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import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmltemp import css, bot_template, user_template
from langchain.llms import HuggingFaceHub
def main():
load_dotenv()
st.set_page_config(page_title="PDF Chatbot", page_icon="📚")
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
st.header("Chat with your PDFs 📚")
user_question = st.text_input("Ask a question about your documents:")
if user_question:
handle_userinput(user_question)
with st.sidebar:
st.sidebar.info("""Note: I haven't used any GPU for this project so It can take
long time to process large PDFs. Also this is POC project and can be easily upgraded
with better model and resources. """)
st.subheader("Your PDFs")
pdf_docs = st.file_uploader(
"Upload your PDFs here", 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_chunks(raw_text)
# create vector store
vectorstore = get_vectorstore(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(vectorstore)
def get_pdf_text(pdf_docs):
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
def get_text_chunks(text):
text_splitter = RecursiveCharacterTextSplitter(
separators=["\n\n", "\n", "."], chunk_size=900, chunk_overlap=200, length_function=len
)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks):
embeddings = HuggingFaceBgeEmbeddings(model_name="BAAI/bge-base-en-v1.5")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
llm = HuggingFaceHub(
repo_id="google/flan-t5-large",
model_kwargs={"temperature": 0.5, "max_length": 1024},
)
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 handle_userinput(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
)
if __name__ == "__main__":
main()
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