fix: Correct Hugging Face config (sdk, python_version, app_file)

README.md

@@ -1,11 +1,13 @@
 ---
 title: Reachify Ai Service
-emoji: 🌖
+emoji: 🚀
 colorFrom: indigo
-colorTo: indigo
-sdk: docker
+colorTo: purple
+sdk: python
+python_version: 3.11
+app_file: api/main.py
 pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

api/main.py

@@ -169,13 +169,85 @@ app = FastAPI(title="Reachify AI Service (Deploy-Ready)", version="11.0.0")
 
 @app.on_event("startup")
 def startup_event():
-    """
-    BARE MINIMUM STARTUP FOR DEBUGGING.
-    This function does nothing except print a message.
-    If the server starts with this, we know the problem is in the original startup logic.
-    """
-    print("--- ✅ FINAL TEST: Bare minimum startup. If this works, the server is alive. ---")
-    pass
+    # 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
+    
+    # --- STEP 1: DOWNLOAD AND LOAD THE LLM MODEL ---
+    print("--- 🚀 AI Service Starting Up... ---")
+    try:
+        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}' from '{MODEL_REPO}'...")
+            hf_hub_download(
+                repo_id=MODEL_REPO,
+                filename=MODEL_FILENAME,
+                local_dir=MODEL_SAVE_DIRECTORY,
+                local_dir_use_symlinks=False
+            )
+            print("   - ✅ Model downloaded successfully.")
+        else:
+            print(f"   - LLM model found locally at {LLAMA_MODEL_PATH}. Skipping download.")
+        
+        # === LLM LOADING IS NOW ENABLED ===
+        print("   - Loading Llama LLM into memory...")
+        _llm_instance = Llama(model_path=LLAMA_MODEL_PATH, n_gpu_layers=0, n_ctx=2048, verbose=False)
+        print("   - ✅ LLM Loaded successfully.")
+    
+    except Exception as e:
+        print(f"   - ❌ FATAL ERROR: Could not download or load the LLM model. This could be due to a memory limit. LLM features will be disabled.")
+        traceback.print_exc()
+        _llm_instance = None # Ensure global variable is None on failure
+
+    # --- STEP 2: INITIALIZE ALL AI COMPONENTS THAT NEED THE LLM ---
+    if _llm_instance:
+        try:
+            print("   - Initializing AI components that depend on LLM...")
+            
+            _creative_director = CreativeDirector(llm_instance=_llm_instance)
+            if VectorStore:
+                _vector_store = VectorStore()
+                print("   - RAG Engine Ready.")
+            _ai_strategist = AIStrategist(llm_instance=_llm_instance, store=_vector_store)
+            _support_agent = SupportAgent(llm_instance=_llm_instance, embedding_path=EMBEDDING_MODEL_PATH, db_path=DB_PATH)
+            
+            print("   - ✅ Core AI components (Director, Strategist, Agent) are online.")
+
+        except Exception as e:
+            print(f"   - ❌ FAILED to initialize core AI components: {e}")
+            traceback.print_exc()
+    else:
+        print("   - ⚠️ SKIPPING initialization of LLM-dependent components because LLM failed to load.")
+
+    # --- STEP 3: LOAD ALL OTHER MODELS (These don't depend on the LLM) ---
+    print("   - Loading ML models from joblib files...")
+    model_paths = {
+        'budget': ('_budget_predictor', 'budget_predictor_v1.joblib'),
+        'matcher': ('_influencer_matcher', 'influencer_matcher_v1.joblib'),
+        'performance': ('_performance_predictor', 'performance_predictor_v1.joblib'),
+        'payout': ('_payout_forecaster', 'payout_forecaster_v1.joblib'),
+        'earnings': ('_earnings_optimizer', 'earnings_model.joblib'),
+        'earnings_encoder': ('_earnings_encoder', 'earnings_encoder.joblib'),
+        'likes_predictor': ('_likes_predictor', 'likes_predictor_v1.joblib'),
+        'comments_predictor': ('_comments_predictor', 'comments_predictor_v1.joblib'),
+        'revenue_forecaster': ('_revenue_forecaster', 'revenue_forecaster_v1.joblib'),
+        'performance_scorer': ('_performance_scorer', 'performance_scorer_v1.joblib'),
+    }
+    for name, (var, file) in model_paths.items():
+        path = os.path.join(MODELS_DIR, file)
+        try:
+            globals()[var] = joblib.load(path)
+            print(f"     - Loaded {name} model.")
+        except FileNotFoundError:
+            globals()[var] = None
+            print(f"   - ⚠️ WARNING: Model '{name}' not found at {path}. Endpoint will be disabled.")
+    
+    print("   - Initializing Text Embedding Model...")
+    load_embedding_model(EMBEDDING_MODEL_PATH)
+
+    print("\n--- ✅ AI Service startup sequence finished! ---")
     
 @app.get("/")
 def health_check():