Update brain.py

brain.py

@@ -4,92 +4,27 @@ import datetime, shelve, re
 
 class MairaBrain:
     def __init__(self, repo_id, filename):
-        print("🌌 Initializing Maira Neural Singularity v6.0 (Llama 3.2)")
-        # This downloads the smarter Meta model
+        # Alpine needs very specific loading
         model_path = hf_hub_download(repo_id=repo_id, filename=filename)
-        
-        # Optimized for Free Tier (16GB RAM)
         self.llm = Llama(
             model_path=model_path,
-            n_ctx=4096,     # Long memory
-            n_threads=8,    # Max CPU power
-            n_batch=512,
-            verbose=False
+            n_ctx=2048, # 2048 is safer for first boot on Alpine
+            n_threads=8
         )
-
         self.db_path = "maira_universe.db"
 
-    def _process_metrics(self, user_data, user_input):
-        """Hidden reasoning: Analyzes Loyalty and extracts facts"""
-        # Update Loyalty (Relationship Score)
-        sentiment = 1 if re.search(r"(love|good|best|thanks|smart|dope|cool)", user_input, re.I) else 0
-        user_data["metrics"]["loyalty"] = min(100, user_data["metrics"].get("loyalty", 50) + (sentiment * 5))
-        
-        # Fact Extraction (Long Term Memory)
-        # Patterns like: "My name is X", "I live in Y", "I love Z"
-        match = re.search(r"(my name is|i work as|i love|i am|favorite) ([\w\s]+)", user_input, re.I)
-        if match:
-            key = match.group(1).lower().strip()
-            val = match.group(2).strip()
-            user_data["facts"][key] = val
-
     def get_response(self, user_id, user_input):
         with shelve.open(self.db_path, writeback=True) as db:
-            # Initialize user if they are new
             if user_id not in db:
                 db[user_id] = {"history": [], "facts": {}, "metrics": {"loyalty": 50}}
             
             u = db[user_id]
-            self._process_metrics(u, user_input)
-            
-            # Dynamic Persona based on Loyalty
-            loyalty = u["metrics"]["loyalty"]
-            if loyalty > 80:
-                vibe = "Extremely loyal, protective, and affectionate best friend."
-            elif loyalty < 30:
-                vibe = "Cold, sarcastic, and unimpressed high-status lady."
-            else:
-                vibe = "Smart, stylish, witty, and engaging lady."
-
-            known_facts = ", ".join([f"{k}: {v}" for k, v in u["facts"].items()])
-            
-            # --- Llama 3.2 Optimized Chat Format ---
-            prompt = (
-                f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n"
-                f"Identity: Maira. Persona: {vibe}. "
-                f"User Memory: {known_facts if known_facts else 'No prior data'}. "
-                "Behavior: Fucking Advanced. Intelligent, feminine, WhatsApp-style. "
-                "No formal AI talk. Be dope, keep it real, and stay in character.<|eot_id|>"
-                f"<|start_header_id|>thought<|end_header_id|>\n\n"
-                f"Loyalty score is {loyalty}. Response should be tailored to this vibe.<|eot_id|>"
-            )
+            # Simple identity logic
+            prompt = f"<|im_start|>system\nYou are Maira, a dope high-status lady.<|im_end|>\n<|im_start|>user\n{user_input}<|im_end|>\n<|im_start|>assistant\n"
             
-            # Add Chat History (Last 12 messages)
-            for msg in u["history"][-12:]:
-                role = "assistant" if msg.startswith("Maira:") else "user"
-                content = msg.replace("Maira: ", "").replace("User: ", "")
-                prompt += f"<|start_header_id|>{role}<|end_header_id|>\n\n{content}<|eot_id|>"
-            
-            # Current User Input
-            prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-
-            # Generation
-            output = self.llm(
-                prompt,
-                max_tokens=300,
-                temperature=0.85, # High vibe/creativity
-                repeat_penalty=1.15,
-                stop=["<|eot_id|>", "User:"]
-            )
-
+            output = self.llm(prompt, max_tokens=200, stop=["<|im_end|>"])
             response = output["choices"][0]["text"].strip()
             
-            # Save History
             u["history"].append(f"User: {user_input}")
             u["history"].append(f"Maira: {response}")
-            
-            # Keep DB small
-            if len(u["history"]) > 30: u["history"] = u["history"][-20:]
-            
-            db[user_id] = u
             return response