I received the error

AttributeError: 'bytes' object has no attribute 'seek'

when trying to upload a test PDF on the streamlit page. My code can be found below. Thank you!

import streamlit as st
from pypdf import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os

from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai
from langchain_community.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv

load_dotenv()

genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

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=RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
    chunks=text_splitter.split_text(text)
    return chunks

def get_vector_store(text_chunks):
    embeddings=GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    vector_store=FAISS.from_texts(text_chunks, embedding=embeddings)
    vector_store.save_local("faiss_index")

def get_conversational_chain():
    prompt_template="""
    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
    the provided context just say, "answer is not available in the context", don't provide the wrong answer nn
    Context:n {context}?n
    Question:n{queestion}n

    Answer:
    """

    model=ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)

    prompt=PromptTemplate(template=prompt_template, input_variables=["context", "question"])
    chain=load_qa_chain(model, chain_type="stuff", prompt=prompt)
    return chain

def user_input(user_question):
    embeddings=GoogleGenerativeAIEmbeddings(model="models/embedding-001")

    new_db = FAISS.load_local("faiss_index", embeddings)
    docs = new_db.similarity_search(user_question)

    chain = get_conversational_chain


    response = chain(
        {"input_documents":docs, "question": user_question}
        , return_only_outputs=True
    )

    print(response)
    st.write("Reply: ", response["output_text"])

def main():
    st.set_page_config("Chat With Multiple PDFs")
    st.header("Chat with Multiple PDFs using Gemini ????‍♀️")

    user_question = st.text_input("Ask a Question from the PDF Files")

    if user_question:
        user_input(user_question)

    with st.sidebar:
        st.title("Menu:")
        pdf_docs = st.file_uploader("Upload your PDF Files and Click on Submit & Process")
        if st.button("Submit & Process"):
            with st.spinner("Processing..."):
                raw_text = get_pdf_text(pdf_docs)
                text_chunks = get_text_chunks(raw_text)
                get_vector_store(text_chunks)
                st.success("Done")

if __name__ == "__main__":
    main()
    

I was originally using PyPdf2 but changed to pypdf as I heard it may have caused this issue. I expected and hoped it was simple fix.