Update app.py

app.py

@@ -121,31 +121,25 @@ def get_conversation_chain(vectorstore, tokenH):
             raise ValueError(f"Error generating response: {str(e)}")
 
     # Create conversational retrieval chain without memory
-    def conversation_chain(user_input):
-        # Ensure user input is a string
-        if isinstance(user_input, dict):
-            user_input = user_input.get('question', '')
-
-        # Get the relevant documents from the vector store using retriever's query method
-        retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 5})  # Adjust the search arguments
-        documents = retriever.get_relevant_documents(user_input)
-        st.write(documents)
-
-        # Extract text from the documents (ensure they are strings)
-        # documents_text = [doc['text'] if isinstance(doc, dict) else str(doc) for doc in documents]
-        documents_text = "\n".join(doc['text'] for doc in documents)
-        messages = [{"role": "user", "content": user_input}, {"role": "system", "content": documents_text}]
-        # Prepare the messages for the InferenceClient
-        # messages = [{"role": "user", "content": user_input}]
-        assistant_response = generate_response(messages)
-
-        # Return both the assistant response and chat history
-        return {
-            "response": assistant_response,
-            "chat_history": chat_history
-        }
-
-    return conversation_chain
+   def conversation_chain(user_input):
+    # Ensure user input is a string
+    if isinstance(user_input, dict):
+        user_input = user_input.get('question', '')
+
+    # Get the relevant documents from the vector store using retriever's query method
+    retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 5})  # Adjust the search arguments
+    documents = retriever.get_relevant_documents(user_input)
+
+    # Extract text from the documents (ensure they are strings)
+    documents_text = "\n".join(doc.page_content for doc in documents)  # Use doc.page_content instead of doc['text']
+
+    # Prepare the messages for the InferenceClient
+    messages = [{"role": "user", "content": user_input}, {"role": "system", "content": documents_text}]
+    response = generate_response(messages)
+
+    # Return the response
+    return response
+
     
 def handle_userinput(user_question):
     # 대화 체인을 사용하여 사용자 질문에 대한 응답을 생성합니다.