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from io import BytesIO
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_openai import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
def extract_text_from_pdf(pdf_file_obj):
pdf_reader = PdfReader(BytesIO(pdf_file_obj.getbuffer()))
text = ""
for page_num in range(len(pdf_reader.pages)):
page_obj = pdf_reader.pages[page_num]
text += page_obj.extract_text()
return text
def get_text_chunks(text):
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks):
metadatas = [{"source": f"{i}-pl"} for i in range(len(text_chunks))]
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
llm = ChatOpenAI()
memory = ConversationBufferMemory(
memory_key='chat_history', return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory
)
return conversation_chain
def handle_userinput(user_question):
response = st.session_state.conversation({'question': user_question})
st.session_state.chat_history = response['chat_history']
for i, message in enumerate(st.session_state.chat_history):
if i % 2 == 0:
st.markdown(("User: "+message.content))
else:
st.markdown(("AI: "+message.content))
def main():
if "conversation" not in st.session_state:
st.session_state.conversation = None
if "chat_history" not in st.session_state:
st.session_state.chat_history = None
if st.session_state.conversation is not None:
st.header("Ask questions from your PDF:books:")
user_question = st.chat_input("Ask a question about your documents:")
if user_question:
handle_userinput(user_question)
if st.session_state.conversation is None:
st.header("Upload your PDF here")
pdf_doc = st.file_uploader("Browse your file here",type="pdf")
if pdf_doc is not None:
with st.spinner("Processing"):
# get pdf text
raw_text = extract_text_from_pdf(pdf_doc)
# get the text chunks
text_chunks = get_text_chunks(raw_text)
# create vector store
vectorstore = get_vectorstore(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(
vectorstore)
st.rerun()
if __name__ == '__main__':
main() |