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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 sentence_transformers import SentenceTransformer
from langchain import HuggingFaceHub
from langchain.chains.question_answering import load_qa_chain
import os
# model = SentenceTransformer('all-MiniLM-L6-v2')
with st.sidebar:
st.title('LLM PDF Chats')
st.markdown('''
## about
- This is LLM power chatbot
- By [Prathamesh Shete]('https://www.linkedin.com/in/prathameshshete')
''')
add_vertical_space(5)
st.write('Made By Prathamesh')
load_dotenv()
def main():
st.header('Chat With PDF')
pdf = st.file_uploader('Upload Your PDF',type='pdf')
if pdf is not None:
pdf_reader = PdfReader(pdf)
# st.write(pdf_reader)
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)
# st.write(chunks)
# embeddings
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)
# accept user query's
ask_query = st.text_input('Ask question about PDF : ')
if ask_query:
docs = VectorStore.similarity_search(query=ask_query, k=3)
# st.write(docs)
llm = HuggingFaceHub(repo_id="google/flan-t5-xl", 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)
# st.write(text)
if __name__ == "__main__":
main() |