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import streamlit as st
from dotenv import load_dotenv
from streamlit_extras.add_vertical_space import add_vertical_space
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
import pickle
from langchain import HuggingFaceHub
from langchain.chains.question_answering import load_qa_chain
import os
def main(pdf):
st.header('Chat With PDF')
if pdf is not None:
pdf_reader = PdfReader(pdf)
text = ''
for page in pdf_reader.pages:
text += page.extract_text()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(text=text)
store_name = pdf.name[:-4]
if os.path.exists(f'{store_name}.pkl'):
with open(f'{store_name}.pkl', 'rb') as f:
VectorStore = pickle.load(f)
else:
embeddings = HuggingFaceEmbeddings()
VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
with open(f'{store_name}.pkl', 'wb') as f:
pickle.dump(VectorStore, f)
ask_query = st.text_input('Ask question about PDF: ')
if ask_query:
docs = VectorStore.similarity_search(query=ask_query, k=3)
llm = HuggingFaceHub(repo_id="Salesforce/xgen-7b-8k-base", model_kwargs={"temperature": 0, "max_length": 64})
chain = load_qa_chain(llm=llm, chain_type='stuff')
response = chain.run(input_documents=docs, question=ask_query)
st.write(response)
if __name__ == "__main__":
load_dotenv()
st.sidebar.title('LLM PDF Chats')
st.sidebar.markdown('''
## About
- This is LLM power chatbot
- By [Prathamesh Shete]('https://www.linkedin.com/in/prathameshshete')
''')
add_vertical_space(5)
st.sidebar.write('Made By Prathamesh')
pdf = st.file_uploader('Upload Your PDF', type='pdf')
main(pdf)
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