Add caching

PDF_Reader.py

@@ -2,7 +2,9 @@ import PyPDF2
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import HuggingFaceBgeEmbeddings
 from langchain.vectorstores import FAISS
+import streamlit as st
 
+@st.cache_data
 def read_pdf(uploaded_file):
     pdf_reader = PyPDF2.PdfReader(uploaded_file)
     text = ""
@@ -10,6 +12,7 @@ def read_pdf(uploaded_file):
         text += page.extract_text()
     return text
 
+@st.cache_data
 def Chunks(docs):
     text_splitter = RecursiveCharacterTextSplitter(
     # Set a really small chunk size, just to show.
@@ -19,7 +22,7 @@ def Chunks(docs):
     doc = text_splitter.split_text(docs)
     return doc
 
-
+@st.cache_resource
 def PDF_4_QA(file):
     content = read_pdf(file)
     pdf_chunks = Chunks(docs=content)

QA_Bot.py

@@ -2,8 +2,7 @@ import streamlit as st
 from QnA import Q_A
 import re,time
 
-
-def QA_Bot(vectorstore):
+def QA_Bot(_vectorstore):
     st.title("Q&A Bot")
     # Initialize chat history
     if "messages" not in st.session_state:
@@ -20,8 +19,7 @@ def QA_Bot(vectorstore):
         st.chat_message("user").markdown(prompt)
         # Add user message to chat history
         st.session_state.messages.append({"role": "user", "content": prompt})
-
-        ai_response = Q_A(vectorstore,prompt)
+        ai_response = ai_proc(_vectorstore, prompt)
         response = f"Echo: {ai_response}"
         # Display assistant response in chat message container
         with st.chat_message("assistant"):
@@ -29,9 +27,12 @@ def QA_Bot(vectorstore):
             full_response = ""
             for chunk in re.split(r'(\s+)', response):
                 full_response += chunk + " "
-                time.sleep(0.01)
 
                 # Add a blinking cursor to simulate typing
                 message_placeholder.markdown(full_response + "▌")
         # Add assistant response to chat history
         st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+@st.cache_resource
+def ai_proc(_vectorstore, prompt):
+    return Q_A(_vectorstore, prompt)

app.py

@@ -36,9 +36,7 @@ def main():
         )
 
     uploaded_file = st.sidebar.file_uploader("Choose a PDF file", type="pdf")
-    print("upload file before")
     if uploaded_file is not None:
-        print("upload file not none")
         # profiler = cProfile.Profile()
         # profiler.enable()
         st.sidebar.success("File uploaded successfully.")