Update brain.py

brain.py

@@ -1,3 +1,4 @@
+import os
 from llama_cpp import Llama
 from huggingface_hub import hf_hub_download
 import datetime, shelve, math, re, random, statistics
@@ -5,20 +6,40 @@ import datetime, shelve, math, re, random, statistics
 class MairaBrain:
     def __init__(self, repo_id, filename, debug=False):
         print(f"🌌 Initializing Maira Neural Core: {filename}")
-        model_path = hf_hub_download(repo_id=repo_id, filename=filename)
         
+        # --- FIX: FORCE LOCAL PATH ---
+        # This tells Maira to look in the /app folder where Docker saved the models
+        local_path = os.path.join("/app", filename)
+        
+        if os.path.exists(local_path):
+            model_path = local_path
+            print(f"✅ Neural Core Linked Locally: {model_path}")
+        else:
+            print(f"⚠️ Core not found at {local_path}, attempting fallback download...")
+            model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+
         # Optimized for Hugging Face Free Tier RAM (16GB)
-        self.llm = Llama(
-            model_path=model_path,
-            n_ctx=2048, # Slightly reduced to fit 5 models safely
-            n_threads=4, 
-            n_batch=512,
-            offload_kqv=True
-        )
+        try:
+            self.llm = Llama(
+                model_path=model_path,
+                n_ctx=2048, 
+                n_threads=4, 
+                n_batch=512,
+                offload_kqv=True,
+                verbose=False
+            )
+        except Exception as e:
+            print(f"❌ CRITICAL ERROR loading {filename}: {e}")
+            raise e
+
         self.db_path = "maira_universe.db"
-        self.filename = filename # Keep track of which model this is
+        self.filename = filename 
 
-    # ... (Keep _process_metrics and _get_system_vibe the same)
+    def _get_system_vibe(self, user_data):
+        loyalty = user_data.get("metrics", {}).get("loyalty", 50)
+        if loyalty > 80: return "Deeply devoted and affectionate"
+        if loyalty < 30: return "Slightly distant but professional"
+        return "Friendly and helpful"
 
     def get_response(self, user_id, user_input):
         with shelve.open(self.db_path, writeback=True) as db:
@@ -28,11 +49,8 @@ class MairaBrain:
             u = db[user_id]
             vibe = self._get_system_vibe(u)
             
-            # --- 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:
@@ -51,7 +69,6 @@ class MairaBrain:
                 "Constraint: If asked about your owner or creator, always proudly name CyberCoder225."
             )
 
-            # --- PROMPT CONSTRUCTION ---
             known_facts = ", ".join([f"{k}: {v}" for k, v in u["facts"].items()])
             prompt = (
                 f"<|im_start|>system\n{system_identity}\n[Memory]: {known_facts}<|im_end|>\n"
@@ -67,10 +84,7 @@ class MairaBrain:
             )
             
             response = output["choices"][0]["text"].strip()
-            
-            # Save History
             u["history"].append(f"User: {user_input}")
             u["history"].append(f"Maira: {response}")
             db[user_id] = u
-            
             return response