app.py

# app cơ bản để demo RAG chatbot, sử dụng streamlit để đơn giản hoá phần frontend/U
import sys
import os
import streamlit as st
from time import time
import logging

os.environ['ROOT_PATH'] = os.path.dirname(os.path.abspath(os.path.dirname(__file__)))
from api.engine import ChatEngine

logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

st.title("Smart Chabot for Organic Crop powered by Eco Footprint")

@st.cache_resource
def initialize():
    return ChatEngine(vector_index="chroma", force_new_db=False)

engine = initialize()


if "messages" not in st.session_state:
    st.session_state.messages = []

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

if user_input := st.chat_input("Bạn muốn hỏi điều gì?"):
    st.session_state.messages.append({"role": "user", "content": user_input})
    with st.chat_message("user"):
        st.markdown(user_input)

    with st.chat_message("assistant"):
        message_placeholder = st.empty()

        response_content = ""
        with st.spinner("Thinking..."):
            start = time()
            streaming_response = engine.query_streaming(user_input)
            # Stream kết quả và cập nhật lên giao diện
            query_end = time()
            print(f"Query time calculated: {round(query_end-start,4)}")
            for chunk in streaming_response.response_gen:
                response_content += chunk
                message_placeholder.markdown(f"{response_content} ▌")
            end = time()
            print(f"Response time calculated: {round(end-start,4)}")
    message_placeholder.markdown(response_content)
    st.session_state.messages.append({"role": "assistant", "content": response_content})