Spaces:
Runtime error
Runtime error
| import os | |
| import streamlit as st | |
| from dotenv import load_dotenv | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.embeddings import OpenAIEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| from htmlTemplates import css, bot_template, user_template | |
| def extract_text_from_pdfs(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 split_text_into_chunks(text): | |
| text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len) | |
| return text_splitter.split_text(text) | |
| def create_vector_store_from_text_chunks(text_chunks): | |
| key = os.getenv('OPENAI_KEY') | |
| embeddings = OpenAIEmbeddings(openai_api_key=key) | |
| return FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
| def create_conversation_chain(vectorstore): | |
| llm = ChatOpenAI() | |
| memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) | |
| return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory) | |
| def process_user_input(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): | |
| template = user_template if i % 2 == 0 else bot_template | |
| st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True) | |
| def main(): | |
| load_dotenv() | |
| st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:") | |
| st.write(css, unsafe_allow_html=True) | |
| st.header("Chat with multiple PDFs :books:") | |
| user_question = st.text_input("Ask a question about your documents:") | |
| if user_question: | |
| process_user_input(user_question) | |
| with st.sidebar: | |
| st.subheader("Your documents") | |
| 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"): | |
| raw_text = extract_text_from_pdfs(pdf_docs) | |
| text_chunks = split_text_into_chunks(raw_text) | |
| vectorstore = create_vector_store_from_text_chunks(text_chunks) | |
| st.session_state.conversation = create_conversation_chain(vectorstore) | |
| if __name__ == '__main__': | |
| main() | |