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from dotenv import load_dotenv
import streamlit as st
import os
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
from streamlit_chat import message
os.environ["OPENAI_API_KEY"] = "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"
def main():
load_dotenv()
st.header(" LLM CHATBOT ON PFD FILES")
st.sidebar.header("Instructions")
st.sidebar.info(
'''This is a web application that allows you to interact with
your PDF Files
'''
)
st.sidebar.info('''Enter a query in the text box and press enter
to receive a response''')
st.sidebar.info('''
This project works perfectly even on your own data
''')
# st.set_page_config(page_title="Ask your PDF")
st.header("Ask your PDF files some questions 💬")
# upload file
pdf = st.file_uploader("Upload your PDF File Below", 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)
# 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)
message(response)
if __name__ == '__main__':
main()
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