code: import os
  import shutil
  import tarfile
  import tempfile
  import time
  from pathlib import Path
  
  import arxiv
  import openai
  import pandas as pd
  import pdfplumber
  import streamlit as st
  from llama_index import (KeywordTableIndex, KnowledgeGraphIndex,
                           ServiceContext, SimpleDirectoryReader, SummaryIndex,
                           TreeIndex, VectorStoreIndex, download_loader,
                           set_global_service_context)
  from llama_index.llms import OpenAI, Xinference
  from llama_index.schema import Document
  from PIL import Image
  from st_files_connection import FilesConnection
  from xinference.client import RESTfulClient
  
  from pdfextract.export_annotation import export_annotation
  from pdfextract.pdf_extract import pdf_extract
  from texannotate.annotate_file import annotate_file
  from texannotate.color_annotation import ColorAnnotation
  from texcompile.client import compile_pdf_return_bytes
  from utils.utils import (find_latex_file, postprocess_latex, preprocess_latex,
                           tup2str)
  
  st.set_page_config(page_title='Chat with arXiv paper without PDF noise, powered by LaTeX Rainbow.', layout="wide")
  texcompile_host = st.secrets.texcompile_host
  texcompile_port = st.secrets.texcompile_port
  
  def main():
      """
      The main function for the Streamlit app.
  
      :return: None.
      """
      st.title("Chat with arXiv paper, without PDF noise")
      st.sidebar.markdown('# Github link: [LaTeX Rainbow](https://github.com/InsightsNet/texannotate)')
      st.sidebar.markdown("""<small>It's always good practice to verify that a website is safe before giving it your API key. 
                          This site is open source, so you can check the code yourself, or run the streamlit app locally.</small>""", unsafe_allow_html=True)
      col1, col2 = st.columns([1, 0.8], gap='medium')
      with col2:
          with st.form("my_form"):
              api_key = st.text_input("Enter OpenAI API key here.", type='password')
              arxiv_id = st.text_input("Please enter a arXiv paper id:", value='1601.00978')
              submitted = st.form_submit_button("Submit and process arXiv paper (click once and wait)")
              if submitted:
                  process_submit_button(col1, col2, arxiv_id, api_key)
                  index = load_data()
                  st.session_state["index"] = index
      if 'index' in st.session_state:
          if "imgs" in st.session_state.keys():
              with col1.container():
                  for img in st.session_state["imgs"]:
                      st.image(img)
  
          chat_engine = st.session_state["index"].as_chat_engine(chat_mode="condense_question", verbose=True)
  
          if "messages" not in st.session_state.keys(): # Initialize the chat message history
              st.session_state.messages = [
                  {"role": "assistant", "content": "Ask me a question about the paper!"}
              ]
  
          if prompt := st.chat_input("Your question"): # Prompt for user input and save to chat history
              st.session_state.messages.append({"role": "user", "content": prompt})
          
          for message in st.session_state.messages: # Display the prior chat messages
              with st.chat_message(message["role"]):
                  st.write(message["content"])
  
          # If last message is not from assistant, generate a new response
          if st.session_state.messages[-1]["role"] != "assistant":
              with st.chat_message("assistant"):
                  with st.spinner("Thinking..."):
                      response = chat_engine.chat(prompt)
                      st.write(response.response)
                      message = {"role": "assistant", "content": response.response}
                      st.session_state.messages.append(message) # Add response to message history
  
  
  def process_submit_button(col1, col2, arxiv_id, api_key):
      with col2:
          with st.spinner("Downloading LaTeX code..."):
              filename = validate_input(arxiv_id)
              if not filename:
                  st.error("id not found on arXiv, or the paper doesn't contain LaTeX code.")
                  return
  
          with st.spinner("Annotating LaTeX code... please wait..."):
              df_toc, df_data = extract_file(filename, col1)
              df_data.to_csv('data.csv', sep='\t')
  
          with st.spinner("Loading llm..."):
              if api_key == '':
                  st.error('Please set your OpenAI key.')
              if api_key == 'local':
                  set_local_llm()
              else:
                  openai.api_key = api_key
                  set_openai_llm()
  
          st.info("Now you get a cleaned PDF. Only colored part are penned by paper author. Extracted text are resorted by the reading order.", icon="📃")
  
