updated imports

app.py

@@ -11,10 +11,9 @@ from pathlib import Path
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.vectorstores import Chroma
 from langchain_community.document_loaders.csv_loader import CSVLoader
-from langchain_community.document_loaders.pdf_loader import PyPDFLoader
+from langchain_community.document_loaders import PyPDFLoader
 from langchain_community.document_loaders.dataframe import DataFrameLoader
-from langchain.text_splitter import CharacterTextSplitter
-from huggingface_hub import HfApi
+from langchain_text_splitters import CharacterTextSplitter
 
 
 # ---------- Configuration ----------
@@ -28,17 +27,6 @@ _pipe = None
 _rag_vectorstore = None
 _embeddings = None
 
-HF_TOKEN = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACEHUB_API_TOKEN")
-if not HF_TOKEN:
-    print("Error: no Hugging Face token found. Set HF_TOKEN or HUGGINGFACEHUB_API_TOKEN as an environment variable or Space secret.")
-    sys.exit(1)
-else:
-    try:
-        HfApi().whoami(token=HF_TOKEN)
-        print("Hugging Face token OK")
-    except Exception as e:
-        print("Invalid Hugging Face token:", e)
-        sys.exit(1)
 
 # ---------- Lazy initialization helpers ----------
 def _init_pipeline():
@@ -68,7 +56,6 @@ def _init_pipeline():
         model=MODEL_ID,
         device_map=model_kwargs.get("device_map"),
         torch_dtype=model_kwargs.get("torch_dtype"),
-        use_auth_token=HF_TOKEN,
        **({} if "quantization_config" not in model_kwargs else {"quantization_config": model_kwargs["quantization_config"]}),
    )
     try:

requirements.txt

@@ -11,4 +11,5 @@ sentence-transformers
 pypdf
 bitsandbytes
 accelerate
-huggingface-hub
+huggingface-hub
+langchain_text_splitters