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_huggingface import HuggingFaceEndpoint
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
 import tempfile
 
 # Initialize global variables
@@ -29,20 +29,19 @@ def process_pdf(file):
     retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
 
     # ✅ Wrap DeepSeek model properly
-    repo_id = "deepseek-ai/DeepSeek-R1-0528"
-
     llm = HuggingFaceEndpoint(
-        repo_id=repo_id,
-        max_new_tokens=128,
-        temperature=0.5,
-        provider="auto",  # set your provider here hf.co/settings/inference-providers
-        # provider="hyperbolic",
-        # provider="nebius",
-        # provider="together",
+        repo_id="deepseek-ai/DeepSeek-R1-0528",
+        task="text-generation",
+        max_new_tokens=512,
+        do_sample=False,
+        repetition_penalty=1.03,
+        provider="auto",  # let Hugging Face choose the best provider for you
     )
     
+    chat_model = ChatHuggingFace(llm=llm)
+    
     retrieval_chain = ConversationalRetrievalChain.from_llm(
-        llm=llm,
+        llm=chat_model,
         retriever=retriever,
         return_source_documents=True
     )