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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
api_key = st.secrets['api_key']
def main():
load_dotenv()
st.set_page_config(page_title="PDF Chatbot", page_icon="📚")
st.image("https://huggingface.co/spaces/wiwaaw/summary/resolve/main/banner.png")
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.title("Chat with Multiple PDFs using FLAN-T5")
user_question = st.text_input("Ask a question about your documents:")
if user_question:
handle_userinput(user_question)
with st.sidebar:
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)
st.success("file uploaded")
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},
huggingfacehub_api_token=api_key
)
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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