cache added

app.py

@@ -6,7 +6,9 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chains.question_answering import load_qa_chain
 import streamlit as st
 from langchain.chains import RetrievalQA
-
+@st.cache_resource
+def llm():
+ return HuggingFaceHub(repo_id="google/flan-t5-small", model_kwargs={"temperature":0,"max_length":200}, huggingfacehub_api_token="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") # type: ignore
 def main():
     pdf_folder_path = "./PDFfiles"
     loader = PyPDFDirectoryLoader(pdf_folder_path)
@@ -15,7 +17,7 @@ def main():
     texts = text_splitter.split_documents(docs)
     embeddings = HuggingFaceEmbeddings()
     vectordb = Chroma.from_documents(documents=texts,embedding=embeddings)
-    llm = HuggingFaceHub(repo_id="google/flan-t5-small", model_kwargs={"temperature":0,"max_length":200}, huggingfacehub_api_token="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") # type: ignore
+    # llm = HuggingFaceHub(repo_id="google/flan-t5-small", model_kwargs={"temperature":0,"max_length":200}, huggingfacehub_api_token="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") # type: ignore
     qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff",retriever=vectordb.as_retriever(search_type="mmr", search_kwargs={'fetch_k': 30}), return_source_documents=True)
     query = st.text_input("Ask a question: ")
     qa(inputs=query,return_only_outputs=True)