app.py

from dotenv import load_dotenv
import os
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback

def main():
    load_dotenv()
    # print(os.getenv("OPENAI_API_KEY"))
    st.set_page_config(page_title="Ask your PDF")
    st.header("Ask your PDF")

    pdf = st.file_uploader("Upload your PDF", type='pdf')

    if pdf is not None:
        pdf_reader = PdfReader(pdf)
        text = ""
        for page in pdf_reader.pages:
            text += page.extract_text()

        #split into chunks
        text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
        chunks = text_splitter.split_text(text)
        
        # convert to embeddings
        embeddings = OpenAIEmbeddings()
        knowledge_base = FAISS.from_texts(chunks, embeddings)

        user_question = st.text_input("Ask a question about your pdf")
        if user_question:
            doocs = knowledge_base.similarity_search(user_question)
            # st.write(doocs)
            llm = OpenAI()
            chain = load_qa_chain(llm, chain_type='stuff')
            # monitor how much money spent
            with get_openai_callback() as cb:
                response = chain.run(input_documents=doocs, question=user_question)
                print(cb)
            st.write(response)




if __name__ == '__main__':
    main()