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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-xxl", | |
| 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() | |