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| import streamlit as st | |
| import streamlit as st | |
| from dotenv import load_dotenv | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.vectorstores import FAISS | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| # from langchain.llms import HuggingFaceHub | |
| from streamlit_chat import message | |
| def get_pdf_text(pdfs): | |
| text="" | |
| for pdf in pdfs: | |
| pdf_reader = PdfReader(pdf) | |
| for page in pdf_reader.pages: | |
| text+= page.extract_text() | |
| return text | |
| def get_text_chunks(text): | |
| text_splitter = CharacterTextSplitter(separator="\n", | |
| chunk_size=1000, chunk_overlap = 200, length_function=len) | |
| chunks = text_splitter.split_text(text) | |
| return chunks | |
| def get_vectorstore(text_chunks): | |
| embeddings = OpenAIEmbeddings() | |
| # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") | |
| vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
| return vectorstore | |
| def get_conversation_chain(vectorstore): | |
| # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl") | |
| llm = ChatOpenAI(max_tokens=2000) | |
| 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 user_input(user_question): | |
| response = st.session_state.conversation({'question':user_question}) | |
| st.session_state.chat_history = response['chat_history'] | |
| for i, messages in enumerate(st.session_state.chat_history): | |
| if i % 2 == 0: | |
| message(messages.content, is_user=True) | |
| else: | |
| message(messages.content) | |
| def main(): | |
| load_dotenv() | |
| st.set_page_config(page_title="Chat with PDF") | |
| 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 PDF") | |
| user_question = st.text_input("Ask a question about your documents...") | |
| if user_question: | |
| user_input(user_question) | |
| with st.sidebar: | |
| st.subheader("Your Documents") | |
| pdfs = st.file_uploader("Upload here", accept_multiple_files=True) | |
| if st.button("Process"): | |
| with st.spinner("Processing"): | |
| raw_text = get_pdf_text(pdfs) | |
| # print(raw_text) | |
| chunks = get_text_chunks(raw_text) | |
| vectorstore = get_vectorstore(chunks) | |
| st.session_state.conversation = get_conversation_chain(vectorstore) | |
| st.success("Processing Complete !") | |
| if __name__ == '__main__': | |
| main() |