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()