app.py

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

USE_OPENAI = True

def get_pdf_text(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 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):
    embeddings = OpenAIEmbeddings()
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore


def main():
    load_dotenv()

    st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
    st.header("Chat with multiple PDFs :books:")

    st.text_input("Ask a question about doc:")

    with st.sidebar:
        st.subheader("Your Documents")
        pdf_docs = st.file_uploader("Upload your PDFs here", accept_multiple_files=True)
        if st.button("Process"):
            with st.spinner("Processing"):
                #get pdf text
                raw_text = get_pdf_text(pdf_docs)


                #get the text chunks
                text_chunks = get_text_chunks(raw_text)


                #create vector store
                vectorstore = get_vectorstore(text_chunks)




if __name__ == '__main__':
    main()