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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() |