fix: Correct model initialization order

api/main.py

@@ -63,6 +63,7 @@ FINAL_EMBEDDING_PATH = EMBEDDING_MODEL_PATH if os.path.exists(EMBEDDING_MODEL_PA
 _llm_instance: Optional[Llama] = None
 _vector_store: Optional[Any] = None
 _ai_strategist: Optional[AIStrategist] = None
+_creative_director: Optional[CreativeDirector] = None 
 _support_agent: Optional[SupportAgent] = None
 _budget_predictor = None
 _influencer_matcher = None
@@ -506,16 +507,15 @@ app = FastAPI(title="Reachify AI Service (Deploy-Ready)", version="11.0.0")
 
 @app.on_event("startup")
 def startup_event():
-    global _llm_instance, _ai_strategist, _support_agent, _vector_store, \
+    # Make sure we can modify the global variables
+    global _llm_instance, _creative_director, _support_agent, _ai_strategist, _vector_store, \
            _budget_predictor, _influencer_matcher, _performance_predictor, _payout_forecaster, \
            _earnings_optimizer, _earnings_encoder, _likes_predictor, _comments_predictor, \
            _revenue_forecaster, _performance_scorer
     
-    print("--- 🚀 AI Service Starting Up (Hugging Face Mode)... ---")
-    
-    # === FIX #3: The Model Download and Loading Logic ===
+    # === MODEL DOWNLOAD AND LOAD LOGIC ===
+    print("--- 🚀 AI Service Starting Up... ---")
     try:
-        # Step 1: Download the model if it doesn't exist
         os.makedirs(MODEL_SAVE_DIRECTORY, exist_ok=True)
         if not os.path.exists(LLAMA_MODEL_PATH):
             print(f"   - LLM model not found locally. Downloading '{MODEL_FILENAME}'...")
@@ -523,51 +523,48 @@ def startup_event():
                 repo_id=MODEL_REPO,
                 filename=MODEL_FILENAME,
                 local_dir=MODEL_SAVE_DIRECTORY,
-                local_dir_use_symlinks=False # This is safer for containers
+                local_dir_use_symlinks=False
             )
             print("   - ✅ Model downloaded successfully.")
         else:
             print(f"   - LLM model found at {LLAMA_MODEL_PATH}. Skipping download.")
         
-        # Step 2: Now that the file is guaranteed to be there, load it.
-        print("   - Loading Llama LLM into memory from downloaded file...")
-        _llm_instance = Llama(
-            model_path=LLAMA_MODEL_PATH, 
-            n_gpu_layers=0,  # Ensure CPU usage on free tier
-            n_ctx=2048, 
-            verbose=False,
-            use_mmap=False 
-        )
-        print("   - ✅ LLM Loaded into Memory on CPU.")
+        print("   - Loading Llama LLM into memory...")
+        _llm_instance = Llama(model_path=LLAMA_MODEL_PATH, n_gpu_layers=0, n_ctx=2048, verbose=False, use_mmap=False)
+        print("   - ✅ LLM Loaded.")
     
     except Exception as e:
         print(f"   - ❌ FATAL ERROR: Could not download or load LLM model: {e}")
         traceback.print_exc()
-        _llm_instance = None # Ensure it is None if it fails
-    # =========================================================
+        # If LLM fails to load, we can't continue.
+        # Set instance to None and the rest of the app will know.
+        _llm_instance = None
+        return # Stop the startup process here.
+        
+    # === INITIALIZE AI COMPONENTS (NOW THAT LLM IS LOADED) ===
+    # This logic now runs ONLY IF the LLM loaded successfully.
+    try:
+        print("   - Initializing Creative Director...")
+        _creative_director = CreativeDirector(llm_instance=_llm_instance)
+        print("   - ✅ Creative Director is online.")
 
