Update utills.py

utills.py

@@ -24,7 +24,7 @@ from langchain_community.llms import llamacpp
 import streamlit as st
 
 store = {}
-@st.cache_resource
+
 def get_session_history(session_id: str):
     if session_id not in store:
         store[session_id] = ChatMessageHistory()
@@ -32,7 +32,7 @@ def get_session_history(session_id: str):
 
 
 
-@st.cache_resource
+
 def load_pdf_documents(data_path):
     try:
         document_loader = PyPDFDirectoryLoader(data_path)
@@ -42,7 +42,7 @@ def load_pdf_documents(data_path):
         return None  # or handle the error in an appropriate manner    
 
 
-@st.cache_data
+
 def load_txt_documents(data_path):
     documents = []
     for filename in os.listdir(data_path):
@@ -51,7 +51,7 @@ def load_txt_documents(data_path):
             documents.extend(TextLoader(file_path).load())
     return documents
 
-@st.cache_resource
+
 def split_docs(_documents, chunk_size, chunk_overlap):
     try:
         text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
@@ -64,7 +64,7 @@ def split_docs(_documents, chunk_size, chunk_overlap):
         print(f"Error splitting documents: {e}")
         return []  # or handle the error in an appropriate manner
 
-@st.cache_data
+
 def load_uploaded_documents(uploaded_files):
     documents = []
     for uploaded_file in uploaded_files:
@@ -79,7 +79,7 @@ def retriever_from_chroma(docs, embeddings, search_type, k):
     retriever = vectordb.as_retriever(search_type=search_type, search_kwargs={"k": k})
     return retriever
 
-@st.cache_resource
+
 def history_aware_retriever(llm, retriever, contextualize_q_system_prompt):
     try:
         contextualize_q_prompt = ChatPromptTemplate.from_messages(
@@ -97,7 +97,7 @@ def history_aware_retriever(llm, retriever, contextualize_q_system_prompt):
 
 
 
-@st.cache_resource
+
 def echo(question, history):
     ai_message = rag_chain.invoke({"input": question, "chat_history": chat_history})
     chat_history.extend([HumanMessage(content=question), ai_message["answer"]])