new cache

app.py

@@ -6,10 +6,11 @@ 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.experimental_singleton
+@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():
+    return HuggingFaceHub(repo_id="google/flan-t5-small", model_kwargs={"temperature":0,"max_length":200}, huggingfacehub_api_token="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") # type: ignore
+@st.cache_resource
+def qa():
     pdf_folder_path = "./PDFfiles"
     loader = PyPDFDirectoryLoader(pdf_folder_path)
     docs = loader.load()
@@ -17,8 +18,18 @@ 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
     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)
+    return qa
+def main():
+    # pdf_folder_path = "./PDFfiles"
+    # loader = PyPDFDirectoryLoader(pdf_folder_path)
+    # docs = loader.load()
+    # text_splitter = RecursiveCharacterTextSplitter (chunk_size=1000, chunk_overlap=200)
+    # 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
+    # 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)
     result = qa({"query": query})