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# Load libraries and dependencies
from dotenv import load_dotenv
from logic import get_pdf_text, get_text_chunks, init_embeddings, get_conversation_chain
import streamlit as st
# Initialization function
def init():
# Load environmental variables to extract API secrets
load_dotenv()
# Create and configure streamlit page
st.set_page_config(page_title="Work-Assistant App", page_icon="🐮")
st.header("Work-Assistant App 🐮")
# Initialize session state
if "conversation_chain" not in st.session_state:
st.session_state.conversation_chain = None
if "chat_history" not in st.session_state:
st.session_state.chat_history = None
# Main function
def main():
init()
# UI main layout
user_input = st.text_input("Ask a question:")
if user_input:
# Obtain response from conversation chain
response = st.session_state.conversation_chain({'question': user_input})
# Update chat history via response history
st.session_state.chat_history = response['chat_history']
# Show conversation
for i, message in enumerate(st.session_state.chat_history):
if i % 2 == 0:
st.write("User:" + message.content)
else:
st.write("Bot:" + message.content)
# UI sidebar layout
with st.sidebar:
st.subheader("Your documents")
pdf_docs = st.file_uploader("Upload your PDFs", accept_multiple_files=True)
if st.button("Process files"):
with st.spinner("Processing..."):
# Extract text content from pdf
text = get_pdf_text(pdf_docs)
# Split text into chunks
chunks = get_text_chunks(text)
# Obtain embeddings
embeddings = init_embeddings()
# Start conversation chain
st.session_state.conversation_chain = get_conversation_chain(chunks=chunks, embeddings=embeddings)
# Main program of the application
if __name__ == '__main__':
main() |