Update app.py

app.py

@@ -473,7 +473,7 @@ def retrieval_blocks(
                 persist_directory = current_dir + "/" + vectorstore_name,
                 embedding_function=embeddings
             )
-            print(prompt_template_name)
+            
             
         # 6. base retriever: Vector store-backed retriever 
         base_retriever = Vectorstore_backed_retriever(
@@ -660,14 +660,14 @@ def answer_template(language="english"):
 answer_prompt = ChatPromptTemplate.from_template(answer_template())
 
 
-
+"""
 # invoke the ChatPromptTemplate
 answer_prompt.invoke(
     {"question":"plaese give more details about DTC, including its use cases and implementation.",
      "context":[Document(page_content="DTC use cases include...")], # the context is a list of retrieved documents.
      "chat_history":memory.chat_memory}
 )
-
+"""
 
 
 
@@ -778,7 +778,7 @@ def create_ConversationalRetrievalChain(
 rephrase the follow up question to be a standalone question, in its original language.\n\n
 Chat History:\n{chat_history}\n
 Follow Up Input: {question}\n
-Standalone question:""")
+Standalone question: {question}""")
 
     # 2. Define the answer_prompt
     # Pass the standalone question + the chat history + the context (retrieved documents) to the `LLM` wihch will answer