Update secrets

crs_arena/utils.py

@@ -22,7 +22,7 @@ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 from src.model.crs_model import CRSModel
 
 # Initialize Hugging Face API
-HF_API = HfApi(token=st.secrets.hf.hf_token)
+HF_API = HfApi(token=st.secrets["hf_token"])
 
 
 @st.cache_resource(show_spinner="Loading CRS...", max_entries=5)
@@ -48,7 +48,7 @@ def get_crs_model(model_name: str, model_config_file: str) -> CRSModel:
     model_args = yaml.safe_load(open(model_config_file, "r"))
 
     if "chatgpt" in model_name:
-        openai.api_key = st.secrets.openai.api_key
+        openai.api_key = st.secrets["openai_api_key"]
 
     # Extract crs model from name
     name = model_name.split("_")[0]
@@ -56,27 +56,10 @@ def get_crs_model(model_name: str, model_config_file: str) -> CRSModel:
     return CRSModel(name, **model_args)
 
 
-def execute_sql_query(query: str, params: Dict[str, str]) -> List[Any]:
-    """Executes a SQL query with parameters.
-
-    Args:
-        query: SQL query.
-        params: Dictionary of parameters.
-
-    Returns:
-        Output of the query.
-    """
-    connection = sqlite3.connect(st.secrets.db.vote_db)
-    cursor = connection.cursor().execute(query, params)
-    output = cursor.fetchall()
-    connection.commit()
-    return output
-
-
 def download_and_extract_models() -> None:
     """Downloads the models folder from the server and extracts it."""
     logging.debug("Downloading models folder.")
-    models_url = st.secrets.files.models_folder_url
+    models_url = st.secrets["models_folder_url"]
     models_targz = "models.tar.gz"
     models_folder = "data/models/"
     try:
@@ -95,7 +78,7 @@ def download_and_extract_models() -> None:
 def download_and_extract_item_embeddings() -> None:
     """Downloads the item embeddings folder from the server and extracts it."""
     logging.debug("Downloading item embeddings folder.")
-    item_embeddings_url = st.secrets.files.item_embeddings_url
+    item_embeddings_url = st.secrets["item_embeddings_url"]
     item_embeddings_tarbz = "item_embeddings.tar.bz2"
     item_embeddings_folder = "data/"
 
@@ -134,7 +117,7 @@ async def upload_conversation_logs_to_hf(
             lambda: HF_API.upload_file(
                 path_or_fileobj=conversation_log_file_path,
                 path_in_repo=repo_filename,
-                repo_id=st.secrets.hf.dataset_repo,
+                repo_id=st.secrets["dataset_repo"],
                 repo_type="dataset",
             ),
         )