Spaces:
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| ########################################################################## | |
| # app.py - Pennwick PDF Chat | |
| # | |
| # HuggingFace Spaces application to anlayze uploaded PDF files | |
| # with open-source models ( hkunlp/instructor-xl ) | |
| # | |
| # Mike Pastor February 17, 2024 | |
| import streamlit as st | |
| from streamlit.components.v1 import html | |
| from dotenv import load_dotenv | |
| from PyPDF2 import PdfReader | |
| from PIL import Image | |
| # Local file | |
| from htmlTemplates import css, bot_template, user_template | |
| # from langchain.embeddings import HuggingFaceInstructEmbeddings | |
| from langchain_community.embeddings import HuggingFaceInstructEmbeddings | |
| # from langchain.vectorstores import FAISS | |
| from langchain_community.vectorstores import FAISS | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| # from langchain.llms import HuggingFaceHub | |
| from langchain_community.llms import HuggingFaceHub | |
| ################################################################################## | |
| # Admin flags | |
| DISPLAY_DIALOG_LINES=6 | |
| SESSION_STARTED = False | |
| ################################################################################## | |
| def extract_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 | |
| ################################################################################## | |
| # Chunk size and overlap must not exceed the models capacity! | |
| # | |
| def extract_bitesize_pieces(text): | |
| text_splitter = CharacterTextSplitter( | |
| separator="\n", | |
| chunk_size=800, # 1000 | |
| chunk_overlap=200, | |
| length_function=len | |
| ) | |
| chunks = text_splitter.split_text(text) | |
| return chunks | |
| ################################################################################## | |
| def prepare_embedding_vectors(text_chunks): | |
| st.write('Here in vector store....', unsafe_allow_html=True) | |
| # embeddings = OpenAIEmbeddings() | |
| # pip install InstructorEmbedding | |
| # pip install sentence-transformers==2.2.2 | |
| embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") | |
| st.write('Here in vector store - got embeddings ', unsafe_allow_html=True) | |
| # from InstructorEmbedding import INSTRUCTOR | |
| # model = INSTRUCTOR('hkunlp/instructor-xl') | |
| # sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" | |
| # instruction = "Represent the Science title:" | |
| # embeddings = model.encode([[instruction, sentence]]) | |
| # embeddings = model.encode(text_chunks) | |
| print('have Embeddings: ') | |
| # text_chunks="this is a test" | |
| # FAISS, Chroma and other vector databases | |
| # | |
| vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
| st.write('FAISS succeeds: ') | |
| return vectorstore | |
| ################################################################################## | |
| def prepare_conversation(vectorstore): | |
| # llm = ChatOpenAI() | |
| # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512}) | |
| # google/bigbird-roberta-base facebook/bart-large | |
| llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.7, "max_length": 512}) | |
| 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 process_user_question(user_question): | |
| print('process_user_question called: \n') | |
| # if not SESSION_STARTED: | |
| # print('No Session') | |
| # st.write( 'Please upload and analyze your PDF files first!') | |
| # return | |
| if user_question == None : | |
| print('question is null') | |
| return | |
| if user_question == '' : | |
| print('question is blank') | |
| return | |
| if st == None : | |
| print('session is null') | |
| return | |
| if st.session_state == None : | |
| print('session STATE is null') | |
| return | |
| print('question is: ', user_question) | |
| print('\nsession is: ', st ) | |
| # try: | |
| # response = st.session_state.conversation({'question': user_question}) | |
| # # response = st.session_state.conversation({'summarization': user_question}) | |
| # st.session_state.chat_history = response['chat_history'] | |
| # Exception: | |
| # st.write( 'Please upload and analyze your PDF files first!') | |
| # return | |
| # st.empty() | |
| # try: | |
| # st.session_state.conversation({'question': "Summarize the document"}) | |
| # # if "key" not in st.session_state: | |
| # # st.write('Good') | |
| # except: | |
| # st.error("Please upload and analyze your PDF files first!") | |
