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##############################################################
# PDF Chat
#
# Mike Pastor February 2024
import streamlit as st
from dotenv import load_dotenv
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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