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import streamlit as st
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
# from langchain.llms import HuggingFaceHub
from streamlit_chat import message
def get_pdf_text(pdfs):
text=""
for pdf in pdfs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text+= page.extract_text()
return text
def get_text_chunks(text):
text_splitter = CharacterTextSplitter(separator="\n",
chunk_size=1000, chunk_overlap = 200, length_function=len)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks):
embeddings = OpenAIEmbeddings()
# embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
# llm = HuggingFaceHub(repo_id="google/flan-t5-xxl")
llm = ChatOpenAI()
memory = ConversationBufferMemory(
memory_key='chat_history', return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory
)
return conversation_chain
def user_input(user_question):
response = st.session_state.conversation({'question':user_question})
st.session_state.chat_history = response['chat_history']
for i, messages in enumerate(st.session_state.chat_history):
if i % 2 == 0:
message(messages.content, is_user=True)
else:
message(messages.content)
def main():
load_dotenv()
st.set_page_config(page_title="PDF Copilot 🚀")
if "conversation" not in st.session_state:
st.session_state.conversation = None
if "chat_history" not in st.session_state:
st.session_state.chat_history = None
st.header("PDF Copilot 🚀")
user_question = st.text_input("Ask a question about your documents...")
if user_question:
user_input(user_question)
with st.sidebar:
st.subheader("Your Documents")
pdfs = st.file_uploader("Upload here", accept_multiple_files=True)
if st.button("Process"):
with st.spinner("Processing"):
raw_text = get_pdf_text(pdfs)
# print(raw_text)
chunks = get_text_chunks(raw_text)
vectorstore = get_vectorstore(chunks)
st.session_state.conversation = get_conversation_chain(vectorstore)
st.success("Processing Complete !")
if __name__ == '__main__':
main() |