| # return | |
| if st.session_state.conversation == None: | |
| st.error("Please upload and analyze your PDF files first!") | |
| return | |
| response = st.session_state.conversation({'question': user_question}) | |
| st.session_state.chat_history = response['chat_history'] | |
| results_size = len(response['chat_history']) | |
| results_string = "" | |
| print('results_size is: ', results_size ) | |
| for i, message in enumerate(st.session_state.chat_history): | |
| # Scrolling does not display the last printed line, | |
| # so only print the last 6 lines | |
| # | |
| print('results_size on msg: ', results_size, i, ( results_size - DISPLAY_DIALOG_LINES ) ) | |
| if results_size > DISPLAY_DIALOG_LINES: | |
| if i < ( results_size - DISPLAY_DIALOG_LINES ): | |
| continue | |
| if i % 2 == 0: | |
| # st.write(user_template.replace( | |
| # "{{MSG}}", message.content), unsafe_allow_html=True) | |
| results_string += ( "<p>" + message.content + "</p>" ) | |
| else: | |
| # st.write(bot_template.replace( | |
| # "{{MSG}}", message.content), unsafe_allow_html=True) | |
| results_string += ( "<p>" + "-- " + message.content + "</p>" ) | |
| html(results_string, height=300, scrolling=True) | |
| ################################################################################### | |
| def main(): | |
| print( 'Pennwick Starting up...\n') | |
| # Load the environment variables - if any | |
| load_dotenv() | |
| ################################################################################## | |
| # st.set_page_config(page_title="Pennwick PDF Analyzer", page_icon=":books:") | |
| # im = Image.open("robot_icon.ico") | |
| # st.set_page_config(page_title="Pennwick PDF Analyzer", page_icon=im ) | |
| # st.set_page_config(page_title="Pennwick PDF Analyzer") | |
| # import base64 | |
| # from PIL import Image | |
| # # Open your image | |
| # image = Image.open("robot_icon.ico") | |
| # # Convert image to base64 string | |
| # with open("robot_icon.ico", "rb") as f: | |
| # encoded_string = base64.b64encode(f.read()).decode() | |
| # # Set page config with base64 string | |
| # st.set_page_config(page_title="Pennwick File Analyzer 2", page_icon=f"data:image/ico;base64,{encoded_string}") | |
| st.set_page_config(page_title="Pennwick File Analyzer", page_icon="./robot_icon.ico") | |
| print( 'prepared page...\n') | |
| ################### | |
| 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("Pennwick File Analyzer :shark:") | |
| # st.header("Pennwick File Analyzer 2") | |
| st.image("robot_icon.png", width=96 ) | |
| st.header(f"Pennwick File Analyzer") | |
| user_question = None | |
| user_question = st.text_input("Ask the Open Source - Flan-T5 Model a question about your uploaded documents:") | |
| if user_question != None: | |
| print( 'calling process question', user_question) | |
| process_user_question(user_question) | |
| # st.write( user_template, unsafe_allow_html=True) | |
| # st.write(user_template.replace( "{{MSG}}", "Hello robot!"), unsafe_allow_html=True) | |
| # st.write(bot_template.replace( "{{MSG}}", "Hello human!"), unsafe_allow_html=True) | |
| with st.sidebar: | |
| st.subheader("Which documents would you like to analyze?") | |
| st.subheader("(no data is saved beyond the session)") | |
| pdf_docs = st.file_uploader( | |
| "Upload your PDF documents here and click on 'Analyze'", accept_multiple_files=True) | |
| # Upon button press | |
| if st.button("Analyze these files"): | |
| with st.spinner("Processing..."): | |
| ################################################################# | |
| # Track the overall time for file processing into Vectors | |
| # # | |
| from datetime import datetime | |
| global_now = datetime.now() | |
| global_current_time = global_now.strftime("%H:%M:%S") | |
| st.write("Vectorizing Files - Current Time =", global_current_time) | |
| # get pdf text | |
| raw_text = extract_pdf_text(pdf_docs) | |
| # st.write(raw_text) | |
| # # get the text chunks | |
| text_chunks = extract_bitesize_pieces(raw_text) | |
| # st.write(text_chunks) | |
| # # create vector store | |
| vectorstore = prepare_embedding_vectors(text_chunks) | |
| # # create conversation chain | |
| st.session_state.conversation = prepare_conversation(vectorstore) | |
| SESSION_STARTED = True | |
| # Mission Complete! | |
| global_later = datetime.now() | |
| st.write("Files Vectorized - Total EXECUTION Time =", | |
| (global_later - global_now), global_later) | |
| if __name__ == '__main__': | |
| main() | |