File size: 2,709 Bytes
62369fb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | import streamlit as st
from database import main as populate_db
from query import query_rag
from langchain.schema.document import Document
# Set the page configuration for Streamlit
st.set_page_config(page_title='Content Engine')
# Function to load and populate the database
def load_and_populate_database():
st.header("Database Population")
st.write("Use this section to populate the database.")
if st.button("Populate Database"):
populate_db()
st.markdown("<hr style='border: 1px dashed #e0e0e0;'>", unsafe_allow_html=True)
# Function to display the scrolling chat box
def display_chat_box():
st.header("Chat Box")
st.write("Enter your questions below and get responses generated from the context.")
# Setup for holding old messages
if 'messages' not in st.session_state:
st.session_state.messages = []
# Display all the history in a scrollable chat box
with st.container():
for message in st.session_state.messages:
st.chat_message(message['role']).markdown(message['content'])
# Input field for user query
user_query = st.chat_input("Your Query:", key="query_input")
if user_query:
# Display user prompt
st.chat_message('user').markdown(user_query)
# Store the user prompt in state
st.session_state.messages.append({'role': 'user', 'content': user_query})
# Buffering message while waiting for response
with st.spinner('Waiting for response...'):
# Query processing
response_text = query_rag(user_query)
# Show the LLM response
st.chat_message('assistant').markdown(response_text)
# Store the LLM response
st.session_state.messages.append({'role': 'assistant', 'content': response_text})
# Main function to run the Streamlit app
def main():
# Title and description
st.markdown("<h1 style='font-size: 60px; text-align: center;'>🧠Content Engine🤖</h1>", unsafe_allow_html=True)
# st.markdown("<hr style='border: 1px dashed #e0e0e0; '>", unsafe_allow_html=True)
# st.write("This system analyzes and compares multiple PDF documents, specifically identifying and highlighting their differences. The system will utilize Retrieval Augmented Generation (RAG) techniques to effectively retrieve, assess, and generate insights from the documents.")
st.markdown("<hr style='border: 1px dashed #e0e0e0;'>", unsafe_allow_html=True)
# To load and populate the database
# load_and_populate_database()
# Display chat box
display_chat_box()
if __name__ == "__main__":
main()
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