File size: 2,242 Bytes
fce8396
 
 
 
 
 
 
 
 
 
 
 
9b3a839
 
 
 
fce8396
9b3a839
 
fce8396
9b3a839
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
54a5241
 
 
 
9b3a839
 
 
 
 
 
 
 
 
 
 
 
54a5241
 
 
9b3a839
54a5241
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
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
import os
import openai
from streamlit_chat import message

def openai_pdf():
    OPENAI_API_KEY = st.text_input("Input your OpenAI API key", "")
    os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
    # st.header("Ask your PDF 💬")
    
    # upload file
    pdf = st.file_uploader("Upload your PDF", type="pdf")
    
    # extract the text
    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)
      
      # create embeddings
      embeddings = OpenAIEmbeddings()
      knowledge_base = FAISS.from_texts(chunks, embeddings)
        
      # if 'generated' not in st.session_state:
          # st.session_state['generated'] = []
      # if 'past' not in st.session_state:
          # st.session_state['past'] = []
      
      # show user input
      user_question = st.text_input("Ask a question about your PDF:")
      if user_question:
        docs = knowledge_base.similarity_search(user_question)
        
        llm = OpenAI()
        chain = load_qa_chain(llm, chain_type="stuff")
        with get_openai_callback() as cb:
          response = chain.run(input_documents=docs, question=user_question)
          print(cb)
           
        st.write(response)
        # st.session_state.past.append(user_question)
        # st.session_state.generated.append(response)
          
      # if st.session_state['generated']:
        # for i in range(len(st.session_state['generated'])-1, -1, -1):
            # message(st.session_state["generated"][i], key=str(i))
            # message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')