code: import arxiv
  import openai
  import langchain
  import pinecone
  from langchain_community.document_loaders import ArxivLoader
  from langchain.docstore.document import Document
  from langchain.text_splitter import RecursiveCharacterTextSplitter
  from langchain.embeddings.openai import OpenAIEmbeddings
  from langchain.vectorstores import Pinecone
  from langchain.llms import OpenAI
  from langchain import PromptTemplate
  from langchain.chat_models import ChatOpenAI
  from langchain.chains.summarize import load_summarize_chain
  from langchain.chains.question_answering import load_qa_chain
  from langchain import OpenAI
  from utils import *
  import streamlit as st
  
  import os
  from dotenv import load_dotenv
  load_dotenv()
  
  openai_api_key = os.getenv('OPENAI_API_KEY')
  pinecone_api_key = os.getenv('PINECONE_API_KEY')
  environment = os.getenv('PINECONE_ENV')
  
  llm_summary = ChatOpenAI(temperature=0.3, model_name="gpt-3.5-turbo-0125")    
  llm = OpenAI(model_name="gpt-3.5-turbo-0125", temperature=0.6, api_key=openai_api_key)
  
  if 'summary' not in st.session_state:
      st.session_state.summary = None
  
  if 'documents' not in st.session_state:
      st.session_state.documents = None
  
  
  st.title('Arxiv Paper Summarizer and Interactive Q&A')
  
  paper_id_input = st.text_input('Enter Arxiv Paper ID', '')
  
  if st.button('Summarize Paper') and paper_id_input:
      with st.spinner('Fetching and summarizing the paper...'):
          try:
              doc = arxiv_loader(paper_id=paper_id_input)
              st.session_state.documents = chunk_data(docs=doc)
  #            st.write(st.session_state.documents)
              chain = load_summarize_chain(
                  llm=llm_summary,
                      chain_type='map_reduce',
                      verbose=False
                      )
              summary = chain.run(st.session_state.documents)
              st.subheader('Summary')
              st.write(summary)
          except Exception as e:
              st.error(f"An error occurred: {e}")
              
  
  def initialize_index(index_name='arxiv-summarizer'):
  #    documents = chunk_data(docs=doc)
      embeddings = OpenAIEmbeddings(api_key=openai_api_key)
      index_name = index_name
      # Make sure environment is correctly spelled (there was a typo in your provided code)
      pinecone.Pinecone(
          api_key=pinecone_api_key,
          environment=environment
      )
      if st.session_state.documents:
          index = Pinecone.from_documents(st.session_state.documents, embeddings, index_name=index_name)
      else:
          index = None
      return index
  
  index = initialize_index()
  
  def retrieve_query(query, k=2):
      matching_results = index.similarity_search(query, k=k)
      return matching_results
  
  def retrieve_answers(query):
      chain = load_qa_chain(llm, chain_type='stuff')
      doc_search = retrieve_query(query)
      print(doc_search)
      response = chain.run(input_documents=doc_search, question=query)
      return response
  
  if paper_id_input:
      user_query = st.text_input("Ask a question about the paper:", '')
      
      if user_query:
          if st.button('Get Answer'):
              with st.spinner('Retrieving your answer...'):
                  try:
                      answer = retrieve_answers(user_query)
                      st.subheader('Answer')
                      st.write(answer)
                  except Exception as e:
                      st.error(f"An error occurred while retrieving the answer: {e}")
  
  
  
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