Update app.py

app.py

@@ -16,7 +16,6 @@ client = Client("on1onmangoes/CNIHUB101324v10", hf_token=HF_TOKEN)
 # Update the conversation history within the function.
 # Return the updated history along with any other required outputs.
 
-#@spaces.GPU()
 def stream_chat_with_rag(
     message: str,
     history: list,
@@ -33,30 +32,12 @@ def stream_chat_with_rag(
     print(f"Message: {message}")
     print(f"History: {history}")
 
-    # OG CODE DELETE
-    # # Add the knowledge Index or VectorStore, RERANKER, 
-    # knowledge_index = vectorstore
-    # reranker = RERANKER
     
     # Build the conversation prompt including system prompt and history
     conversation = system_prompt + "\n\n" + "For Client:" + client_name
     for user_input, assistant_response in history:
         conversation += f"User: {user_input}\nAssistant: {assistant_response}\n"
     conversation += f"User: {message}\nAssistant:"
-
-    # Optionally, if your `answer_with_rag` function or LLM supports context, you can include the conversation
-    # Since you prefer not to modify `answer_with_rag`, we'll proceed with the message as is
-     # OG CODE DELETE
-    # # Call `answer_with_rag` to get the final answer
-    # answer, relevant_docs = answer_with_rag(
-    #     question=message,
-    #     knowledge_index=knowledge_index,
-    #     reranker=reranker,
-    #     num_retrieved_docs=num_retrieved_docs,
-    #     num_docs_final=num_docs_final,
-    #     client_name=client_name,
-    # )
-
     
     answer, relevant_docs = client.predict(question=question, api_name="/answer_with_rag")
     # debug 092624