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import os
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
def extract_text_from_pdfs(pdf_docs):
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
def split_text_into_chunks(text):
text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
return text_splitter.split_text(text)
def create_vector_store_from_text_chunks(text_chunks):
key = os.getenv('OPENAI_KEY')
embeddings = OpenAIEmbeddings(openai_api_key=key)
return FAISS.from_texts(texts=text_chunks, embedding=embeddings)
def create_conversation_chain(vectorstore):
llm = ChatOpenAI()
memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)
def process_user_input(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):
template = user_template if i % 2 == 0 else bot_template
st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
def main():
load_dotenv()
st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
st.write(css, unsafe_allow_html=True)
st.header("Chat with multiple PDFs :books:")
user_question = st.text_input("Ask a question about your documents:")
if user_question:
process_user_input(user_question)
with st.sidebar:
st.subheader("Your documents")
pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
if st.button("Process"):
with st.spinner("Processing"):
raw_text = extract_text_from_pdfs(pdf_docs)
text_chunks = split_text_into_chunks(raw_text)
vectorstore = create_vector_store_from_text_chunks(text_chunks)
st.session_state.conversation = create_conversation_chain(vectorstore)
if __name__ == '__main__':
main()
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