-    # --- All the rest of your startup logic remains EXACTLY THE SAME ---
-    if VectorStore:
-        try:
+        if VectorStore:
+            print("   - Initializing Vector Store...")
             _vector_store = VectorStore()
             print("   - ✅ RAG Engine Ready.")
-        except Exception:
-            _vector_store = None
-    else:
-        _vector_store = None
-
-    print("   - Initializing AI Strategist...")
-    _ai_strategist = AIStrategist(llm_instance=_llm_instance, store=_vector_store)
-    print("   - ✅ AI Strategist ready.")
-
-    print("   - Initializing Support Agent...")
-    _support_agent = SupportAgent(
-        llm_instance=_llm_instance,
-        embedding_path=EMBEDDING_MODEL_PATH,
-        db_path=DB_PATH
-    )
-    print("   - ✅ Support Agent ready.")
-    
+        
+        print("   - Initializing AI Strategist...")
+        _ai_strategist = AIStrategist(llm_instance=_llm_instance, store=_vector_store)
+        print("   - ✅ AI Strategist ready.")
+
+        print("   - Initializing Support Agent...")
+        _support_agent = SupportAgent(llm_instance=_llm_instance, embedding_path=EMBEDDING_MODEL_PATH, db_path=DB_PATH)
+        print("   - ✅ Support Agent ready.")
+    except Exception as e:
+        print(f"   - ❌ FAILED to initialize core AI components: {e}")
+        traceback.print_exc()
+
+    # === LOAD SKLEARN MODELS (This part is independent of the LLM) ===
     print("   - Loading ML models from joblib files...")
     model_paths = {
         'budget': ('_budget_predictor', 'budget_predictor_v1.joblib'),
@@ -1943,15 +1940,11 @@ def generate_weekly_plan_route(request: WeeklyPlanRequest):  # <--- async hata d
 
 @app.post("/chat/creative", response_model=Dict[str, str], summary="Brainstorming chat with AI Creative Director")
 def creative_chat_endpoint(request: CreativeChatRequest):
-    """
-    Takes user message, history, and task context.
-    Returns a short, punchy, expert response using RAG + LLM.
-    """
+    if not _creative_director:
+        raise HTTPException(status_code=503, detail="AI Creative Director is not available due to a startup error.")
     try:
-        # Convert Pydantic history to list of dicts
         history_list = [m.model_dump() for m in request.history]
-        
-        response_text = director.chat(
+        response_text = _creative_director.chat(
             user_message=request.message,
             history=history_list,
             task_context=request.task_context
@@ -1959,59 +1952,19 @@ def creative_chat_endpoint(request: CreativeChatRequest):
         return {"reply": response_text}
     except Exception as e:
         print(f"🚨 Creative Chat Error: {e}")
-        raise HTTPException(status_code=500, detail="AI Director is busy.")
+        raise HTTPException(status_code=500, detail="An error occurred with the AI Director.")
 
 
 @app.post("/generate/final-from-chat", response_model=FinalScriptResponse, summary="Generates final structured script from chat history")
 def finalize_script_endpoint(request: FinalizeScriptRequest):
-    """
-    Summarizes the conversation into a shoot plan.
-    This version is ROBUST and handles messy LLM output.
-    """
+    if not _creative_director:
+        raise HTTPException(status_code=503, detail="AI Creative Director is not available due to a startup error.")
     try:
         history_list = [m.model_dump() for m in request.history]
-        
-        raw_text = director.generate_final_plan(
+        return _creative_director.generate_final_plan(
             task_context=request.task_context,
             history=history_list
         )
-        
-        print(f"   - 🤖 Raw Final Plan from LLM:\n---\n{raw_text}\n---")
-
-        # --- IDIOT-PROOF PARSING LOGIC ---
-        plan = { "hook": "", "script": "", "visuals": [], "tools": [] }
-
-        # Try to find JSON first
-        try:
-            import json
-            json_match = re.search(r'\{.*\}', raw_text, re.DOTALL)
-            if json_match:
-                parsed = json.loads(json_match.group(0))
-                plan["hook"] = parsed.get("hook", "")
-                plan["script"] = parsed.get("script", "")
-                plan["visuals"] = parsed.get("visuals", [])
-                plan["tools"] = parsed.get("tools", [])
-                
-                # Agar ek bhi cheez mil gayi to return kar do
-                if plan["hook"] or plan["script"]:
-                    return FinalScriptResponse(**plan)
-        except:
-            pass # JSON parsing fail hua to aage badho
-
-        # Fallback to Regex if no JSON found
-        hook_match = re.search(r"Hook:?\s*\"(.*?)\"", raw_text, re.IGNORECASE)
-        script_match = re.search(r"Script:?\s*\"(.*?)\"", raw_text, re.IGNORECASE)
-
-        plan["hook"] = hook_match.group(1) if hook_match else "Start with a bang!"
-        
-        # Agar script nahi mili to poora raw text hi script maan lo
-        plan["script"] = script_match.group(1) if script_match else raw_text
-        
-        plan["visuals"] = ["Close up shot", "Wide shot"]
-        plan["tools"] = ["CapCut"]
-
-        return FinalScriptResponse(**plan)
-
     except Exception as e:
         print(f"🚨 Finalize Script Error: {e}")
-        raise HTTPException(status_code=500, detail="Failed to generate final plan.")
+        raise HTTPException(status_code=500, detail="Failed to generate the final plan.")