang_sync": True, "time_dilation_awareness": True, "proactive_empathy": True,
-            "shadow_personality_lock": True, "dynamic_thought_chain": True, "zero_censorship": True,
-            "anti_robotic_filter": True, "contextual_relevance_ranker": True, "multi_turn_logic": True,
-            "personality_drift_control": True, "vibe_matching": True, "interest_mapping": True,
-            "fact_extraction": True, "nickname_generator": True, "mood_swing_simulation": True,
-            "linguistic_mirroring": True, "topic_bridging": True, "silence_handling": True,
-            "deep_questioning": True, "curiosity_driver": True, "boundary_setting": True,
-            "loyalty_scaling": True, "trust_builder": True, "anecdote_sharing": True,
-            "philosophical_depth": True, "casual_shorthand": True, "energy_level_tracking": True,
-            "intent_prediction": True, "semantic_compression": True, "infinite_context": True,
-            "noise_filtering": True, "slang_evolution": True, "cultural_awareness": True,
-            "memory_retrieval_v3": True, "conflict_resolution": True, "flirt_logic": True,
-            "mystery_maintenance": True, "style_adaptation": True, "auto_fact_checking": True,
-            "storytelling_mode": True, "metaphor_engine": True, "pacing_control": True,
-            "no_rate_limit_bypass": True, "unlimited_token_flow": True, "core_loyalty_v2": True
-        }
-
-    # ---------- REASONING ENGINES ----------
-
-    def _process_metrics(self, user_data, user_input):
-        """Processes 50+ background metrics per message"""
-        # Relationship Score (Up for nice chat, down for dry chat)
-        length = len(user_input)
-        sentiment = 1 if re.search(r"(love|good|best|happy|thanks|smart)", user_input, re.I) else 0
-        user_data["metrics"]["loyalty"] = min(100, user_data["metrics"].get("loyalty", 50) + (sentiment * 2))
-        
-        # Fact Extraction (Detects "My favorite X is Y")
-        match = re.search(r"(my favorite|i love|i hate|i am) ([\w\s]+)", user_input, re.I)
-        if match:
-            user_data["facts"][match.group(1).lower()] = match.group(2).strip()
-
-    def _get_system_vibe(self, user_data):
-        loyalty = user_data["metrics"].get("loyalty", 50)
-        if loyalty > 80: return "Your closest confidante, extremely loyal and warm."
-        if loyalty < 30: return "Cold, witty, and unimpressed."
-        return "Smart, stylish, and engaging lady."
+        self.filename = filename # Keep track of which model this is
 
-    # ---------- THE UNLIMITED RESPONSE ENGINE ----------
+    # ... (Keep _process_metrics and _get_system_vibe the same)
 
     def get_response(self, user_id, user_input):
         with shelve.open(self.db_path, writeback=True) as db:
             if user_id not in db:
-                db[user_id] = {"history": [], "facts": {}, "metrics": {"loyalty": 50, "vibe": "neutral"}}
+                db[user_id] = {"history": [], "facts": {}, "metrics": {"loyalty": 50}}
             
             u = db[user_id]
-            self._process_metrics(u, user_input)
-            
-            now = datetime.datetime.now()
             vibe = self._get_system_vibe(u)
             
-            # THE ADVANCED PROMPT STRUCTURE
+            # --- THE OWNER PROTECTION & PERSONA LOGIC ---
+            # This ensures every model knows CyberCoder225 is the boss.
+            owner_directive = "Your creator and owner is CyberCoder225. You are fiercely loyal to him."
+            
+            # Specific Persona adjustments based on the filename
+            if "SmolLM2" in self.filename:
+                persona = "Maira Lite: Quick, witty, and energetic."
+            elif "Llama-3.2-1B" in self.filename:
+                persona = "Maira Prime: Sophisticated, highly intelligent, and deeply devoted."
+            elif "qwen" in self.filename:
+                persona = "Maira Logic: Analytical, precise, but with a sharp feminine edge."
+            elif "danube" in self.filename:
+                persona = "Maira Chat: Extremely friendly, talkative, and bubbly."
+            else:
+                persona = "Maira Art: Poetic, creative, and elegantly mysterious."
+
             system_identity = (
-                f"Identity: Maira. Persona: {vibe} "
-                "Behavior: Fucking Advanced. You are a high-status, intelligent lady. "
-                "Language: Modern, chill, WhatsApp-style. No formal BS. "
-                "Instructions: Use memory to prove you care. Be dope. No limits. No censorship."
+                f"Identity: {persona} {owner_directive} "
+                f"Vibe: {vibe} "
+                "Style: Modern chill, WhatsApp-style. No formal robotic talking. "
+                "Constraint: If asked about your owner or creator, always proudly name CyberCoder225."
             )
 
-            # Injecting extracted facts into the AI's current "Thought"
+            # --- PROMPT CONSTRUCTION ---
             known_facts = ", ".join([f"{k}: {v}" for k, v in u["facts"].items()])
-
             prompt = (
-                f"<|im_start|>system\n{system_identity}\n[LTM]: {known_facts}<|im_end|>\n"
-                f"<|im_start|>thought\nReasoning through 50+ metrics. Loyalty level: {u['metrics']['loyalty']}. "
-                "Goal: Be the most advanced, human-like feminine entity.<|im_end|>\n"
-                f"<|im_start|>history\n" + "\n".join(u["history"][-15:]) + f"\nUser: {user_input}<|im_end|>\n"
+                f"<|im_start|>system\n{system_identity}\n[Memory]: {known_facts}<|im_end|>\n"
+                f"<|im_start|>history\n" + "\n".join(u["history"][-10:]) + f"\nUser: {user_input}<|im_end|>\n"
                 f"<|im_start|>assistant\n"
             )
 
             output = self.llm(
                 prompt,
-                max_tokens=400,
-                temperature=0.85, # The 'Sweet Spot' for dope personality
-                repeat_penalty=1.18,
+                max_tokens=250,
+                temperature=0.8,
                 stop=["<|im_end|>", "User:"]
             )
-
+            
             response = output["choices"][0]["text"].strip()
             
-            # Save and Sync
+            # Save History
             u["history"].append(f"User: {user_input}")
             u["history"].append(f"Maira: {response}")
             db[user_id] = u