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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()
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