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##############################################################
# PDF Chat
#
# Mike Pastor February 2024
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from InstructorEmbedding import INSTRUCTOR
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
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
from langchain.llms import HuggingFaceHub
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
# Chunk size and overlap must not exceed the models capacity!
#
def get_text_chunks(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 get_vectorstore(text_chunks):
# embeddings = OpenAIEmbeddings()
# pip install InstructorEmbedding
# pip install sentence-transformers==2.2.2
embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
# 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)
print('FAISS succeeds: ')
return vectorstore
def get_conversation_chain(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": 0.5, "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 handle_userinput(user_question):
response = st.session_state.conversation({'question': user_question})
# response = st.session_state.conversation({'summarization': user_question})
st.session_state.chat_history = response['chat_history']
# st.empty()
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)
def main():
load_dotenv()
st.set_page_config(page_title="MLP Chat with multiple PDFs",
page_icon=":books:")
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("Mike's PDF Chat :books:")
user_question = st.text_input("Ask a question about your documents:")
if user_question:
handle_userinput(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("Your documents")
pdf_docs = st.file_uploader(
"Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
# Upon button press
if st.button("Process 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 = get_pdf_text(pdf_docs)
# st.write(raw_text)
# # get the text chunks
text_chunks = get_text_chunks(raw_text)
# st.write(text_chunks)
# # create vector store
vectorstore = get_vectorstore(text_chunks)
# # create conversation chain
st.session_state.conversation = get_conversation_chain(vectorstore)
# 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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