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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') |