Update app.py

app.py

@@ -5,7 +5,7 @@ from langchain.vectorstores import FAISS
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.document_loaders import PyPDFLoader
-from langchain_community.llms import HuggingFaceHub
+from langchain_huggingface import HuggingFaceEndpoint
 import tempfile
 
 # Initialize global variables
@@ -29,9 +29,10 @@ def process_pdf(file):
     retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
 
     # ✅ Wrap DeepSeek model properly
-    llm = HuggingFaceHub(
+    llm = HuggingFaceEndpoint(
         repo_id="deepseek-ai/DeepSeek-R1-0528",
-        model_kwargs={"temperature": 0.7, "max_new_tokens": 512}
+        temperature=0.7,
+        max_new_tokens=512
     )
 
     retrieval_chain = ConversationalRetrievalChain.from_llm(
@@ -60,7 +61,7 @@ def respond(
     # Reformat history for LangChain
     chat_history = [(h["content"], h.get("response", "")) for h in history if h["role"] == "user"]
 
-    result = retrieval_chain({"question": message, "chat_history": chat_history})
+    result = retrieval_chain.invoke({"question": message, "chat_history": chat_history})
 
     return result["answer"]