is this it?

config/settings.py

@@ -4,38 +4,38 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
+
 class Settings:
     # Base Paths
     BASE_DIR = Path(__file__).parent.parent
-    
+
     # Deployment Mode
     DEPLOYMENT_MODE = os.getenv('DEPLOYMENT_MODE', 'local')
-    
+
     # API Keys
     OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
     HF_TOKEN = os.getenv("HF_TOKEN")
-    
+
     # HuggingFace Settings
     HF_SPACE = "SongLift/LyrGen2"
     HF_DATASET = "SongLift/LyrGen2_DB"
-    
+
     # Local Settings
     LYRICS_DIR = BASE_DIR / "data" / "raw" / "lyrics"
     EMBEDDINGS_DIR = BASE_DIR / "data" / "processed" / "embeddings"
 
-    
     # Model Settings
     EMBEDDING_MODEL = "text-embedding-ada-002"
     LLM_MODEL = "gpt-4"
-    
+
     # ChromaDB Settings
     CHROMA_COLLECTION_NAME = "lyrics_v1"
-    
+
     @classmethod
     def is_huggingface(cls) -> bool:
         """Check if running in HuggingFace environment"""
         return cls.DEPLOYMENT_MODE == 'huggingface'
-    
+
     @classmethod
     def get_embeddings_path(cls) -> Path:
         """Get the base embeddings path"""
@@ -43,31 +43,31 @@ class Settings:
             # In HuggingFace, first check the dataset cache
             data_dir = Path("/data")
             print(f"\nSearching for embeddings in: {data_dir}")
-            
+
             # Look for the most recent snapshot directory containing chroma
             snapshot_pattern = "**/datasets--*--*/snapshots/*/chroma"
             print(f"Using search pattern: {snapshot_pattern}")
-            
+
             snapshots = list(data_dir.glob(snapshot_pattern))
             print(f"Found {len(snapshots)} potential snapshot directories:")
             for snap in snapshots:
                 print(f"- {snap} (Modified: {snap.stat().st_mtime})")
-            
+
             if snapshots:
                 chosen_path = max(snapshots, key=lambda p: p.stat().st_mtime)
                 print(f"Selected most recent: {chosen_path}")
                 return chosen_path
-            
+
             print("No snapshots found, using fallback location")
             fallback_path = data_dir / "processed/embeddings"
             print(f"Fallback path: {fallback_path}")
             return fallback_path
-        
+
         # Local: Use project-relative path
         embeddings_path = cls.BASE_DIR / "data" / "processed" / "embeddings"
         print(f"Local embeddings path: {embeddings_path}")
         return embeddings_path
-    
+
     @classmethod
     def get_chroma_path(cls) -> Path:
         """Get the Chroma DB path"""
@@ -76,14 +76,14 @@ class Settings:
             return cls.get_embeddings_path()
         # Local: Use subdirectory
         return cls.get_embeddings_path() / "chroma"
-    
+
     @classmethod
     def ensure_embedding_paths(cls) -> None:
         """Ensure all embedding-related directories exist"""
         if not cls.is_huggingface():  # Only create directories locally
             cls.get_embeddings_path().mkdir(parents=True, exist_ok=True)
             cls.get_chroma_path().mkdir(parents=True, exist_ok=True)
-    
+
     @classmethod
     def get_chroma_settings(cls) -> dict:
         """Get ChromaDB settings"""
@@ -93,11 +93,12 @@ class Settings:
             "persist_directory": str(chroma_path),
             "collection_name": cls.CHROMA_COLLECTION_NAME
         }
-    
+
     @classmethod
     def debug_openai_key(cls) -> None:
         """Print debug information about OpenAI API key"""
         if cls.OPENAI_API_KEY:
-            print(f"OpenAI API Key is set. Length: {len(cls.OPENAI_API_KEY)} characters.")
+            print(
+                f"OpenAI API Key is set. Length: {len(cls.OPENAI_API_KEY)} characters.")
         else:
-            print("OpenAI API Key is NOT set.")
+            print("OpenAI API Key is NOT set.")

src/generator/generator.py

@@ -53,10 +53,11 @@ class LyricGenerator:
     def _create_embeddings_with_retry(self):
         """Create OpenAI embeddings with retry logic"""
         try:
+            api_key = Settings.OPENAI_API_KEY.strip()  # Clean the key
             return OpenAIEmbeddings(
-                openai_api_key=Settings.OPENAI_API_KEY,
-                timeout=60,  # Increase timeout
-                openai_proxy=None  # Try with no proxy
+                openai_api_key=api_key,
+                timeout=60,
+                openai_proxy=None
             )
         except Exception as e:
             print(f"Error creating embeddings: {type(e).__name__}: {str(e)}")