  
  @st.cache_resource(show_spinner=True)
  def load_data():
      df_data = pd.read_csv('data.csv', sep='\t')
      text = ''
      section_id = 0
      df_data.index.name='myindex'
      for i, row in df_data[df_data['reading_order']!=-1].sort_values(by=['reading_order', 'myindex']).iterrows():
          if row['section_id'] > section_id:
              text += '\n'
              section_id = row['section_id']
          if row['label'] != 'Figure':
              text += row['token'] + ' '
      sections =  text.split('\n')
      docs = [Document(text=section) for section in sections]
      with st.spinner(text="Loading and indexing the paper - hang tight! This should take 1-2 minutes."):
          index = VectorStoreIndex.from_documents(docs)
          return index
      
  
  def validate_input(arxiv_id):
      try:
          paper = next(arxiv.Search(id_list=[arxiv_id]).results())
          filename = paper.download_source()
          return filename    
      except:
          return False
  
  def extract_file(filename, col1):
      with col1:
          placeholder = st.empty()
          st.session_state['imgs'] = []
          try:
              Path("tmp").mkdir(parents=True, exist_ok=True)
              td = 'tmp'
              #print('temp dir', td)
              with tarfile.open(filename ,'r:gz') as tar:
                  tar.extractall(td)
                  preprocess_latex(td)
  
              basename, pdf_bytes = compile_pdf_return_bytes(
                  sources_dir=td,
                  host=texcompile_host, 
                  port=texcompile_port
              ) # compile the unmodified latex firstly
              with placeholder.container():
                  for page in pdfplumber.open(pdf_bytes).pages:
                      image = page.to_image(resolution=300).original
                      st.image(image)
  
              shapes, tokens = pdf_extract(pdf_bytes)
              ## get colors
              color_dict = ColorAnnotation()
              for rect in shapes:
                  color_dict.add_existing_color(tup2str(rect['stroking_color']))
              for token in tokens:
                  color_dict.add_existing_color(token['color'])
              shutil.rmtree(td)
              Path("tmp").mkdir(parents=True, exist_ok=True)
  
              with tarfile.open(filename ,'r:gz') as tar:
                  tar.extractall(td)
              tex_file = Path(find_latex_file(Path(basename).stem, basepath=td)).name
              annotate_file(tex_file, color_dict, latex_context=None, basepath=td)
              postprocess_latex(str(Path(find_latex_file(Path(basename).stem, basepath=td))))
              basename, pdf_bytes_mod = compile_pdf_return_bytes(
                  sources_dir=td,
                  host=texcompile_host, 
                  port=texcompile_port
              ) # compile the modified latex
              placeholder.empty()
              with placeholder.container():
                  for page in pdfplumber.open(pdf_bytes_mod).pages:
                      image = page.to_image(resolution=300).original
                      st.image(image)
              shapes, tokens = pdf_extract(pdf_bytes_mod)
              df_toc, df_data = export_annotation(shapes, tokens, color_dict)
              shutil.rmtree(td)
  
              colors = {
                  "Abstract":(255, 182, 193), "Author":(0, 0, 139), "Caption":(57, 230, 10),
                  "Equation":(255, 0, 0),"Figure":(230, 51, 249),"Footer":(255, 255, 255),
                  "List":(46, 33, 109),"Paragraph":(181, 196, 220),"Reference":(81, 142, 32),
                  "Section":(24, 14, 248),"Table":(129, 252, 254),"Title":(97, 189, 251)
              }
              imgs = []
              placeholder.empty()
              with placeholder.container():
                  for i, page in enumerate(pdfplumber.open(pdf_bytes).pages):
                      image = page.to_image(resolution=300)
                      for _, rect in df_data.iterrows():
                          if rect['page'] == i+1:
                              color = colors.get(rect['label'], (0,0,0))
                              image.draw_rect((rect['x0'], rect['y0'], rect['x1'], rect['y1']), fill=(color[0],color[1],color[2],70), stroke=color, stroke_width=1)
                      imgs.append(image.annotated)
                      st.image(image.annotated)
              st.session_state['imgs'] = imgs
              return df_toc, df_data
  
          except Exception as e:
              raise e
              #st.error("LaTeX code parsing error, please follow LaTeX Rainbow's example to add new parsing rules.")
              return None, None
  
  def set_local_llm():
      port = 9997  # replace with your endpoint port number
      client = RESTfulClient(f"http://localhost:{port}")
  
      # Download and Launch a model, this may take a while the first time
      model_uid = client.launch_model(
          model_name="llama-2-chat",
          model_size_in_billions=7,
          model_format="pytorch",
          quantization="none",
      )
  
      # Initiate Xinference object to use the LLM
      llm = Xinference(
          endpoint=f"http://localhost:{port}",
          model_uid=model_uid,
          temperature=0.5,
          max_tokens=512,
      )
      service_context = ServiceContext.from_defaults(
          llm=llm, embed_model="local:BAAI/bge-small-en"
      )
      set_global_service_context(service_context)
  
  
  def set_openai_llm():
      service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-3.5-turbo", temperature=0.5, system_prompt="You are an expert on the paper and your job is to answer technical questions. Keep your answers precise and based on facts – do not hallucinate features."))
      set_global_service_context(service_context)
  
  
  if __name__ == '__main__':
      main()
apis: llama_index.VectorStoreIndex.from_documents llama_index.schema.Document llama_index.ServiceContext.from_defaults llama_index.llms.OpenAI llama_index.set_global_service_context llama_index.llms.Xinference
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