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import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import HuggingFaceHub
from html_template import css, bot_template, user_template
def get_pdf_text(pdf_docs):
text = ''
for pdf in pdf_docs:
reader = PdfReader(pdf)
for page in reader.pages:
text += page.extract_text()
return text
def get_text_chuks(raw_text):
text_splitter = CharacterTextSplitter(
separator = '\n',
chunk_size = 1000,
chunk_overlap = 200,
length_function = len
)
chunks = text_splitter.split_text(raw_text)
return chunks
def get_vector_store(text_chunks):
# embeddings = OpenAIEmbeddings()
embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
vector_store = FAISS.from_texts(text_chunks, embeddings)
return vector_store
def get_conversation_chain(vectorstore):
llm = ChatOpenAI(temperature=0.2)
# llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.2, "max_length":512})
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory,
# retriever_kwargs={"k": 1},
)
return conversation_chain
def handle_user_question(user_question):
response = st.session_state.conversation({"question": user_question})
st.session_state.chat_history = response['chat_history']
for i, message in enumerate(st.session_state.chat_history):
if i % 2 == 0:
st.write(user_template.replace(
"{{MSG}}", message.content), unsafe_allow_html=True)
else:
st.write(bot_template.replace(
"{{MSG}}", message.content), unsafe_allow_html=True)
def main():
load_dotenv()
st.set_page_config(page_title='Chat with your PDFs', page_icon='📂', layout='wide')
st.header('Chat with multiple PDFs :books:')
# st.write(bot_template.replace('{{MSG}}', 'hello user'), unsafe_allow_html=True)
# st.write(user_template.replace('{{MSG}}', 'hello bot'), unsafe_allow_html=True)
st.write(css, unsafe_allow_html=True)
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
with st.sidebar:
st.subheader('Document')
pdf_docs = st.file_uploader('Upload your PDFs here and click on Process', accept_multiple_files=True)
if st.button('Process'):
with st.spinner('Processing...'):
# get pdf text
raw_text = get_pdf_text(pdf_docs)
# get the text chunks
text_chunks = get_text_chuks(raw_text)
# create vector store
vectorstore = get_vector_store(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(vectorstore)
user_question = st.text_input('Ask a question about your pdf')
if user_question:
handle_user_question(user_question)
if __name__ == '__main__':
main()
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